The desktop interface in intelligent tutoring systems

The interface between an Intelligent Tutoring System (ITS) and the person being tutored is critical to the success of the learning process. If the interface to the ITS is confusing or non-supportive of the tutored domain, the effectiveness of the instruction will be diminished or lost entirely. Consequently, the interface to an ITS should be highly integrated with the domain to provide a robust and semantically rich learning environment. In building an ITS for ZetaLISP on a LISP Machine, a Desktop Interface was designed to support a programming learning environment. Using the bitmapped display, windows, and mouse, three desktops were designed to support self-study and tutoring of ZetaLISP. Through organization, well-defined boundaries, and domain support facilities, the desktops provide substantial flexibility and power for the student and facilitate learning ZetaLISP programming while screening the student from the complex LISP Machine environment. The student can concentrate on learning ZetaLISP programming and not on how to operate the interface or a LISP Machine

Retrieval, reuse, revision and retention in case-based reasoning

El original está disponible en www.journals.cambridge.orgCase-based reasoning (CBR) is an approach to problem solving that emphasizes the role of prior experience during future problem solving (i.e., new problems are solved by reusing and if necessary adapting the solutions to similar problems that were solved in the past). It has enjoyed considerable success in a wide variety of problem solving tasks and domains. Following a brief overview of the traditional problem-solving cycle in CBR, we examine the cognitive science foundations of CBR and its relationship to analogical reasoning. We then review a representative selection of CBR research in the past few decades on aspects of retrieval, reuse, revision, and retention.Peer reviewe

Domain-Specific Knowledge Acquisition for Conceptual Sentence Analysis

The availability of on-line corpora is rapidly changing the field of natural language processing (NLP) from one dominated by theoretical models of often very specific linguistic phenomena to one guided by computational models that simultaneously account for a wide variety of phenomena that occur in real-world text. Thus far, among the best-performing and most robust systems for reading and summarizing large amounts of real-world text are knowledge-based natural language systems. These systems rely heavily on domain-specific, handcrafted knowledge to handle the myriad syntactic, semantic, and pragmatic ambiguities that pervade virtually all aspects of sentence analysis. Not surprisingly, however, generating this knowledge for new domains is time-consuming, difficult, and error-prone, and requires the expertise of computational linguists familiar with the underlying NLP system. This thesis presents Kenmore, a general framework for domain-specific knowledge acquisition for conceptual sentence analysis. To ease the acquisition of knowledge in new domains, Kenmore exploits an on-line corpus using symbolic machine learning techniques and robust sentence analysis while requiring only minimal human intervention. Unlike most approaches to knowledge acquisition for natural language systems, the framework uniformly addresses a range of subproblems in sentence analysis, each of which traditionally had required a separate computational mechanism. The thesis presents the results of using Kenmore with corpora from two real-world domains (1) to perform part-of-speech tagging, semantic feature tagging, and concept tagging of all open-class words in the corpus; (2) to acquire heuristics for part-ofspeech disambiguation, semantic feature disambiguation, and concept activation; and (3) to find the antecedents of relative pronouns

Planning Responses From High-Level Goals: Adopting the Respondent\u27s Perspective Cooperative Response Generation

Within the natural-language research community it has long been acknowledged that the conventions and pragmatics of natural-language communication often oblige dialogue systems to consider and address the underlying purposes of queries in their responses rather than answering them literally and without further comment or elaboration. Such systems cannot simply translate their users\u27 requests into transactions on database or expert systems, but must apply many more complex reasoning mechanisms to the task of selecting responses that are both appropriate and useful. This idea has given rise to a broadly-defined program of research in cooperative response generation (CRG). Research in CRG carried on over more than a decade has yielded a substantial body of literature. Analysis of that literature, however, shows that investigators have focused primarily on modeling manifestations of cooperative behavior without directly considering the nature and motivations of the behavior itself. But if we want to develop natural language dialogue systems that are truly to function as cooperative respondents instead of serving only as models of particular kinds of cooperative responses, a different approach is required. I identify two opposing perspectives on the process of cooperative response generation: the questioner-based and the respondent-based perspectives. I argue that past research efforts have largely been questioner-based, and that this view has led to the development of theories that are incompatible and cannot be integrated. I propose the respondent-based view as an alternative, and provide evidence that taking such a perspective might allow several interesting but otherwise poorly-understood aspects of cooperative response behavior to be modeled. The final portion of the dissertation explores the computational implications of a respondent-based perspective. I outline the architecture of a Cooperative Response Planning System, a dialogue system that raises, reasons about, and attempts to satisfy high-level cooperative goals in its responses. This architecture constitutes a first approximation to a theory of how a system might reason from the beliefs it derives from a questioner\u27s utterances to choose a cooperative response. The processing of two sample responses in this framework is described in detail to illustrate the architecture\u27s capabilities

Third Conference on Artificial Intelligence for Space Applications, part 1

The application of artificial intelligence to spacecraft and aerospace systems is discussed. Expert systems, robotics, space station automation, fault diagnostics, parallel processing, knowledge representation, scheduling, man-machine interfaces and neural nets are among the topics discussed

Developing a computational framework for explanation generation in knowledge-based systems and its application in automated feature recognition

A Knowledge-Based System (KBS) is essentially an intelligent computer system which explicitly or tacitly possesses a knowledge repository that helps the system solve problems. Researches focusing on building KBSs for industrial applications to improve design quality and shorten research cycle are increasingly attracting interests. For the early models, explanability is considered as one of the major benefits of using KBSs since that most of them are generally rule-based systems and the explanation can be generated based on the rule traces of the reasoning behaviors. With the development of KBS, the definition of knowledge base is becoming much more general than just using rules, and the techniques used to solve problems in KBS are far more than just rule-based reasoning. Many Artificial Intelligence (AI) techniques are introduced, such as neural network, genetic algorithm, etc. The effectiveness and efficiency of KBS are thus improved. However, as a trade-off, the explanability of KBS is weakened. More and more KBSs are conceived as black-box systems that do not run transparently to users, resulting in loss of trusts for the KBSs. Developing an explanation model for modern KBSs has a positive impact on user acceptance of the KBSs and the advices they provided. This thesis proposes a novel computational framework for explanation generation in KBS. Different with existing models which are usually built inside a KBS and generate explanations based on the actual decision making process, the explanation model in our framework stands outside the KBS and attempts to generate explanations through the production of an alternative justification that is unrelated to the actual decision making process used by the system. In this case, the knowledge and reasoning approaches in the explanation model can be optimized specially for explanation generation. The quality of explanation is thus improved. Another contribution in this study is that the system aims to cover three types of explanations (where most of the existing models only focus on the first two): 1) decision explanation, which helps users understand how a KBS reached its conclusion; 2) domain explanation, which provides detailed descriptions of the concepts and relationships within the domain; 3) software diagnostic, which diagnoses user observations of unexpected behaviors of the system or some relevant domain phenomena. The framework is demonstrated with a case of Automated Feature Recognition (AFR). The resulting explanatory system uses Semantic Web languages to implement an individual knowledge base only for explanatory purpose, and integrates a novel reasoning approach for generating explanations. The system is tested with an industrial STEP file, and delivers good quality explanations for user queries about how a certain feature is recognized

First Annual Workshop on Space Operations Automation and Robotics (SOAR 87)

Several topics relative to automation and robotics technology are discussed. Automation of checkout, ground support, and logistics; automated software development; man-machine interfaces; neural networks; systems engineering and distributed/parallel processing architectures; and artificial intelligence/expert systems are among the topics covered