Question: Web-STAR: A visual, web-based integrated development environment for a story comprehension system.

Innovative Game Design: Dual Purpose Entertainment and Computational Problem Solving
The contemporary approach to game design not only focuses on providing entertainment but also harnesses the vast amount of data generated during gameplay for dual purposes. This data, collected as a byproduct of engaging in games, is leveraged not just for improving gaming experiences but also for solving complex computational problems and training advanced AI algorithms. This dual-purpose design not only enriches the gaming industry but also contributes significantly to the fields of artificial intelligence and computational science.

**Question**: A Novel Algorithm for the Traveling Salesman Problem Using Ant Colony Optimization

**Documents**: An Ant Colony Optimization Approach to the Vehicle Routing Problem
In this paper, an Ant Colony Optimization (ACO) algorithm is applied to the Vehicle Routing Problem (VRP) for the first time. The ACO algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. It was inspired by the behavior of real ants, which are able to find the shortest path between their nest and a food source. The ACO algorithm has been successfully applied to a wide range of optimization problems, including the Traveling Salesman Problem (TSP), the Quadratic Assignment Problem (QAP), and the Set Covering Problem (SCP). The VRP is a well-known NP-hard problem in combinatorial optimization, which consists of determining the optimal set of routes for a fleet of vehicles to service a set of customers, where each customer must be serviced exactly once. The objective is to minimize the total distance traveled by the vehicles while satisfying certain constraints, such as the capacity of the vehicles and the time windows for servicing the customers.

**Similar Documents**: Ant Colony Optimization for the Vehicle Routing Problem: A Novel Approach
This paper presents a pioneering application of the Ant Colony Optimization (ACO) technique to the Vehicle Routing Problem (VRP), marking the first time this method has been employed for this specific challenge. Inspired by the natural foraging behavior of ants, ACO is a probabilistic strategy adept at solving computational problems by identifying optimal paths within graphs. Its effectiveness has been demonstrated across various optimization tasks, notably the Traveling Salesman Problem (TSP), the Quadratic Assignment Problem (QAP), and the Set Covering Problem (SCP). The Vehicle Routing Problem, a recognized NP-hard issue in the realm of combinatorial optimization, entails identifying the most efficient route set for a vehicle fleet to serve a given customer base, with each customer requiring a single service instance. The goal is to

This paper addresses the role of marketing in hypermedia computer-mediated environments (CMEs). Our approach considers hypermedia CMEs to be large-scale (i.e. national or global) networked environments, of which the World Wide Web on the Internet is the first and current global implementation. We introduce marketers to this revolutionary new medium, and propose two structural models of consumer behavior in a CME. Then we examine the set of consequent testable research propositions and marketing implications that flow from the models. Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations 1) Introduction Firms communicate with their customers through various media. Traditionally, these media follow a passive one-to-many communication model whereby a firm reaches many current and potential customers, segmented or not, through marketing efforts that allow only limited forms of feedback on the part of the customer. For several years now, a revolution has been developing that is dramatically altering this traditional view of advertising and communication media. This revolution is the Internet, the massive global network of interconnected packet-switched computer networks, and as a new marketing medium, has the potential to radically change the way firms do business with their customers. The Internet operationalizes a model of distributed computing that facilitates interactive multimedia many-to-many communication. As such, the Internet supports discussion groups (e.g. USENET news and moderated and unmoderated mailing lists), multi-player games and communications systems (e.g. MUDs, irc, chat, MUSEs), file transfer, electronic mail, and global information access and retrieval systems (e.g. archie, gopher, and the World Wide Web). The business implications of this model "[where] the engine of democratization sitting on so many desktops is already out of control, is already creating new players in a new game" (Carroll 1994), will be played out in as yet unknown ways for years to come. This paper is concerned with the marketing implications of commercializing hypermedia computer-mediated environments (CMEs), of which the World Wide Web (Berners-Lee et. al. 1992, 1993) on the Internet is the first and current networked global implementation. While we provide a formal definition subsequently, at this point we informally define a hypermedia CME as a distributed computer network used to access and provide hypermedia content (i.e., multimedia content connected across the network with hypertext links). Though other CMEs are relevant to marketers, including private bulletin board systems (Bunch 1994); public conferencing systems such as the WELL (Figallo 1993; Rheingold 1992, 1993) and ECHO; and commercial online services such as America On-Line, Prodigy, and CompuServe, we restrict our current focus to marketing activities in hypermedia CMEs accessible via the "Web" on the Internet. The Internet is an important focus for marketers because consumers and firms are conducting business on the Internet in proportions that dwarf the commercial provider base of the other CMEs combined. There are over 21,700 commercial Internet addressess (Verity and Hof 1994), and an increasing percentage of these commercial addresses are providing Web services. As of December 28, 1994, 1465 firms were listed in Open Market’s (1994) directory of "Commercial Services on the Net," and there were 6370 entries in the "Business/Corporations" directory of the Yahoo Guide to WWW (Filo and Yang 1994). The central thesis driving this research is that hypermedia CMEs, such as but not limited to the World Wide Web on the Internet, require the development and application of new marketing concepts and models. This is because hypermedia CMEs possess unique characteristics, including machine-interactivity, telepresence, hypermedia, and network navigation, which distinguish them from traditional media and some interactive multimedia, on which conventional concepts and models are based. Hoffman & Novak (1995), "Marketing in Hypermedia CMEs: Conceptual Foundations" page 1

A revolution in computer interface design is changing the way we think about computers. Rather than typing on a keyboard and watching a television monitor, Augmented Reality lets people use familiar, everyday objects in ordinary ways. The difference is that these objects also provide a link into a computer network. Doctors can examine patients while viewing superimposed medical images; children can program their own LEGO constructions; construction engineers can use ordinary paper engineering drawings to communicate with distant colleagues. Rather than immersing people in an artificially-created virtual world, the goal is to augment objects in the physical world by enhancing them with a wealth of digital information and communication capabilities.

The Semantic Web relies heavily on the formal ontologies that structure underlying data for the purpose of comprehensive and transportable machine understanding. Therefore, the success of the Semantic Web depends strongly on the proliferation of ontologies, which requires fast and easy engineering of ontologies and avoidance of a knowledge acquisition bottleneck. Ontology Learning greatly facilitates the construction of ontologies by the ontology engineer. The vision of ontology learning that we propose here includes a number of complementary disciplines that feed on different types of unstructured, semi-structured and fully structured data in order to support a semi-automatic, cooperative ontology engineering process. Our ontology learning framework proceeds through ontology import, extraction, pruning, refinement, and evaluation giving the ontology engineer a wealth of coordinated tools for ontology modeling. Besides of the general framework and architecture, we show in this paper some exemplary techniques in the ontology learning cycle that we have implemented in our ontology learning environment, Text-ToOnto, such as ontology learning from free text, from dictionaries, or from legacy ontologies, and refer to some others that need to complement the complete architecture, such as reverse engineering of ontologies from database schemata or learning from XML documents. Ontologies for the Semantic Web Conceptual structures that define an underlying ontology are germane to the idea of machine processable data on the Semantic Web. Ontologies are (meta)data schemas, providing a controlled vocabulary of concepts, each with an explicitly defined and machine processable semantics. By defining shared and common domain theories, ontologies help both people and machines to communicate concisely,

This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible.

