Artificial intelligence; a new synthesis / (Record no. 3539)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 09359nam a2200145 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9788181471901)pb) |
| 040 ## - CATALOGING SOURCE | |
| Transcribing agency | CUS |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 006.3 |
| Item number | NIL/A |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nils j. Nilsson |
| 245 ## - TITLE STATEMENT | |
| Title | Artificial intelligence; a new synthesis / |
| Statement of responsibility, etc. | NilsJ, Nilsson |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc. | Burlington: |
| Name of publisher, distributor, etc. | Morgan Kaufmann , |
| Date of publication, distribution, etc. | 1998. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xxi, 513 p. |
| Other physical details | ill. ; |
| 505 ## - FORMATTED CONTENTS NOTE | |
| Formatted contents note | <br/>Introduction<br/>1.1 What Is AI?<br/>1.2 Approaches to Artificial Intelligence<br/>1.5 Brief History of AI<br/>1.4 Plan of the Book<br/>1.5 Additional Readings and Discussion<br/>Exercises<br/>Reactive Machines<br/>Stimulus-Response Agents<br/>2.1 Perception and Action<br/>2.1.1 Perception<br/>2.1.2 Action<br/>2.1.3 Boolean Algebra<br/>2.1.4 Classes and Forms of Boolean Functions<br/>2.2 Representing and Implementing Action Functions<br/>2.2.1 Production Systems<br/>2.2.2 Networks<br/>2.2.3 The Subsumption Architecture<br/>2.3 Additional Readings and Discussion<br/>Exercises<br/>3 ^ Neural Networks<br/>5.1 Introduction<br/>3.2 Training Single TLUs<br/>3.2.1 TLU Geometry<br/>3.2.2 Augmented Vectors<br/>3.2.3 Gradient Descent Methods<br/>3.2.4 The Widrow-Hoff Procedure<br/>3.2.5 The Generalized Delta Procedure<br/>3.2.6 The Error-Correction Procedure<br/>3.3 Neural Networks<br/>3.3.1 Motivation<br/>3.3.2 Notation<br/>33.3 The Backpropagation Method<br/>3.3.4 Computing Weight Changes in the Final Layer<br/>3.3.5 Computing Changes to the Weights in Intermediate Layers<br/>3.4 Generalization, Accuracy, and Overfitting<br/>3.5 Additional Readings and Discussion<br/>Exercises<br/>Machine Evolution<br/>4.1 Evolutionary Computation<br/>4.2 Genetic Programming<br/>4.2.1 Program Representation in GP<br/>Contents<br/>4.2.2 The GP Process<br/>4.2.5 Evolving a Wall-Following Robot<br/>4.3 Additional Readings and Discussion<br/>Exercises<br/>BFp^H State Machines<br/>5.1 Representing the Environment by Feature Vectors<br/>5.2 Elman Networks<br/>5.3 Iconic Representations<br/>5.4 Blackboard Systems<br/>5.5 Additional Readings and Discussion<br/>Exercises<br/>Robot Vision<br/>6.1 Introduction<br/>6.2 Steering an Automobile<br/>6.3 Two Stages of Robot Vision<br/>6.4 Image Processing<br/>6.4.1 Averaging<br/>6.4.2 Edge Enhancement<br/>6.4.3 Combining Edge Enhancement with Averaging<br/>6.4.4 Region Finding<br/>6.4.5 Using Image Attributes Other Than Intensity<br/>6.5 Scene Analysis<br/>6.5.1 Interpreting Lines and Curves in the Image<br/>6.5.2 Model-Based Vision<br/>6.6 Stereo Vision and Depth Information<br/>6.7 Additional Readings and Discussion<br/>Exercises<br/>w<br/>Contents<br/>II Search in State Spaces<br/>7L :• Agents That Plan<br/>7.1 Memory Versus Computation<br/>7.2 State-Space Graphs<br/>7.3 Searching Explicit State Spaces<br/>7.4 Feature-Based State Spaces<br/>7.5 Graph Notation<br/>7.6 Additional Readings and Discussion<br/>Exercises<br/>Uninformed Search<br/>8.1 Formulating the State Space<br/>8.2 Components of Implicit State-Space Graphs<br/>8.3 Breadth-First Search<br/>8.4 Depth-First or Backtracking Search<br/>8.5 Iterative Deepening<br/>8.6 Additional Readings and Discussion<br/>Exercises<br/>^ 9 J Heuristic Search<br/>9.1 Using Evaluation Functions<br/>9.2 A General