Bücher der Reihe Artificial Intelligence Series

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

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.

This tour de force in experimental robotics describes the first robot able to play, and even beat, human ping-pong players.

An inside report on current applications, trends, and future opportunities in one of the world's major centers of artificial intelligence research.

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

Made-Up Minds addresses fundamental questions of learning and concept invention by means of an innovative computer program that is based on the cognitive-developmental theory of psychologist Jean Piaget. Drescher uses Piaget's theory as a source of inspiration for the design of an artificial cognitive system called the schema mechanism, and then uses the system to elaborate and test Piaget's theory. The approach is original enough that readers need not have extensive knowledge of artificial intelligence, and a chapter summarizing Piaget assists readers who lack a background in developmental psychology. The schema mechanism learns from its experiences, expressing discoveries in its existing representational vocabulary, and extending that vocabulary with new concepts. A novel empirical learning technique, marginal attribution, can find results of an action that are obscure because each occurs rarely in general, although reliably under certain conditions. Drescher shows that several early milestones in the Piagetian infant's invention of the concept of persistent object can be replicated by the schema mechanism.