In this paper we present an application framework that leverages geospatial content on the World Wide Web by enabling innovative modes of interaction and novel types of user interfaces on advanced mobile phones and PDAs. We discuss the current development steps involved in building mobile geospatial Web applications and derive three technological pre-requisites for our framework: spatial query operations based on visibility and field of view, a 2.5D environment model, and a presentationindependent data exchange format for geospatial query results. We propose the Local Visibility Model as a suitable XML-based candidate and present a prototype implementation.

Programming foundations is not an easy subject to be taught — many students have difficulties understanding the abstract concepts of programming and have a wrong view about the programming activity. In order to address these problems, experiences have suggested the integrated teaching of programming concepts and software testing in introductory CS courses. Shortly, the idea is that testing can contribute to enhance the students' capabilities of understanding and analysis. However, such perspective requires tools to provide an adequate feedback to evaluate the students' performance concerning programming and testing activities. In this paper we describe ProgTest — a web-based tool for the submission and automatic evaluation of practical programming assignments based on testing activities. Results from a preliminary validation of ProgTest are also presented. Such results provide evidences on the practical use of ProgTest as a supporting mechanism for the integrated teaching of programming foundations and software testing.

This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.

The Software Concordance is a hypermedia software development environment exploring how document technology and versioned hypermedia can improve software document management. The Software Concordance’s central tool is a document editor that integrates program analysis and hypermedia services for both source code and multimedia documentation in XML. The editor allows developers to embed inline multimedia documentation, including images and audio clips, into their program sources and to bind them to any program fragment. Web style hyperlinks are also supported. The developers are able to move seamlessly between source code and the documentation that describes its motivation, design, correctness and use without disrupting the integrated program analysis services, which include lexing, parsing and type checking. This paper motivates the need for environments like the Software Concordance, describes the key features and architecture of its document editor and discusses future work to improve its functionality.

The confluence of decades of computer science and computer engineering research in multimodal interaction (e.g., speech and gesture recognition), machine learning (e.g., classification and feature extraction), software (e.g., web browsers, distributed agents), electronics (e.g., energy-efficient microprocessors, head-mounted displays), design methodology in user-centered design, and rapid prototyping have enabled a new class of computers—wearable computers. The lecture takes the viewpoint of a potential designer or researcher in wearable computing. Designing wearable computers requires attention to many different factors because of the computer’s closeness to the body and its use while performing other tasks. For the purposes of discussion, we have created the UCAMP framework, which consists of the following factors: user, corporal, attention, manipulation, and perception. Each of these factors and their importance is described. A number of example prototypes developed by the authors, as well as by other researchers, are used to illustrate these concepts. Wearable computers have established their first foothold in several application domains, such as vehicle and aircraft maintenance and manufacturing, inspection, language translation, and other areas. The lecture continues by describing the next step in the evolution of wearable computers, namely, context awareness. Context-aware computing takes into account a user’s state and surroundings, and the mobile computer modifies its behavior based on this information. A user’s context can be quite rich, consisting of attributes such as physical location, physiological state, personal history, daily behavioral patterns, and so forth. If a human assistant were given such context, he or she would make decisions in a proactive fashion, anticipating user needs, and acting as a proactive assistant. The goal is to enable mobile computers to play an analogous role, exploiting context information to significantly reduce demands on human attention. Context-aware intelligent agents can deliver relevant information when a user needs that information. These data make possible many exciting new applications, such as augmented reality, context-aware collaboration, and augmented manufacturing. The combined studies and research reported in this lecture suggest a number of useful guidelines for designing wearable computing devices. Also included with the guidelines is a list of questions that designers should consider when beginning to design a wearable computer. The research directions section emphasizes remaining challenges and trends in the areas of user interface, modalities of interaction, and wearable cognitive augmentation. Finally, we summarize the most important challenges and conclude with a projection of future directions in wearable computing. KEyWoRDS wearable computers, wearability design, user interface, manipulation devices, interaction modalities, context-aware computing, cognitive augmentation vi

Business specifications – that formerly only supported IT development – increasingly become business configurations in the form of rules that can be loaded directly into IT solutions. PROLOG is well–known for its qualities in the development of sophisticated rule systems. It is desirable to combine the advantages of PROLOG with JAVA, since JAVA has become one of the most used programming languages in industry. However, experts of both programming languages are rare. To overcome the resulting interoperability problems, we have developed a framework which generates a JAVA archive that provides methods to query a given set of PROLOG rules; it ensures that valid knowledge bases are transmitted between JAVA and PROLOG. We use XML Schema for describing the format for exchanging a knowledge base between PROLOG and JAVA. From the XML Schema desciption, we scaffold JAVA classes; the JAVA programmer can use them and fill in the open slots by statements accessing other JAVA data structures. The data structure on the JAVA side reflects the complex structured knowledge base, with which the PROLOG rules work, in an object–oriented way. We can to some extend verify the correctness of the data set / knowledge base sent from JAVA to PROLOG using standard methods for XML Schema. Moreover, we can add constraints that go beyond XML. For instance, we can specify standard integrity constraints known from relational databases, such as primary key, foreign key, and not–null constraints. Since we are dealing with complex structured XML data, however, there can be far more general integrity constraints. These can be expressed by standard PROLOG rules, which can be evaluated on the PROLOG side; they could also be compiled to JAVA by available PROLOG to JAVA converters such as Prolog Cafe – since they will usually be written in a supported subset of PROLOG. We have used our framework for integrating PROLOG business rules into a commercial E–Commerce system written in JAVA.


Question: Web-STAR: A visual, web-based integrated development environment for a narrative understanding system.

This paper introduces Web-STAR: Story, a web-based Integrated Development Environment (IDE) designed specifically for a story comprehension system. Similar to how a web-based IDE was developed for IDP, a knowledge base system rooted in first order logic, Web-STAR: Story aims to make the story comprehension system more accessible and user-friendly for newcomers. The goal is to provide a compelling online platform that encourages potential users to explore and ultimately adopt the system, while also offering offline capabilities for more extensive application development. By leveraging web technology, Web-STAR: Story can deliver a contemporary IDE experience with minimal development effort, ensuring that users can easily interact with the story comprehension system, whether they are trying it out for the first time or working on substantial projects.

This paper explores the current state of the task of generating easily understandable information from data for people using natural language, which is currently addressed by two independent research fields: the natural language generation field — and, more specifically, the data-to-text sub-field — and the linguistic descriptions of data field. Both approaches are explained in a detailed description which includes: i) a methodological revision of both fields including basic concepts and definitions, models and evaluation procedures; ii) the most relevant systems, use cases and real applications described in the literature. Some reflections about the current state and future trends of each field are also provided, followed by several remarks that conclude by hinting at some potential points of mutual interest and convergence between both fields. © 2015 Elsevier B.V. All rights reserved.

This paper describes a model for evaluating a computer-generated plot. The main motivation of this project is to provide MEXICA, our plot generator, with the capacity of evaluating its own outputs as well as assessing narratives generated by other agents that can be employed to enrich its knowledge base. We present a description of our computer model as well as an explanation of our first prototype. Then, we show the results of assessing three computergenerated narratives. The outcome suggests that we are in the right direction, although much more work

We outline the design of a visualizer, named Vishit, for texts in the Hindi language. The Hindi language is lingua franca in many states of India where people speak different languages. The visualized text serves as a universal language where seamless communication is needed by many people who speak different languages and have different cultures. Vishit consists of the following three major processing steps: language processing, knowledge base creation and scene generation. Initial results from the Vishit prototype are encouraging.

