The,HandbookOta _^#i#s_r^i^i

EditorsAvron barr and Edward a. Feigenbaum, Volumes I and II

PAUL R. COHEN and EDWARD A. FEIGENBAUM, Volume 111Stanford University

Publication DateVolume I, Summer 1981

Volumes II and 111, Summer 1982

A pioneering work that brings together and explains indetailed, yet comprehensible language, the results of

more than 25 years of research and development in thenew art and science of Artificial Intelligence.

". . .1 rate this book as the mostimportant . . . have enjoyed the

book thoroughly and urge you tostop what you're doing and sit

down to read it ... I found thebook well organized, written, and

edited . . . the first volume ofThe Handbook told me everything I

ever wanted to know about thetopics it covered. It cleared up

some misconceptions I had,extended the breadth and depth ofmy AI knowledge, and packaged a

big meal into a palatable anddigestible portion. Try it, you'll

like it."—Frederick Hayes-Roth

from a forthcoming reviewin the AI Journal

". . . an important contributionto the literature of artificial intelli-gence ... it is to the book's creditthat these articles have been editedand organized to present thematerial clearly and cohesively, sothat laymen and experts alike canmake sense of it . . ."

—Computerworld

". . . extremely well written, up todate, concise articles covering awide spectrum of topics germaneto the current status, potentialand perils of A.1., or more broadly,computer science . . . highlyreadable accounts . . . for profes-sional and layman alike . . ."

—Book Trends Monthly(Complete contents follow; order form, page 4)

The Handbook of Artificial Intelligence

What is a heuristic problem-solving program!

PHow do computers understand English?What are semantic nets and frames!Can computer programs outperform human experts?

Such questions are asked by scientists, engineers,physicians, students, and hobbyists when they first"discover" Artificial Intelligence, a dynamic branch ofcomputer science research on programs that exhibithuman-like intelligence. In the 19705, discoveries in AIresulted in a variety of novel projects: chess-playing andother home-entertainment programs, natural language"front-ends"for military and business systems, and, mostpromising, "intelligent assistants" in a number of fields.As the cost of computing continuestofall, newcomputerapplications will become viable, and they will inevitablybe served by symbolic models and inference techniquesdeveloped by AI researchers. Yet those who understandsymbolic computation have been speaking largely tothemselves for 25 years. The Handbook of ArtificialIntelligence will satisfy the urgent need for AI to "gopublic," making the full scopeof its important techniquesand concepts available for the first time to the rapidlyexpanding world of computer technologists and users.

The Handbook was conceived in 1975 by ProfessorEdward A. Feigenbaum as a compendium of knowledgeof AI and its applications. In the ensuing years, studentsand AI researchers at Stanford's Department of Com-puter Science, a major center for AI research, and atuniversities and laboratories across the nation have con-tributed to the project. The scope of the work is broad:About 200 short articles cover most of the importantideas, techniques, and systems developedduring 25 yearsof research in AI.

Overview articles in each chapter describe the basicissues, alternative approaches, and unsolved problemsthat characterize areas of AI; they are the best criticaldiscussions anywhere of activity in the field. These, aswell as the more technical articles, arewrittenfor readerswith no prior familiaritywith AI. The articles have beencarefully edited to remove confusing and unessentialjargon,key concepts are introduced with thorough ex-planations, (usually in the overview articles), and thethree volumes are completely indexed and cross-referenced to make it clear how the importantideas of AIrelate to each other. Theeditors have giventhe volumes ahierarchical organizationso that readers can choose how

deeply into the detail of each chapter they wish topenetrate. Thus, the Handbook is a reference work, atextbook, a guide to programming techniques and to theextensive literature of the field, and a book for intellec-tual browsing.

The Handbook is valuable to newcomers to AI, as wellas to members of the AI community. Since AI is a youngand rapidly developing field, itsresearch literature is notaccessible or intelligible to outsiders, or even to research-ers in different areas of AI. Few, if any, researchers knowthe literature of machine vision, automatic pro-gramming, natural-language understanding, and in-telligent computer-assisted instruction, yet these and adozen other major subdivisions of AI are represented inthe three volumes of the Handbook. This scope reflectsthe broad range of expertiseof more than 100researchersfrom universities and research institutions all over theworld who have contributed andreviewed articles for theHandbook. Simply by merit of its scope, the Handbooksurpasses all other texts on AI, but, in addition, itsorganization and its readability promise to make it thestandard reference work of the field of AI.

The Handbook comprises approximately 1,500pages.Volume I appeared in June 1981, and Volumes II and 111will be published in Summer 1982by William Kaufmann,Inc., in cooperation with HeurisTech Press, a Stanford-area press specializing in AI publications.

The Handbook of Artificial Intelligence will be revisedcontinually as the field of AI grows and changes. Com-ments on the first volume have been highly favorable.Those who have used it predict that the Handbook willbecome the standard reference work of therapidly grow-ing AI field.

Edward A. Feigenbaum is director of the Heuristic Pro-grammingProject, a major centerfor AI research in Stan-ford University's internationally renowned Departmentof Computer Science. He was coeditor of the first majorcompendium of AI research, the famous Computers andThought. Avron Barr and Paul R. Cohen are graduatestudents in Computer Science and Psychology at Stan-ford and have served for several years as managingeditors of the Handbook project.