Graph-Searching Algorithm<br/>9.2.1 Algorithm A*<br/>9.2.2 Admissibility of A*<br/>9.2.5 The Consistency (or Monotone) Condition<br/>9.2.4 Iterative-Deepening A*<br/>9.2.5 Recursive Best-First Search<br/>Ff7<iVT.l<br/>Contents<br/>9.5 Heuristic Functions and Search Efficiency<br/>9.4 Additionad Readings and Discussion<br/>Exercises<br/>Planning, Acting, and Learning<br/>10.1 The Sense/Plan/Act Cycle<br/>10.2 Approximate Search<br/>10.2.1 Island-Driven Search<br/>10.2.2 Hierarchical Search<br/>10.2.5 Limited-Horizon Search<br/>10.2.4 Cycles<br/>10.2.5 Building Reactive Procedures<br/>10.3 Learning Heuristic Functions<br/>10.3.1 Explicit Graphs<br/>10.3.2 Implicit Graphs<br/>10.4 Rewards Instead of Goals<br/>10.5 Additional Readings and Discussion<br/>Exercises<br/>Alternative Search Formulations and<br/>Applications<br/>11.1 Assignment Problems<br/>11.2 Constructive Methods<br/>11.3 Heuristic Repair<br/>11.4 Function Optimization<br/>Exercises<br/>j|2 Adversarial Search<br/>12.1 Two-Agent Games<br/>12.2 The Minimax Procedure<br/>xii Contents<br/>12.5 The Alpha-Beta Procedure<br/>12.4 The Search Efficiency of the Alpha-Beta Procedure<br/>12.5 Other Important Matters<br/>12.6 Games of Chance<br/>12.7 Learning Evaluation Functions<br/>12.8 Additional Readings and Discussion<br/>Exercises<br/>III Knowledge Representation and<br/>Reasoning 21;<br/>The Propositional Calculus<br/>13.1 Using Constraints on Feature Values<br/>13.2 The Language<br/>13.3 Rules of Inference<br/>13.4 Definition of Proof<br/>13.5 Semantics<br/>13.5.1 Interpretations<br/>13.5.2 The Propositional Truth Table<br/>13.5.3 Satisfiability and Models<br/>13.5.4 Validity<br/>13.5.5 Equivalence<br/>13.5.6 Entailment<br/>13.6 Soundness and Completeness<br/>13.7 The PSAT Problem<br/>13.8 Other Important Topics<br/>13.8.1 Language Distinctions<br/>13.8.2 Metatheorems<br/>13.8.3 Associative Laws<br/>13.8.4 Distributive Laws<br/>Exercises<br/>I Resolution in the Propositional Calculns<br/>14.1 A New Rule of Inference: Resolution<br/>14.1.1 Clauses as wffs<br/>14.1.2 Resolution on Clauses<br/>14.1.5 Soundness of Resolution<br/>- 14.2 Converting Arbitrary wffs to Conjunctions of Clauses<br/>14.5 Resolution Refutations<br/>14.4 Resolution Refutation Search Strategies<br/>14.4.1 Ordering Strategies<br/>14.4.2 Refinement Strategies<br/>14.5 Horn Clauses<br/>Exercises<br/>The Predicate Calculus<br/>15.1 Motivation<br/>15.2 The Language and Its Syntax<br/>15.5 Semantics<br/>15.3.1 Worlds<br/>15.3.2 Interpretations<br/>15.3.3 Models and Related Notions<br/>15.3.4 Knowledge<br/>15.4 Quantification<br/>15.5 Semantics of Quantifiers<br/>15.5.1 Universal Quantifiers<br/>15.5.2 Existential Quantifiers<br/>15.5.3 Useful Equivalences<br/>15.5.4 Rules of Inference<br/>15.6 Predicate Calculus as a Language for Representing<br/>Knowledge<br/>15.6.1 Conceptualizations<br/>15.6.2 Examples<br/>15.7 Additional Readings and Discussion<br/>Exercises<br/>Resolution in the Predicate Calculus<br/>16.1 Unification<br/>16J2 Predicate-Calculus Resolution<br/>16.5 Completeness and Soundness<br/>16.4 Converting Arbitrary wffs to Clause Form<br/>16.5 Using Resolution to Prove Theorems<br/>16.6 Answer Extraction<br/>16.7 Hie Equality Predicate<br/>16.8 Additional Readings and Discussion<br/>Exercises<br/>Knowledge-Based Systems<br/>17.1 Confronting the Real World<br/>17.2 Reasoning Using Horn Clauses<br/>17.3 Maintenance in Dynamic Knowledge Bases<br/>V7A Rule-Based Expert Systems<br/>17.5 Rule Learning<br/>17.5.1 Learning Propositional Calculus Rules<br/>17.5.2 Learning First-Order Logic Rules<br/>17.5.3 Explanation-Based Generalization<br/>17.6 Additional Readings and Discussion<br/>Exercises<br/>' J Representing Commonsense Knowledge<br/>18.1 The Commonsense World<br/>18.1.1 What Is Commonsense Knowledge?