The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.

This paper is intended to serve as a comprehensive introduction to the emerging eld concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to e ective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, rst discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the speci cation, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging eld, considering various case studies, software tools for ontology development, key research issues and future prospects. AIAI-TR-191 Ontologies Page i

Despite the wealth of information contained in the Web of Linked Data, the current limitations and entry barriers of the Semantic Web technologies hinder the users from taking advantage of these information resources. Linked Data visualization can alleviate this problem. In this paper, we adopt a proper Linked Data visualization model, design Linked Data visualization algorithms, and develop a lightweight, easy-to-use prototype tool, LOD Viewer, using the platform independent JavaScript language. LOD Viewer can visualize different sources of RDF data including SPARQL endpoints for Linked Open Data (LOD) sources, and display the data in different graphic illustrations. Our case studies have verified the effectiveness and realizability of the proposed method. The time complexity analysis and experimental test show that the run-time of the proposed algorithms approximately exhibits a linear growth rate as the visualized RDF triples size increases.

In this paper, we investigate an approach for reconstructing storyline graphs from large-scale collections of Internet images, and optionally other side information such as friendship graphs. The storyline graphs can be an effective summary that visualizes various branching narrative structure of events or activities recurring across the input photo sets of a topic class. In order to explore further the usefulness of the storyline graphs, we leverage them to perform the image sequential prediction tasks, from which photo recommendation applications can benefit. We formulate the storyline reconstruction problem as an inference of sparse time-varying directed graphs, and develop an optimization algorithm that successfully addresses a number of key challenges of Web-scale problems, including global optimality, linear complexity, and easy parallelization. With experiments on more than 3.3 millions of images of 24 classes and user studies via Amazon Mechanical Turk, we show that the proposed algorithm improves other candidate methods for both storyline reconstruction and image prediction tasks.

A wealth of knowledge is encoded in the form of tables on the World Wide Web. We propose a classification algorithm and a rich feature set for automatically recognizing layout tables and attribute/value tables. We report the frequencies of these table types over a large analysis of the Web and propose open challenges for extracting from attribute/value tables semantic triples (knowledge). We then describe a solution to a key problem in extracting semantic triples: protagonist detection, i.e., finding the subject of the table that often is not present in the table itself. In 79% of our Web tables, our method finds the correct protagonist in its top three returned candidates.

Reiter and Dale’s book is about natural language generation (NLG), and describes methods for building systems of this kind. It offers a comprehensive overview of the field of NLG, from a perspective that has rarely been described elsewhere. It is very valuable for people with little or even no knowledge about NLG particulars who intend to investigate applications where NLG technology might play a certain role, as well as for people who want to obtain an application-oriented overview of the field. In this sense, all communities intended to be served by the book—students, academics in related areas, and software developers—are well addressed. In addition, some sections provide material that is probably new even to researchers with considerable experience in the field, such as the discussions on corpus analysis and domain modeling. In the introduction, the field of NLG is briefly characterized from researchand application-oriented perspectives and is illustrated by screen shots produced by several systems. Then conditions for effective uses of this technology are elaborated and contrasted with conditions where other techniques are more appropriate, and methods for determining the intended functionality of a system to be built are discussed. The main sections of the book are devoted to the prototypical architecture of applicationoriented NLG systems and their major processing phases: document planning, microplanning, and surface realization. Each of these three phases is illustrated by a number of detailed examples, demonstrating the successive refinements of utterance specifications in the course of processing. In the final section, the embedding of natural language processing technology is discussed, featuring typography, combined uses with graphics and hypertext, and integration with speech. The methods are illustrated by a large number of examples—the book contains more than 120 figures on its 248 pages. At the end of each section, a number of useful references for further reading are related to the section topics. In the appendix, a table summarizing the 35 systems referred to in the book is given. In general, the presentations in the book are beneficial for their high degree of detail in documenting representations. Running examples are illustrated by intermediate representations at various stages of processing. They demonstrate very well the increasing degrees of precision in which utterances are specified and the associated commitments about possible text portions that express these specifications. Particularly valuable are the comparisons of alternative representations, ranging from skeletal propositions over lexicalized case frames to canned text, including a comparison of the consequences for the distribution of work over the system components involved and discussions of the associated pros and cons. These chapters provide a number of valuable hints, given the currently low degree of standardization in the field. Further important aspects addressed, which have been widely ignored in the literature so far,

We have long envisioned that one day computers will understand natural language and anticipate what we need, when and where we need it, and proactively complete tasks on our behalf. As computers get smaller and more pervasive, how humans interact with them is becoming a crucial issue. Despite numerous attempts over the past 30 years to make language understanding (LU) an effective and robust natural user interface for computer interaction, success has been limited and scoped to applications that were not particularly central to everyday use. However, speech recognition and machine learning have continued to be refined, and structured data served by applications and content providers has emerged. These advances, along with increased computational power, have broadened the application of natural LU to a wide spectrum of everyday tasks that are central to a user's productivity. We believe that as computers become smaller and more ubiquitous [e.g., wearables and Internet of Things (IoT)], and the number of applications increases, both system-initiated and user-initiated task completion across various applications and web services will become indispensable for personal life management and work productivity. In this article, we give an overview of personal digital assistants (PDAs); describe the system architecture, key components, and technology behind them; and discuss their future potential to fully redefine human?computer interaction.


Question: Web-STAR: A visual, web-based integrated development environment for a narrative understanding system.

This paper presents the Stanford CoreNLP Natural Language Processing Toolkit, a versatile and widely adopted tool for conducting core linguistic analysis. The popularity of this toolkit extends beyond academic research circles to encompass commercial and governmental entities seeking open-source NLP solutions. Its widespread use can be attributed to several factors: a user-friendly design that ensures ease of use, clear and accessible interfaces, the integration of reliable and high-quality analysis modules, and the absence of a cumbersome setup process. The toolkit's modular architecture facilitates extensibility, making it a preferred choice for various NLP tasks. By combining simplicity, efficiency, and functionality, Stanford CoreNLP has established itself as a leading tool in the field of natural language processing.

**Question**: An Efficient and Effective Method for Computing Similarity Between Two 3D Shapes

**Documents**: A Robust and Efficient Method for Solving the Camera Localization Problem
We present a robust and efficient method for solving the camera localization problem. Our method is based on the observation that the camera pose can be recovered from the intersection of a set of planes, which are defined by the cross product of corresponding 3D points and their 2D projections. We propose an algorithm that computes the intersection of these planes in a robust way, using a randomized sampling technique. The proposed method is evaluated on both synthetic and real data, and is shown to outperform state-of-the-art methods in terms of accuracy and efficiency.

**Similar Documents**: This paper introduces an innovative and efficient approach to address the camera localization challenge, a critical component in various computer vision applications. The methodology hinges on the insight that the camera's position and orientation can be deduced from the convergence point of a series of planes. These planes are uniquely determined by the cross product of corresponding three-dimensional points and their two-dimensional projections. To ensure robustness and reliability, we propose an algorithm that employs a randomized sampling technique to accurately compute the intersection of these planes. This approach not only enhances the method's resilience to outliers but also accelerates the computation process. Extensive evaluations conducted on both simulated and real-world datasets demonstrate the superiority of our technique compared to existing state-of-the-art solutions, particularly in terms of precision and computational efficiency.

Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.

