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 II: Understanding Vision: Representing and Computing Visual Information; Visual Detection of Light Sources; Representing and Analyzing Surface Orientation; Registering Real Images Using Synthetic Images; Analyzing Curved Surfaces Using Reflectance Map Techniques; Analysis of Scenes from a Moving Viewpoint; Manipulation and Productivity Technology: Force Feedback in Precise Assembly Tasks; A Language for Automatic Mechanical Assembly; Kinematics, Statics, and Dynamics of Two-Dimensional Manipulators; Understanding Manipulator Control by Synthesizing Human Handwriting; Computer Design and Symbol Manipulation: The LISP Machine; Shallow Binding in LISP 1.5; Optimizing Allocation and Garbage Collection of Spaces; Compiler Optimization Based on Viewing LAMBDA as RENAME Plus GOTO; Control Structure as Patterns of Passing Messages.
This book explains how it is possible for computers to reason and perceive, thus introducing the field called artificial intelligence. From the book, you learn why the field is important, both as a branch of engineering and as a science.
If you are a computer scientist or an engineer, you will enjoy the book, because it provides a cornucopia of new ideas for representing knowledge, using knowledge, and building practical systems. If you are a psychologist, biologist, linguist, or philosopher, you will enjoy the book because it provides an exciting computational perspective on the mystery of intelligence.
The Knowledge You Need This completely rewritten and updated edition of Artificial Intelligence reflects the revolutionary progress made since the previous edition was published.
Part I is about representing knowledge and about reasoning methods that make use of knowledge. The material covered includes the semantic-net family of representations, describe and match, generate and test, means-ends analysis, problem reduction, basic search, optimal search, adversarial search, rule chaining, the rete algorithm, frame inheritance, topological sorting, constraint propagation, logic, truth maintenance, planning, and cognitive modeling.
Part II is about learning, the sine qua non of intelligence. Some methods involve much reasoning; others just extract regularity from data. The material covered includes near-miss analysis, explanation-based learning, knowledge repair, case recording, version-space convergence, identification-tree construction, neural-net training, perceptron convergence, approximation-net construction, and simulated evolution.
Part III is about visual perception and language understanding. You learn not only about perception and language, but also about ideas that have been a major source of inspiration for people working in other subfields of artificial intelligence. The material covered includes object identification, stereo vision, shape from shading, a glimpse of modern linguistic theory, and transition-tree methods for building practical natural-language interfaces.
Special Features of this Edition - Based on extensive teaching experience
- Semiformal representation and procedure specifications bring the ideas to within a step or two of implementation and highlight unifying themes.
- Application examples provide a glimpse of the ideas at work in real-world systems.
- Powerful ideas and principles are identified and emphasized.
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