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Artificial intelligence : structures and strategies for complex problem solving

Author: George F Luger; William A Stubblefield
Publisher: Redwood City, Calif. : Benjamin/Cummings Pub. Co., ©1993.
Edition/Format:   Print book : English : 2nd edView all editions and formats
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Additional Physical Format: Online version:
Luger, George F.
Artificial intelligence.
Redwood City, Calif. : Benjamin/Cummings Pub. Co., ©1993
(OCoLC)594530686
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: George F Luger; William A Stubblefield
ISBN: 0805347801 9780805347807
OCLC Number: 26305570
Description: xxiv, 740 pages : illustrations ; 24 cm
Contents: Pt. I. Artificial Intelligence: Its Roots and Scope --
Artificial Intelligence --
An Attempted Definition. 1. AI: History and Applications. 1.1. From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artifice. 1.2. Overview of AI Application Areas. 1.3. Artificial Intelligence --
A Summary --
Pt. II. Artificial Intelligence as Representation and Search --
Knowledge Representation --
Problem Solving as Search. 2. The Predicate Calculus. 2.1. The Propositional Calculus. 2.2. The Predicate Calculus. 2.3. Using Inference Rules to Produce Predicate Calculus Expressions. 2.4. Application: A Logic-Based Financial Advisor. 3. Structures and Strategies for State Space Search. 3.1. Graph Theory. 3.2. Strategies for State Space Search. 3.3. Using the State Space to Represent Reasoning with the Predicate Calculus. 4. Heuristic Search. 4.1. An Algorithm for Heuristic Search. 4.2. Admissibility, Monotonicity, and Informedness. 4.3. Using Heuristics in Games. 4.4. Complexity Issues. 5. Control and Implementation of State Space Search. 5.1. Recursion-Based Search. 5.2. Pattern-Directed Search. 5.3. Production Systems. 5.4. Predicate Calculus and Planning. 5.5. The Blackboard Architecture for Problem Solving --
Pt. III. Languages for AI Problem Solving --
Languages, Understanding, and Levels of Abstraction --
Requirements for AI Languages --
The Primary AI Languages: LISP and PROLOG --
Selecting an Implementation Language. 6. An Introduction to PROLOG. 6.1. Syntax for Predicate Calculus Programming. 6.2. Abstract Data Types (ADTs) in PROLOG. 6.3. A Production System Example in PROLOG. 6.4. Designing Alternative Search Strategies. 6.5. A PROLOG Planner. 6.6. PROLOG: Toward a Nonprocedural Computing Language. 7. LISP. 7.1. LISP: A Brief Overview. 7.2. Search Algorithms in LISP: A Functional Approach to the Farmer, Wolf, Goat, and Cabbage Problem. 7.3. Higher-Order Functions and Procedural Abstraction. 7.4. Search Strategies in LISP. 7.5. Pattern Matching in LISP. 7.6. A Recursive Unification Function. 7.7. Interpreters and Embedded Languages --
Pt. IV. Representations for Knowledge-Based Systems. 8. Rule-Based Expert Systems. 8.1. Overview of Expert Systems Technology. 8.2. A Framework for Organizing and Applying Human Knowledge. 8.3. Managing Uncertainty in Expert Systems. 8.4. MYCIN: A Case Study. 9. Knowledge Representation. 9.0. Knoewledge Reppresentation Languages. 9.1. Issues in Knowledge Representation. 9.2. A Survey of Network Representations. 9.3. Conceptual Graphs: A Network Representation Language. 9.4. Structured Representations. 9.5. Type Hierarchies, Inheritance, and Exception Handling. 9.6. Further Problems in Knowledge Representation. 10. Natural Language. 10.0. Role of Knowledge in Language Understanding. 10.1. The Natural Language Problem. 10.2. Syntax. 10.3. Combining Syntax and Semantics in ATN Parsers. 10.4. Natural Language Applications. 11. Automated Reasoning. 11.0. Introduction to Weak Methods in Theorem Proving. 11.1. The General Problem Solver and Difference Tables. 11.2. Resolution Theorem Proving. 11.3. Further Issues in the Design of Automated Reasoning Programs. 12. Machine Learning. 12.1. A Framework for Learning. 12.2. Version Space Search. 12.3. The ID3 Decision Tree Induction Algorithm. 12.4. Inductive Bias and Learnability. 12.5. Knowledge and Learning. 12.6. Unsupervised Learning. 12.7. Parallel Distributed Processing. 12.8. Genetic Algorithms --
Pt. V. Advanced AI Programming Techniques --
AI Languages and Meta-Interpreters --
Object-Oriented Programming --
Hybrid Environments --
A Hybrid Example. 13. Advanced Representation in PROLOG. 13.1. PROLOG Tools: Meta-Predicates, Types, and Unification. 13.2. Meta-Interpreters in PROLOG. 13.3. Natural Language Understanding in PROLOG. 13.4. Version Space Search in PROLOG. 13.5. Explanation-Based Learning in PROLOG. 13.6. PROLOG and Programming with Logic. 14. Advanced LISP Programming Techniques for Artificial Intelligence. 14.0. Introduction: Abstraction and Complexity. 14.1. Logic Programming in LISP. 14.2. Streams and Delayed Evaluation. 14.3. An Expert System Shell in LISP. 14.4. Network Representations and Inheritance. 14.5. The ID3 Induction Algorithm. 15. Objects, Messages, and Hybrid Expert System Design. 15.1. Object-Oriented Knowledge Representation. 15.2. LISP and Object-Oriented Programming. 15.3. The Common LISP Object System. 15.4. Object-Oriented Programming and Concurrency in PROLOG. 15.5. Hybrid Expert System Tools --
Pt. VI. Epilogue. 16. Artificial Intelligence as Empirical Enquiry. 16.1. Artificial Intelligence: A Revised Definition. 16.2. Cognitive Science: An Overview. 16.3. Representational Models for Intelligence: Issues and Directions.
Responsibility: George F. Luger, William A Stubblefield.

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