Speech-based user interfaces are growing in popularity. Unfortunately, the technology expertise required to build speech UIs precludes many individuals from participating in the speech interface design process. Furthermore, the time and knowledge costs of building even simple speech systems make it difficult for designers to iteratively design speech UIs. SUEDE, the speech interface prototyping tool we describe in this paper, allows designers to rapidly create prompt/response speech interfaces. It offers an electronically supported Wizard of Oz (WOz) technique that captures test data, allowing designers to analyze the interface after testing. This informal tool enables speech user interface designers, even non-experts, to quickly create, test, and analyze speech user interface prototypes.

OntoNotes is a five year multi-site collaboration between BBN Technologies, Information Sciences Institute of University of Southern California, University of Colorado, University of Pennsylvania and Brandeis University. The goal of the OntoNotes project is to provide linguistic data annotated with a skeletal representation of the literal meaning of sentences including syntactic parse, predicate-argument structure, coreference, and word senses linked to an ontology, allowing a new generation of language understanding technologies to be developed with new functional capabilities. In its third year of existence, the OntoNotes project has generated a large amount of high quality data covering various layers of linguistic annotation. This is probably the first time that data of such quality has been available in large quantities covering multiple genres (newswire, broadcast news, broadcast conversation and weblogs) and languages (English, Chinese and Arabic). The guiding principle has been to find a “sweet spot” in the space of inter-tagger agreement, productivity, and depth of representation. The most effective use of this resource for research requires simultaneous access to multiple layers of annotation. This has been made possible by representing the corpus with a relational database to accommodate the dense connectedness of the data and ensure consistency across layers. In order to facilitate ease of understanding and manipulability, the database has also been supplemented with a object-oriented Python API. The tutorial consists of two parts. In the first part we will familiarize the user with this new resource, describe the various layers of annotations in some detail and discuss the linguistic principles and sometimes practical considerations behind the important design decisions that shapes the corpus. We will also describe the salient differences between the three languages at each layer of annotation and how linguistic peculiarities of different languages were handled in the data. In the second part, we will describe the data formats of each of the layers and talk about various design decisions that went into the creation of the architecture of the database and the individual tables comprising it, along with issues that came up during the representation process and compromises that were made without sacrificing some primary objectives one of which being the independent existence of each layer that is necessary to allow multi-site collaboration. We will explain how the database schema attempts to interconnect all the layers. Then we will go into the details of the Python API that allows easy access to each of the layers and show that by making the objects closely resemble database tables, the API allows for their flexible integration. This will be followed by a hands-on working session.

Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational practice, and programming education research hardly address the issue of teaching the skills needed for systematic development of programs.
 We provide a conceptual framework for incremental program development, called Stepwise Improvement, which unifies best practice in modern software development such as test-driven development and refactoring with the prevailing perspective of programming methodology, stepwise refinement. The conceptual framework enables well-defined characterizations of incremental program development.
 We utilize the conceptual framework to derive a programming process, STREAM, designed specifically for novices. STREAM is a carefully down-scaled version of a full and rich agile software engineering process particularly suited for novices learning object-oriented programming. In using it we hope to achieve two things: to help novice programmers learn faster and better while at the same time laying the foundation for a more thorough treatment of more advanced aspects of software engineering. In this article, two examples demonstrate the application of STREAM.
 The STREAM process has been taught in the introductory programming courses at our universities for the past three years and the results are very encouraging. We report on a small, preliminary study evaluating the learning outcome of teaching STREAM. The study indicates a positive effect on the development of students’ process competences.

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant. The core of the problem is that whether the user is browsing or searching, whether they are an eighth grade student or a Nobel prize winner, the identical information is selected and it is presented the same way. In this paper, we report on research that adapts information navigation based on a user profile structured as a weighted concept hierarchy. A user may create his or her own concept hierarchy and use them for browsing Web sites. Or, the user profile may be created from a reference ontology by ‘watching over the user’s shoulder’ while they browse. We show that these automatically created profiles reflect the user’s interests quite well and they are able to produce moderate improvements when applied to search results.

1. ABSTRACT Animated agents based either on real video, cartoon-style drawings or even model-based 3D graphics offer great promise for computer-based presentations as they make presentations more lively and appealing and allow for the emulation of conversation styles known from human-human communication. In this paper, we describe a life-like interface agent which presents multimedia material to the user following the directives of a script. The overall behavior of the presentation agent is partly determined by such a script, and partly by the agent's self-behavior. In our approach, the agent's behavior is defined in a declarative specification language. Behavior specifications are used to automatically generate a control module for an agent display system. The first part of the paper describes the generation process which involves AI planning and a two-step compilation. Since the manual creation of presentation scripts is tedious and error-prone, we also address the automated generation of presentation scripts which may be forwarded to the interface agent. The second part of the paper presents an approach for multimedia presentation design which combines hierarchical planning with temporal reasoning.

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief “opinionated survey” of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-

In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children’s stories show that our system outperforms previous data-driven approaches.

A new web content structure analysis based on visual representation is proposed in this paper. Many web applications such as information retrieval, information extraction and automatic page adaptation can benefit from this structure. This paper presents an automatic top-down, tag-tree independent approach to detect web content structure. It simulates how a user understands web layout structure based on his visual perception. Comparing to other existing techniques, our approach is independent to underlying documentation representation such as HTML and works well even when the HTML structure is far different from layout structure. Experiments show satisfactory results.

In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.


Question: Web-STAR: A visual, web-based integrated development environment for a narrative understanding system.

This paper introduces a new generative model designed to accurately parse natural language, specifically focusing on tree structures. The model innovates by separately scoring semantic (lexical dependency) and syntactic components, offering a clear conceptual advantage. This division not only simplifies the model but also provides distinct avenues for enhancing each component independently. Remarkably, despite its streamlined approach, the model's performance is on par with more complex, non-factored models. A key distinction of this factored model is its compatibility with an efficient A* parsing algorithm. Unlike contemporary parsing models, this compatibility allows for precise, efficient inference, a critical feature in natural language processing tasks. The model's design and efficiency make it a promising tool for advancing the field of natural language understanding and parsing.

This paper describes how language is grounded by a comprehension system called Lucia within a robotic agent called Rosie that can manipulate objects and navigate indoors. The whole system is built within the Soar cognitive architecture and uses Embodied Construction Grammar (ECG) as a formalism for describing linguistic knowledge. Grounding is performed using knowledge from the grammar itself, from the linguistic context, from the agent's perception, and from an ontology of long-term knowledge about object categories and properties and actions the agent can perform. The paper also describes a benchmark corpus of 200 sentences in this domain, along with test versions of the world model and ontology, and gold-standard meanings for each of the sentences. The benchmark is contained in the supplemental materials.