CONTENTS

VOLUME IIntroductionA. What is Artificial Intelligence?B. The AI HandbookC. Accessing the AI literature

SearchA. OverviewB. Problem representation

1. State-space representation2. Problem-reductionrepresentation3. Game trees

C. Search methods1. Blind state-space search2. Blind AND/OR graph search3. Heuristic state-space search

a. Basic concepts in heuristic searchb. A* —Optimal search for an optimal solutionc. Relaxing the optimality requirementd. Bidirectional search

4. Heuristic search of an AND/OR graph5. Game tree search

a. Minimax procedureb. Alpha-betapruningc. Heuristics in game tree search

D. Sample search programs1. Logic Theorist2. General ProblemSolver3. Gelernter's geometry theorem-provingmachine4. Symbolic integration programs5. STRIPS6. ABSTRIPS

. Knowledge RepresentationA. OverviewB. Survey of representation techniquesC. Representationschemes

1. Logic2. Procedural representations3. Semantic networks4. Production systems5. Direct (analogical) representations6. Semantic primitives7. Frames and scripts

. Understanding NaturalLanguageA. OverviewB. Machine translationC. Grammars

1. Review of formal grammars2. Transformational grammars3. Systemic grammar4. Case grammars

D. Parsing1. Overview of parsing techniques2. Augmented transition networks3. The General SyntacticProcessor

E. Text generationF. Natural language processing systems

1. Early natural language systems2. Wilks's machine translation system3. LUNAR4. SHRDLU5. MARGIE6. SAM and PAM7. LIFER

V. Understanding SpokenLanguageA. OverviewB. Speech systems architectureC. The ARPA SUR projects

1. HEARSAY2. HARPY3. HWIM4. The SRI/SDC speech systems

VOLUME IIVI. ProgrammingLanguages for AI Research

A. OverviewB. LISPC. Features of AI programming languages

1. Overview2. Data structures3. Control structures4. Pattern matching5. Programming environment

D. Dependencies and assumptionsVII. Applications-orientedAI Research: Science

A. OverviewB. TEIRESIASC. Applications in chemistry

1. Chemical analysis2. The DENDRAL programs

a. Heuristic DENDRALb. CONGEN and its extensionsc. Meta-DENDRAL

3. CRYSALIS4. Applications in organic synthesis

D. Other scientific applications1. MACSYMA2. The SRI Computer-based Consultant3. PROSPECTOR4. AI in database management

VIII. Applications-orientedAI Research: MedicineA. OverviewB. Medical systems

1. MYCIN2. CASNET3. INTERNIST4. Present Illness Program5. Digitalis Therapy Advisor6. IRIS7. EXPERT

IX. Applications-oriented AI Research: EducationA. OverviewB. ICAI systems designC. Intelligent CAI systems

1. SCHOLAR2. WHY3. SOPHIE4. WEST5. WUMPUS6. GUIDON7. BUGGY8. EXCHECK

D. Other applications of AI to education

X. Automatic ProgrammingA. OverviewB. Methods of program specification

C. Basic approachesD. Automaticprogramming systems

1. PSI and CHI2. SAFE3. Programmer'sApprentice4. PECOS5. DEDALUS6. Protosystem I7. NLPQ8. LIBRA

VOLUME 111XI. Models of Cognition

A. OverviewB. General Problem SolverC. Opportunistic problem solvingD. EPAME. Semantic-network models of memory

1. Quillian's semanticmemory system2. HAM3. ACT4. MEMOD

F. Belief systemsXII. Automatic Deduction

A. OverviewB. Resolution-based theoremprovingC. Nonresolution theoremprovingD. Boyer-MooreTheorem ProverE. Nonmonotonic logicsF. Logic programming

XIII. VisionA. OverviewB. Blocks-world understanding

1. Roberts2. Guzman3. Falk4. Huffman-Clowes5. Waltz6. Shirai7. Mackworth8. Kanade

C. Early processing of visual data1. Visual input2. Color3. Preprocessing4. Edge detection and line finding5. Regionanalysis6. Texture

D. Representationof scene characteristics1. Intrinsic images2. Motion3. Stereo vision4. Rangefinders5. Shape-from methods6. Three-dimensional shape description and recognition

E. Algorithmsfor vision1. Pyramids and quad trees2. Template matching3. Linguistic methods for computer vision4. Relaxation algorithms

F. Devices and systems1. Robotic vision2. Organizationand control of vision systems3. ACRONYM

XIV. Learning and Inductive InferenceA. OverviewB. Rote learning

1. Issues2. Rote learning in Samuel's checkers player

C. Learning by taking advice1. Issues2. Mostow'soperationalizer

D. Learning from examples1. Issues2. Learning in control and pattern-recognition systems3. Learning singleconcepts

a. Version spaceb. Data-drivenknowledge-space operatorsc. Generating and testing plausiblehypothesesd. Schema instantiation

4. Learning multiple conceptsa. AQIIb. Meta-DENDRALc. AM

5. Learning to perform multiple-step tasksa. Samuel's checkers playerb. Waterman's poker playerc. HACKERd. LEXc. Grammatical inference

XV. Planningand Problem SolvingA. OverviewB. STRIPS and ABSTRIPSC. Nonhierarchical plannersD. Hierarchical planners

1. NOAH2. MOLGEN

E. Refinement of skeletal plans

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