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This book describes experimental advances made in the interpretation of visual motion over the last few years that have moved researchers closer to emulating the way in which we recover information about the surrounding world.

This supplement to Building Problem Solvers contains the Common Lisp code examples referenced throughout the text. The code is available on disk and can also be downloaded via ftp.

For nearly two decades, Kenneth Forbus and Johan de Kleer have accumulated a substantial body of knowledge about the principles and practice of creating problem solvers. In some cases they are the inventors of the ideas or techniques described, and in others, participants in their development.Building Problem Solvers communicates this knowledge in a focused, cohesive manner. It is unique among standard artificial intelligence texts in combining science and engineering, theory and craft to describe the construction of AI reasoning systems, and it includes code illustrating the ideas.After working through Building Problem Solvers, readers should have a deep understanding of pattern directed inference systems, constraint languages, and truth maintenance systems. The diligent reader will have worked through several substantial examples, including systems that perform symbolic algebra, natural deduction, resolution, qualitative reasoning, planning, diagnosis, scene analysis, and temporal reasoning.

First published in 1977. Routledge is an imprint of Taylor & Francis, an informa company.

This monograph by one of the world's leading vision researchers provides a thorough, mathematically rigorous exposition of a broad and vital area in computer vision: the problems and techniques related to three-dimensional (stereo) vision and motion.

Using a case-based approach, this volume focuses on constructing explanations. All chapters relate to the problem of building computer programs that can develop hypotheses about what might have caused an observed event, an ability that is a hallmark of human intelligence.

Using a case-based approach, this volume focuses on constructing explanations. All chapters relate to the problem of building computer programs that can develop hypotheses about what might have caused an observed event, an ability that is a hallmark of human intelligence.

First Published in 1986. Routledge is an imprint of Taylor & Francis, an informa company.

First Published in 1989. Routledge is an imprint of Taylor & Francis, an informa company.

First Published in 1987. Routledge is an imprint of Taylor & Francis, an informa company.

Defining the structure and complexity of human language in terms of the mathematics of information and computation. Ristad argues that language is the process of constructing linguistic representations from the forms produced by other cognitive modules - a process that is NP-complete.

The broad range of material included in these volumes suggests to the newcomer the nature of the field of artificial intelligence, while those with some background in AI will appreciate the detailed coverage of the work being done at MIT. The results presented are related to the underlying methodology. Each chapter is introduced by a short note outlining the scope of the problem begin taken up or placing it in its historical context.Contents, Volume IExpert Problem Solving: Qualitative and Quantitative Reasoning in Classical Mechanics • Problem Solving About Electrical Circuits • Explicit Control of Reasoning • A Glimpse of Truth Maintenance • Design of a Programmer's Apprentice • Natural Language Understanding and Intelligent Computer Coaches: A Theory of Syntactic Recognition for Natural Language • Disambiguating References and Interpreting Sentence Purpose in Discourse • Using Frames in Scheduling • Developing Support Systems for Information Analysis • Planning and Debugging in Elementary Programming • Representation and Learning: Learning by Creating and Justifying Transfer Frames • Descriptions and the Specialization of Concept • The Society Theory of Thinking • Representing and Using Real-World Knowledge

Psychology and philosophy have long studied the nature and role of explanation. More recently, artificial intelligence research has developed promising theories of how explanation facilitates learning and generalization. By using explanations to guide learning, explanation-based methods allow reliable learning of new concepts in complex situations, often from observing a single example. The author of this volume, however, argues that explanation-based learning research has neglected key issues in explanation construction and evaluation. By examining the issues in the context of a story understanding system that explains novel events in news stories, the author shows that the standard assumptions do not apply to complex real-world domains. An alternative theory is presented, one that demonstrates that context -- involving both explainer beliefs and goals -- is crucial in deciding an explanation''s goodness and that a theory of the possible contexts can be used to determine which explanations are appropriate. This important view is demonstrated with examples of the performance of ACCEPTER, a computer system for story understanding, anomaly detection, and explanation evaluation.