Spoken dialogue systems have received increased interest because they are potentially much more natural and powerful methods of communicating with machines than are current graphics-based interfaces. Wired for Speech presents basic research in the psychological and sociological aspects of voice synthesis and recognition. Its major lesson is that people attribute human characteristics to spoken dialogue systems for reasons related to human evolution. But although it contains interesting basic research, the book is mainly aimed at giving technological or marketing advice to those seeking to use voice interfaces when creating commercial applications. The book is oriented around a series of simple experiments designed to show just how pervasive psychological and social influences can be on the opinions and behaviors of people confronted with voice interfaces. Each chapter describes a basic research hypothesis, introduces an experiment to test it, and discusses its implications for designing voice interfaces: gender, personality, accent, ethnicity, emotion, number of distinct voices, use of " I " by the system, voices in concert with faces, mixed synthetic and recorded voices, context, and the effects of errors in human–computer cooperation. Although Wired for Speech is very accessible , especially to the non-scientist, it is written with an unusual bibliography style for an academic book: All references and details are given in a notes section at the end of the book, making up one third of the content. This narrative exposition style will probably not satisfy either type of reader: Scientists will be frustrated at the imprecision in argumen-tation, lack of detail in the book itself, and continually having to refer to the notes. The lack of detail also prevents the book from serving as a reference work. Meanwhile those needing advice when implementing voice interfaces will be puzzled at references to Chomsky, Grice, and Zemlin. Thus the book seems to suffer from not knowing its audience well, which is odd as this is precisely the lesson that the book tries to impart. Another complaint from the scientific point of view is the obsession this particular book has with casting every experimental result as advice for the business and marketing side of voice interfaces, typically concentrating on Web-based e-marketing examples such as buying books on line. Most of the experiments have sample sizes of between 40 and 50, but the authors seem ready to invite multi-billion-dollar businesses to immediately base their deployed systems on these results. Finally, and fundamentally, this …

Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological journals.

Programming is recognized as one of seven grand challenges in computing education. Decades of research have shown that the major problems novices experience are composition-based---they may know what the individual programming language constructs are, but they do not know how to put them together. Despite this fact, textbooks, educational practice, and programming education research hardly address the issue of teaching the skills needed for systematic development of programs.
 We provide a conceptual framework for incremental program development, called Stepwise Improvement, which unifies best practice in modern software development such as test-driven development and refactoring with the prevailing perspective of programming methodology, stepwise refinement. The conceptual framework enables well-defined characterizations of incremental program development.
 We utilize the conceptual framework to derive a programming process, STREAM, designed specifically for novices. STREAM is a carefully down-scaled version of a full and rich agile software engineering process particularly suited for novices learning object-oriented programming. In using it we hope to achieve two things: to help novice programmers learn faster and better while at the same time laying the foundation for a more thorough treatment of more advanced aspects of software engineering. In this article, two examples demonstrate the application of STREAM.
 The STREAM process has been taught in the introductory programming courses at our universities for the past three years and the results are very encouraging. We report on a small, preliminary study evaluating the learning outcome of teaching STREAM. The study indicates a positive effect on the development of students’ process competences.

IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a strong focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. An analysis of these activities strongly suggested that improving the organization’s ability to quickly discover each other's results and rapidly combine different technologies and approaches would accelerate scientific advance. Furthermore, the ability to reuse and combine results through a common architecture and a robust software framework would accelerate the transfer of research results in NLP into IBM’s product platforms. Market analyses indicating a growing need to process unstructured information, specifically multilingual, natural language text, coupled with IBM Research’s investment in NLP, led to the development of middleware architecture for processing unstructured information dubbed UIMA. At the heart of UIMA are powerful search capabilities and a data-driven framework for the development, composition and distributed deployment of analysis engines. In this paper we give a general introduction to UIMA focusing on the design points of its analysis engine architecture and we discuss how UIMA is helping to accelerate research and technology transfer. 1 Motivation and Objectives We characterize a UIM application as a software system that analyzes large volumes of unstructured information while consulting structured information sources to discover, organize and present knowledge relevant to the application’s end-user. We define structured information as information whose intended meaning is unambiguous and explicitly represented in the structure or format of the data. The canonical example of structured information is a relational database table. We define unstructured information as information whose intended meaning is only loosely implied by its form. The canonical example is a natural language document; other examples include voice, audio, image and video. To appear in a special issue of the Journal of Natural Language Engineering 2004 2 Ferrucci and Lally Do Not Distirubte without written permission from authors Market indicators suggest a rapid increase in the use of text and voice analytics to a growing class of commercially viable applications (Roush 2003). National security interests are driving applications to involve an even broader set of UIM technologies including image and video analytics. Emerging and commercially important UIM application areas include life sciences, e-commerce, technical support, advanced search, and national and business intelligence. In six major labs spread out over the globe, IBM Research has over 200 people working on Unstructured Information Management (UIM) technologies with a primary focus on Natural Language Processing (NLP). These researchers are engaged in activities ranging from natural language dialog, information retrieval, topic-tracking, named-entity detection, document classification and machine translation to bioinformatics and open-domain question answering. Each group is developing different technical and engineering approaches to process unstructured information in pursuit of their specific research agenda in the UIM space. While IBM Research’s independent UIM technologies fare well in scientific venues, accelerated development of integrated, robust applications is becoming increasingly important to IBM’s commercial interests. Furthermore the rapid integration of different alogorthims and techniques is also a means to advance scientific results. For example, experiments in statistical name-entity detection or machine translation may benefit from using a deep parser as a feature generator. Facilitating the rapid integration of an independently produced deep parser with statistical algorithm implementations can accelerate the research cycle time as well as the time to product. Some of the challenges that face a large research organization in efficiently leveraging and integrating its (and third-party) UIM assets include: • Organizational Structure. IBM Research is largely organized in three to five person teams geographically dispersed among different labs throughout world. This structure makes it more difficult to jointly develop and reuse technologies. • Skills Alignment. There are inefficiencies in having PhDs trained and specialized in, for example, computational linguistics or statistical machine learning, develop and apply the requisite systems expertise to deliver their work into product architectures and/or make it easily usable by other researchers. To appear in a special issue of the Journal of Natural Language Engineering 2004 3 Ferrucci and Lally Do Not Distirubte without written permission from authors • Development Inefficiencies. Research results are delivered in a myriad of interfaces and technologies. Reuse and integration often requires too much effort dedicated to technical adaptation. Too often work is replicated. • Speed to product. Software Products introduce their own architectures, interfaces and implementation technologies. Technology transfer from the lab to the products often becomes a significant engineering or rewrite effort for researchers. A common framework and engineering discipline could help address the challenges listed above. The Unstructured Information Management Architecture (UIMA) project was initiated within IBM Research based on the premise that a common software architecture for developing, composing and delivering UIM technologies, if adopted by the organization, would facilitate technology reuse and integration, enable quicker scientific experimentation and speed the path to product. IBM’s Unstructured Information Management Architecture (UIMA) high-level objectives are two fold: 1) Accelerate scientific advances by enabling the rapid combination of UIM technologies (e.g., natural language processing, audio and video analysis, information retrieval, etc.). 2) Accelerate transfer of UIM technologies to product by providing an architecture and associated framework implementations that promote reuse and support flexible deployment options. 2 The Unstructured Information Management Architecture (UIMA) UIMA is a software architecture for developing UIM applications. The UIMA high-level architecture, illustrated in Figure 1, defines the roles, interfaces and communications of large-grained components essential for UIM applications. These include components capable of analyzing unstructured artifacts, integrating and accessing structured sources and storing, indexing and searching for artifacts based on discovered semantic content resulting from analyses. A primary design point for UIMA is that it should admit the implementation of middleware frameworks that provide a component-based infrastructure for developing UIM applications suitable for vastly different deployment environments. These should range from lightweight and embeddable implementations to highly scalable implementations that are meant to exploit clusters of machines and provide high throughput, high availability serviced-based offerings. While the primary focus of current UIMA framework implementations is squarely on natural language To appear in a special issue of the Journal of Natural Language Engineering 2004 4 Ferrucci and Lally Do Not Distirubte without written permission from authors UIM Application Analysis Collection Level Analysis Document, Collection & Metadata Store Knowledge Source Access Collection Processing Manager (CPM) Knowledge & Data Bases Unstructured Information (Text) Analysis Engines Crawlers Analysis Engine Directory Acquisition Unstructured Information Analysis Structured Information Access Semantic Search Engine Indices Indices Documents Collections Metadata Knowledge Source Adapter Directory Access

Chat-bots are computer programs coded to have a textual or verbal conversation which is logical or intelligent. Chat-bots are designed to make humans believe that they are talking to a human; but instead they are in fact talking to a machine. Taking advantage of this transparency property of chat-bot, an artificial character and personality can be given to a chat-bot which acts like a person of a specific profession. This paper describes an approach to the idea of implementing web-based artificially intelligent chat-bot as a personal assistant of the user, which stimulates setting and initiating meetings of user with his clients. The exchange of information happens through email conversations whereas its evaluation happens through natural language procession and natural language generation and AIML files.