<br/>18.1.2 Difficulties in Representing Commonsense Knowledge<br/>18.1.3 The Importance of Commonsense Knowledge<br/>18.1.4 Research Areas<br/>18.2 Time<br/>18.5 Knowledge Representation by Networks<br/>18.5.1 Taxonomic Knowledge<br/>18.3.2 Semantic Networks<br/>18.3.3 Nonmonotonic Reasoning in Semantic Networks<br/>18.3.4 Frames<br/>18.4 Additional Readings and Discussion<br/>Exercises<br/>Reasoning with Uncertain Information<br/>19.1 Review of Probability Theory<br/>19.1.1 Fundamental Ideas<br/>19.1.2 Conditional Probabilities<br/>19.2 Probabilistic Inference<br/>19.2.1 A General Method<br/>19.2.2 Conditional Independence<br/>19.5 Bayes Networks<br/>19.4 Patterns of Inference in Bayes Networks<br/>19.5 Uncertain Evidence<br/>19.6 D-Separation<br/>19.7 Probabilistic Inference in Polytrees<br/>19.7.1 Evidence Above<br/>19.7.2 Evidence Below<br/>19.7.3 Evidence Above and Below<br/>19.7.4 A Numerical Example<br/>19.8 Additional Readings and Discussion<br/>Exercises<br/>lOlg] Learning and Acting with Bayes Nets<br/>20.1 Learning Bayes Nets<br/>20.1.1 Known Network Structure<br/>20.1.2 Learning Network Structure<br/>20.2 Probabilistic Inference and Action<br/>20.2.1 The General Setting<br/>20.2.2 An Extended Example<br/>20.2.3 Generalizing the Example<br/>20.3 Additional Readings and Discussion<br/>Exercises<br/>IV Planning Methods Based on<br/>Logic<br/>21 The Situation Calculus<br/>21.1 Reasoning about States and Actions<br/>21.2 Some Difficulties<br/>21.2.1 Frame Axioms<br/>21.2.2 Qualifications<br/>21.2.3 Ramifications<br/>21.3 Generating Plans<br/>21.4 Additional Readings and Discussion<br/>Exercises<br/>H-''. 'iZZ ' 'I Planning<br/>22.1 STRIPS Planning Systems<br/>22.1.1 Describing States and Goals<br/>22.1.2 Forward Search Methods<br/>22.1.3 Recursive STRIPS<br/>22.1.4 Plans with Run-Time Conditionals<br/>22.1.5 The Sussman Anomaly<br/>22.1.6 Backward Search Methods<br/>22.2 Plan Spaces and Partial-Order Planning<br/>22.3 Hierarchical Planning<br/>22.3.1 ABSTRIPS<br/>22.3.2 Combining Hierarchical and Partial-Order Planning<br/>22.4 Learning Plans<br/>22.5 Additional Readings and Discussion<br/>Exercises<br/>V Communication and Integration<br/>Multiple Agents<br/>23.1 Interacting Agents<br/>23.2 Models of Other Agents<br/>23.2.1 Varieties of Models<br/>23.2.2 Simulation Strategies<br/>23.2.3 Simulated Databases<br/>23.2.4 The Intentional Stance<br/>23.3 A Modal Logic of Knowledge<br/>23.3.1 Modal Operators<br/>23.3.2 Knowledge Axioms<br/>23.5.3 Reasoning about Other Agents' Knowledge<br/>23.5.4 Predicting Actions of Other Agents<br/>25.4 Additional Readings and Discussion<br/>Exercises<br/>Communication among Agents<br/>24.1 Speech Acts<br/>24.1.1 Planning Speech Acts<br/>24.1.2 Implementing Speech Acts<br/>24.2 Understanding Language Strings<br/>24.2.1 Phrase-Structure Grammars<br/>24.2.2 Semantic Analysis<br/>24.2.5 Expanding the Grammar<br/>24.3 Efficient Communication<br/>24.3.1 Use of Context<br/>24.3.2 Use of Knowledge to Resolve Ambiguities<br/>24.4 Natural Language Processing<br/>24.5 Additional Readings and Discussion<br/>Exercises<br/>Agent Architectures<br/>25.1 Three-Level Architectures<br/>25.2 Goal Arbitration<br/>25.5 The Triple-Tower Architecture<br/>25.4 Bootstrapping<br/>25.5 Additional Readings and Discussion<br/>Exercises |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | GN Books |
| Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Full call number | Accession number | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Central Library, Sikkim University | Central Library, Sikkim University | General Book Section | 24/06/2016 | 006.3 NIL/A | P18904 | 24/06/2016 | General Books |