We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies).
We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.

Even if the benefits of the usability testing are remarkable, it is scarcely adopted in the software development process. To foster its adoption, this paper presents a Web platform, UTAssistant, that supports people, also without skills in Human-Computer Interaction (HCI), in evaluating Web site usability.

As autonomous robots become more complex in their behavior, more sophisticated software architectures are required to support the ever more sophisticated robotics software. These software architectures must support complex behaviors involving adaptation and learning, implemented, in particular, by neural networks. We present in this paper a neural based schema [2] software architecture for the development and execution of autonomous robots in both simulated and real worlds. This architecture has been developed in the context of adaptive robotic agents, ecological robots [6], cooperating and competing with each other in adapting to their environment. The architecture is the result of integrating a number of development and execution systems: NSL, a neural simulation language; ASL, an abstract schema language; and MissionLab, a schema-based mission-oriented simulation and robot system. This work contributes to modeling in Brain Theory (BT) and Cogniti ve Psychology, with applications in Distributed Artificial Intell igence (DAI), Autonomous Agents and Robotics. Areas: Robotics, Agent-oriented programming, Neural Nets

The Biological Magnetic Resonance Data Bank (BMRB) is a public repository of Nuclear Magnetic Resonance (NMR) spectroscopic data of biological macromolecules. It is an important resource for many researchers using NMR to study structural, biophysical, and biochemical properties of biological macromolecules. It is primarily maintained and accessed in a flat file ASCII format known as NMR-STAR. While the format is human readable, the size of most BMRB entries makes computer readability and explicit representation a practical requirement for almost any rigorous systematic analysis. To aid in the use of this public resource, we have developed a package called nmrstarlib in the popular open-source programming language Python. The nmrstarlib’s implementation is very efficient, both in design and execution. The library has facilities for reading and writing both NMR-STAR version 2.1 and 3.1 formatted files, parsing them into usable Python dictionary- and list-based data structures, making access and manipulation of the experimental data very natural within Python programs (i.e. “saveframe” and “loop” records represented as individual Python dictionary data structures). Another major advantage of this design is that data stored in original NMR-STAR can be easily converted into its equivalent JavaScript Object Notation (JSON) format, a lightweight data interchange format, facilitating data access and manipulation using Python and any other programming language that implements a JSON parser/generator (i.e., all popular programming languages). We have also developed tools to visualize assigned chemical shift values and to convert between NMR-STAR and JSONized NMR-STAR formatted files. Full API Reference Documentation, User Guide and Tutorial with code examples are also available. We have tested this new library on all current BMRB entries: 100% of all entries are parsed without any errors for both NMR-STAR version 2.1 and version 3.1 formatted files. We also compared our software to three currently available Python libraries for parsing NMR-STAR formatted files: PyStarLib, NMRPyStar, and PyNMRSTAR. The nmrstarlib package is a simple, fast, and efficient library for accessing data from the BMRB. The library provides an intuitive dictionary-based interface with which Python programs can read, edit, and write NMR-STAR formatted files and their equivalent JSONized NMR-STAR files. The nmrstarlib package can be used as a library for accessing and manipulating data stored in NMR-STAR files and as a command-line tool to convert from NMR-STAR file format into its equivalent JSON file format and vice versa, and to visualize chemical shift values. Furthermore, the nmrstarlib implementation provides a guide for effectively JSONizing other older scientific formats, improving the FAIRness of data in these formats.


Question: Web-STAR: A visual, web-based integrated development environment for a narrative understanding system.

This paper presents the current status and planned functionalities of Web-STAR, a web-based integrated development environment (IDE) specifically designed for a story comprehension system. Web-STAR aims to offer a user-friendly platform for assisting developers in the creation, evaluation, debugging, and testing of story comprehension algorithms. To achieve this, innovative support mechanisms are being developed that align with the unique requirements of story comprehension and the practical challenges faced in real-world applications. Web-STAR draws inspiration from the findings of a project dedicated to enhancing methodologies for the development of story comprehension systems. At present, the tool offers source-code editors equipped with syntax highlighting, syntax validation, and a visual representation of the program structure, tailored for the specific language of the story comprehension system. Additional features include the capability to integrate with external solvers, visual debugging tools, and an intuitive interface for modifying story outcomes. Web-STAR is accessible as a plugin for the widely-used Eclipse platform and is designed with modular architecture to facilitate future expansions of the IDE.

The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books and decompiled games) to create language models suitable to this domain. Moreover, we embed mechanisms that allow us to specify, and separately handle, important tasks as fighting opponents, managing inventory, and navigating on the game map. We validated usefulness of these mechanisms, measuring agent's performance on the set of 50 interactive fiction games. Finally, we show that our agent plays on a level comparable to the winner of the last year Text-Based Adventure AI Competition.

TALE-SPIN, the laat major AI attempt at atory generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR ia a program (under development) which generates atoriea as a creative reaaoner in purauit of her own narrative goals. It is thus intended to aimulate an author's mind aa ahe makes up a story, rather than the world as things happen in it. The four major forces driving the atory generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal. 1. World Simulation Based Story Generation TALE-SPIN [Meehan, 1976] is a program which made up Aesop-fable-like conceptual atoriea, which it then expressed in natural language. Each atory TALE-SPIN made up waa based on a model of the storyworld, including the characters in it and their goala, peraonalities, and interperaonal relatione. Storiea were apun by working out consequences of the model. Thus, central to TALE-SPIN waa the idea of world simulation. The three levels TALE-SPIN was most concerned with simulating were: 1. character intentionality-e.g., if Joe Bear was hungry, he would try to get something to eat 2. social conatrainta-e.g., if Joe Bear believed Irving Bird to be hia friend, he could aak an outright favor; if Joe Bear believed their relation to be a bit cooler, he would feel obliged to offer something in return (auch aa a worm) 3. phyaical cauaality-e.g., if Irving Bird ate a worm, the worm would cease to exist; after "fooling around", Aa oppoaed to Sheldon Klein's ayetem which only produced atory texts and which ia incapable, in principle, of understanding the atoriea it "makes up" [Klein et el., 1976]. characters felt "wiped out" World simulation, however, fails to reflect the proctaa that an author goes through in making up a atory-storyworlds are developed by authors aa needed, frequently as post hoc justification for eventa that the author has already decided ahe wants as part of the story. As illustration, let us consider the question of why, in the Star Wars aequel, "The Empire Strikes Back", Princess Leia and Solo go to visit Landeau. If one asks a reader (or viewer) this question, the anawer will be along the lines of, "They needed a place to hide from imperial stormtroopers until they got the hyperdrive fixed, and Landeau was an old buddy of Solo's, and ..." • …

We designed eSports - a collaborative and synchronous video annotation platform, which is to be used in Internet scale cross-platform grid computing environment to facilitate computer supported cooperative work (CSCW) in education settings such as distance sport coaching, distance classroom etc. Different from traditional multimedia annotation systems, eSports provides the capabilities to collaboratively and synchronously play and archive real time live video, to take snapshots, to annotate video snapshots using whiteboard and to play back the video annotations synchronized with original video streams. eSports is designed based on the grid based collaboration paradigm $the shared event model using NaradaBrokering, which is a publish/subscribe based distributed message passing and event notification system. In addition to elaborate the design and implementation of eSports, we analyze the potential use cases of eSports under different education settings. We believed that eSports is very useful to improve the online collaborative coaching and education.

There is an increased interest in artificially intelligent technology that surrounds us and takes decisions on our behalf. This creates the need for such technology to be able to communicate with humans and understand natural language and non-verbal behaviour that may carry information about our complex physical world. Artificial agents today still have little knowledge about the physical space that surrounds us and about the objects or concepts within our attention. We are still lacking computational methods in understanding the context of human conversation that involves objects and locations around us. Can we use multimodal cues from human perception of the real world as an example of language learning for robots? Can artificial agents and robots learn about the physical world by observing how humans interact with it and how they refer to it and attend during their conversations? This PhD project’s focus is on combining spoken language and non-verbal behaviour extracted by multi-party dialogue in order to increase context awareness and spatial understanding for artificial agents.

Natural Language Interface to Database (NLIDB) systems convert a Natural Language (NL) query into a Structured Query Language (SQL) and then use the SQL query to retrieve information from a database. The main advantage with this function of an NLIDB system is that it makes information retrieval much easier and more importantly, it also allows non-technical users to query a database. In this work, we present an effective usage of tokens in an NL query to address various problems in an NLIDB system. The conversion of an NL query to SQL query is framed as a token classification problem, using which we unveil a novel method of mapping an NL query to an SQL query. Our approach reasonably addresses domain dependency which is one of the major drawbacks of NLIDB systems. Concepts Identification is a major component of NLIDB systems (Gupta et al., 2012; Srirampur et al., 2014). We improve Concepts Identification (Srirampur et al., 2014) by making use of Stanford Parser Dependencies. Our approach is more robust than previously proposed methods. In addition, we also discuss how Concepts Identification can be applied to address Ellipsis in a dialogue process. In addition to providing results to a user, it is essential to provide a relevant and a compact set of results. We propose a new problem of generating a compact set of results to a user query. At a higher level, user-system interactions are modeled based on patterns frequently observed between a user’s current query and his previous queries, while interacting with a system. Using these models, we propose a novel method of system prompting to help a user obtain a smaller and a relevant set of results. In addition to providing a compact and a relevant set of results, it is imperative that answers of an NLIDB system are comprehensible even by the people who are less familiar with a common language like English. NLIDB systems use Natural Language Generation (NLG) modules to provide answers in the form of sentences. It is important to make sure that an answer generated by an NLG module is very simple to understand. This brings in the problem of text simplification, wherein, one of the most crucial and initial sub-problems is Complex Word Identification (CWI). We address the problem of CWI by distinguishing words as simple and complex. A plethora of classifiers were explored to identify the complex words. This information helps us in improving the simplification of an NLIDB system’s final output to a user. To summarize, in addition to addressing problems within an NLIDB system, this work touches postNLIDB problems like results processing. All the proposed issues are tackled using tokens in an NL query as the basic and a driving unit of force.

This article describes the software architecture of an auto nomous, interactive tour-guide robot. It presents a modular and distributed software archi te ture, which integrates localization, mapping, collision avoidance, planning, and vari ous modules concerned with user interaction and Web-based telepresence. At its heart, the s oftware approach relies on probabilistic computation, on-line learning, and any-time alg orithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot ’s peration. Special emphasis is placed on the design of interactive capabilities that appeal to people’s intuition. The interface provides new means for human-robot interaction w ith crowds of people in public places, and it also provides people all around the world with the ability to establish a “virtual telepresence” using the Web. To illustrate our approac h, results are reported obtained in mid-1997, when our robot “RHINO” was deployed for a period of six days in a densely populated museum. The empirical results demonstrate relia bl operation in public environments. The robot successfully raised the museum’s atten dance by more than 50%. In addition, thousands of people all over the world controlled the robot through the Web. We conjecture that these innovations transcend to a much large r range of application domains for service robots.

We introduce a neural semantic parser which is interpretable and scalable. Our model converts natural language utterances to intermediate, domain-general natural language representations in the form of predicate-argument structures, which are induced with a transition system and subsequently mapped to target domains. The semantic parser is trained end-to-end using annotated logical forms or their denotations. We achieve the state of the art on SPADES and GRAPHQUESTIONS and obtain competitive results on GEOQUERY and WEBQUESTIONS. The induced predicate-argument structures shed light on the types of representations useful for semantic parsing and how these are different from linguistically motivated ones.1

While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief “opinionated survey” of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-

72 AI MAGAZINE The Tactical Language and Culture Training System (TLCTS) helps people quickly acquire functional skills in foreign languages and cultures. It includes interactive lessons that focus on particular communicative skills and interactive games that apply those skills. Heavy emphasis is placed on spoken communication: learners must learn to speak the foreign language to complete the lessons and play the games. It focuses on the language and cultural skills needed to accomplish particular types of tasks and gives learners rich, realistic opportunities to practice achieving those tasks. Several TLCTS courses have been developed so far. Tactical Iraqi, Tactical Pashto, and Tactical French are in widespread use by U.S. marines and soldiers, and increasingly by military service members in other countries. Additional courses are being developed for use by business executives, workers for nongovernmental organizations, and high school and college students. While precise numbers are impossible to obtain (we do not control copies made by the U.S. government), over 40,000 and as many as 60,000 people have trained so far with TLCTS courses. More than 1000 people download copies of TLCTS courses each month, either for their own use or to set up computer language labs and redistribute copies to students. Just one training site, the military advisor training center at Fort Riley, Kansas, trains approximately 10,000 people annually. Artificial intelligence technologies play multiple essential functions in TLCTS. Speech is the primary input modality, so automated speech recognition tailored to foreign language learners is essential. TLCTS courses are populated with “virtual humans” that engage in dialogue with learners. AI techniques are used to model the decision processes of the virtual humans and to support the generation of their behavior. This makes it possible to give learners extensive conversational practice. Learner modeling software continually monitors each learner’s application of communication skills to estimate the learner’s


Question: Empowering the Masses: The Significance of Human Input in Interactive Machine Learning Systems

In the realm of interactive machine learning (IML), where human users actively engage in the model-building process, the evaluation of models assumes a critical role. Users often rely on their assessment of model performance to guide their efforts in enhancing the model. Recognizing the criteria that are most significant to users can significantly impact the design of user interfaces for model evaluation and the selection of learning algorithms. This study delves into the evaluation practices of end users who are involved in constructing supervised learning systems for practical gesture analysis tasks. Our analysis uncovers a broad spectrum of user-defined evaluation criteria, ranging from conventional metrics like accuracy and cost to more unconventional factors such as the element of surprise or unexpectedness. We observed that users employ various evaluation strategies, including cross-validation and immediate, real-time evaluation, not merely to evaluate and refine algorithms' performance but also to enhance their ability to provide more effective training data. Interestingly, we found that the evaluation process also served as a learning tool for users, helping them understand the feasibility and ease of building different model types. This insight sometimes led users to adjust the definition of the learning problem or their strategies for utilizing the trained models in real-world scenarios. We explore the implications of these findings on the significance of generalization accuracy in IML, the development of novel algorithms and user interfaces, and the potential advantages of integrating human interaction into the design of supervised learning systems.

The Internet of Things assumes that objects have digital functionality and can be identified and tracked automatically. The main goal of embedded interaction is to look at new opportunities that arise for interactive systems and the immediate value users gain. The authors developed various prototypes to explore novel ways for human-computer interaction (HCI), enabled by the Internet of Things and related technologies. Based on these experiences, they derive a set of guidelines for embedding interfaces into people's daily lives.

In this paper we describe Paul, a robotic installation that produces observational sketches of people. The sketches produced have been considered of interest by fine art professionals in recent art fairs and exhibitions, as well as by the public at large. We identify factors that may account for the perceived qualities of the produced sketches. A technical overview of the system is also presented.

Humans communicate with each other using abstract signs and symbols. While the cooperation between humans and machines can be a powerful tool for solving complex or difficult tasks, the communication must be at the abstract enough level that is both natural to the humans and understandable to the machines. Our paper focuses on natural language and in particular on sign language recognition. The approach described here combines heuristics for segmentation of the video stream by identifying the epenthesis with stacked LSTMs for automatic classification of the derived segments. This approach segments continuous stream of video data with the accuracy of over 80% and reaches accuracies of over 95% on segmented sign recognition. We compare results in terms of the number of signs being recognised and the utility of various features used for the recognition. We aim to integrate the models into a single continuous sign language recognition system and to learn policies for specific domains that would map perception of a robot to its action. This will improve the accuracy of understanding the common task within the shared activity between a human and a machine. Such understanding, in turn, will foster meaningful cooperation.

Crowdsourcing has inspired a variety of novel mobile applications. However, identifying common practices across different applications is still challenging. In this paper, we use smart parking as a case study to investigate features of crowdsourcing that may apply to other mobile applications. Based on this we derive principles for efficiently harnessing crowdsourcing. We draw three key guidelines: First, we suggest that that the organizer can play an important role in coordinating participants', a key factor to successful crowdsourcing experience. Second, we suggest that the expected participation rate is a key factor when designing the crowdsourcing system: a system with a lower expected participation rate will place a higher burden in individual participants (e.g., through more complex interfaces that aim to improve the accuracy of the collected data). Finally, we suggest that not only above certain threshold of contributors, a crowdsourcing-based system is resilient to freeriding but, surprisingly, that including freeriders (i.e., actors that do not participate in system effort but share its benefits in terms of coordination) benefits the entire system.

We present the first formal study of crowdworkers who have disabilities via in-depth open-ended interviews of 17 people (disabled crowdworkers and job coaches for people with disabilities) and a survey of 631 adults with disabilities. Our findings establish that people with a variety of disabilities currently participate in the crowd labor marketplace, despite challenges such as crowdsourcing workflow designs that inadvertently prohibit participation by, and may negatively affect the worker reputations of, people with disabilities. Despite such challenges, we find that crowdwork potentially offers different opportunities for people with disabilities relative to the normative office environment, such as job flexibility and lack of a need to rely on public transit. We close by identifying several ways in which crowd labor platform operators and/or individual task requestors could improve the accessibility of this increasingly important form of employment.

The advancement in technology indicates that there is an opportunity to enhance Human-Computer Interaction by way of affective state recognition. Affective state recognition is typically based on passive stimuli such as watching video clips, which does not reflect genuine interaction. This paper presents a study on affective state recognition using active stimuli, i.e. facial expressions of users when they attempt computerised tasks, particularly across typical usage of computer systems. A data collection experiment is presented for acquiring data from normal users whilst they interact with software, attempting to complete a set of predefined tasks. In addition, a hierarchical machine learning approach is presented for facial expression-based affective state recognition, which employs an Euclidean distance-based feature representation, conjointly with a customised encoding for users’ self-reported affective states. Consequently, the aim is to find the potential relationship between the facial expressions, as defined by Paul Ekman, and the self-reported emotional states specified by users using Russells Circumplex model, in relation to the actual feelings and affective states. The main findings of this study suggest that facial expressions cannot precisely reveal the actual feelings of users whilst interacting with common computerised tasks. Moreover, during active interaction tasks more variation occurs within the facial expressions of participants than occurs within passive interaction.

In this paper we present a novel wearable navigation system along with implicit HCI (iHCI) model, where interaction with technology is dissolved into a day-today activity. In this type of HCI model a computer takes the input and tries to output an action that is a proactive anticipation of next action of a user. Usually, in urban areas people use voice assisted navigation systems or navigation guidelines displayed on a mobile phone. Some navigation systems are already installed on car dashboard, which needs explicit attention in order to make driving decisions. A navigation system using haptic perception to guide a user throughout a journey is the key contribution of this paper. It does not ask for explicit user attention and demonstrates the indolent form of technological interaction. This wearable device is an index finger sleeve, which consists of vibrator modules, Bluetooth communication module and Microcontroller Unit (MCU). A working prototype has been built and tested.

We present and discuss four important yet underserved research questions critical to the future of sharedenvironment human-robot collaboration. We begin with a brief survey of research surrounding individual components required for a complete collaborative robot control system, discussing the current state of the art in Learning from Demonstration, active learning, adaptive planning systems, and intention recognition. We motivate the exploration of the presented research questions by relating them to existing work and representative use cases from the domains of construction and cooking.

We consider the problem of learning preferences over trajectories for mobile manipulators such as personal robots and assembly line robots. The preferences we learn are more intricate than simple geometric constraints on trajectories; they are rather governed by the surrounding context of various objects and human interactions in the environment. We propose a coactive online learning framework for teaching preferences in contextually rich environments. The key novelty of our approach lies in the type of feedback expected from the user: the human user does not need to demonstrate optimal trajectories as training data, but merely needs to iteratively provide trajectories that slightly improve over the trajectory currently proposed by the system. We argue that this coactive preference feedback can be more easily elicited than demonstrations of optimal trajectories. Nevertheless, theoretical regret bounds of our algorithm match the asymptotic rates of optimal trajectory algorithms. We implement our algorithm on two high-degree-of-freedom robots, PR2 and Baxter, and present three intuitive mechanisms for providing such incremental feedback. In our experimental evaluation we consider two context rich settings, household chores and grocery store checkout, and show that users are able to train the robot with just a few feedbacks (taking only a few minutes).

We consider the problem of human-assisted graph search: given a directed acyclic graph with some (unknown) target node(s), we consider the problem of finding the target node(s) by asking an omniscient human questions of the form “Is there a target node that is reachable from the current node?”. This general problem has applications in many domains that can utilize human intelligence, including curation of hierarchies, debugging workflows, image segmentation and categorization, interactive search and filter synthesis. To our knowledge, this work provides the first formal algorithmic study of the optimization of human computation for this problem. We study various dimensions of the problem space, providing algorithms and complexity results. We also compare the performance of our algorithm against other algorithms, for the problem of webpage categorization on a real taxonomy. Our framework and algorithms can be used in the design of an optimizer for crowdsourcing platforms such as Mechanical Turk.