Artificial Intelligence
Structures and Strategies for Complex Problem Solving
856 pages, parution le 01/08/2001
(4eme édition)
Résumé
Artificial intelligence (AI) began as the quest to create
machines that could think for themselves and (perhaps)
out-think humans: the holy grail of computing! Over the
years, while still exploring the mechanisms that enable
thought, AI has evolved into a more pragmatic discipline.
AI uses different strategies to solve the complex practical
problems that present themselves wherever computing
technology is applied. Also, intelligence itself is now
known to be too complex to be described by any single
theory — instead, a constellation of theories
characterize the subject from different levels of
abstraction. At the lowest levels, neural networks, genetic
algorithms and other forms of computation aid understanding
of adaptation, perception, embodiment, and interaction with
the physical world. On a more abstract level, designers of
expert systems, intelligent agents, stochastic models, and
natural language understanding programs reflect the role of
knowledge and social processes in creating, transmitting
and sustaining knowledge. Further, logicians propose
deduction, abduction, induction, truth-maintenance, and
other models and modes for reasoning.
In this fourth edition, George Luger touches on all these levels of structures and strategies for complex problem solving, as well as conveying excitement for the study of intelligence itself. He shows how to use many different software tools and techniques for addressing the complex problems that challenge the modern computer scientist.
The trademark features of this best-selling text are:
- a thorough and balanced treatment of the foundations of AI
- the combination of theoretical foundations ofintelligent problem solving with the data structures and algorithms needed for implementation
- example programs written in LISP and PROLOG Putting practical applications of AI into context
- unique discussion of the social and philosophical issues of AI
In addition, this thoroughly updated fourth edition includes:
- the incorporation of more "agent-based" problem solving
- new material on reinforcement learning
- improved chapters on Bayesian-based inference techniques, including belief networks
- model-based reasoning and planning examples from the NASA space program
- updated material on Natural Language Processing
- comments on the AI endeavor from r perspectives of philosophy, psychology and neuro-physiology
Artificial Intelligence: Structures and Strategies for Complex Problem Solving is ideal for a one or two semester university course on AI, as well as an invaluable reference for researchers in the field or practitioners wishing to understand and employ the power of current AI techniques in their work.
ContentsI. ARTIFICIAL INTELLIGENCE: ITS ROOTS AND SCOPE.
1. AI: History and
Applications.
From Eden to ENIAC:
Attitudes toward intelligence, knowledge and human
artifice.
Overview of AI application areas.
Artificial intelligence—a summary.
Epilogue and references.
Exercises.
Overview of AI application areas.
Artificial intelligence—a summary.
Epilogue and references.
Exercises.
II. ARTIFICIAL INTELLIGENCE AS REPRESENTATION AND SEARCH.
2. The Predicate
Calculus.
3. Structures and Strategies for State Space Search.
4. Heuristic Search.
5. Control and Implementation of State Space Search.
Introduction.
The propositional calculus.
The predicate calculus.
Using inference rules to produce predicate calculus expressions.
Application: a logic-based financial advisor.
Epilogue and references.
Exercises.
The propositional calculus.
The predicate calculus.
Using inference rules to produce predicate calculus expressions.
Application: a logic-based financial advisor.
Epilogue and references.
Exercises.
3. Structures and Strategies for State Space Search.
Introduction.
Graph theory.
Strategies for state space search.
Using the state space to represent reasoning with the predicate calculus.
Epilogue and references.
Exercises.
Graph theory.
Strategies for state space search.
Using the state space to represent reasoning with the predicate calculus.
Epilogue and references.
Exercises.
4. Heuristic Search.
Introduction.
An algorithm for heuristic search.
Admissibility, monotonicity, and informedness.
Using heuristics in games.
Complexity issues.
Epilogue and references.
Exercises.
An algorithm for heuristic search.
Admissibility, monotonicity, and informedness.
Using heuristics in games.
Complexity issues.
Epilogue and references.
Exercises.
5. Control and Implementation of State Space Search.
Introduction.
Recursion-based search.
Pattern-directed search.
Production systems.
The blackboard architecturefor problem solving.
Epilogue and references.
Exercises.
Recursion-based search.
Pattern-directed search.
Production systems.
The blackboard architecturefor problem solving.
Epilogue and references.
Exercises.
III. REPRESENTATION AND INTELLIGENCE: THE AI CHALLENGE.
6 .Knowledge
Representation.
7. Strong Method Problem Solving.
8. Reasoning in Uncertain Situations.
Issues in knowledge
representation.
A brief history of AI representational systems.
Conceptual graphs: a network language.
Alternatives to explicit representation.
Agent based and distributed problem solving.
Epilogue and references.
Exercises.
A brief history of AI representational systems.
Conceptual graphs: a network language.
Alternatives to explicit representation.
Agent based and distributed problem solving.
Epilogue and references.
Exercises.
7. Strong Method Problem Solving.
Introduction.
Overview of expert systems technology.
Rule-based expert systems.
Model-based, case based, and hybrid systems.
Planning.
Epilogue and references.
Exercises.
Overview of expert systems technology.
Rule-based expert systems.
Model-based, case based, and hybrid systems.
Planning.
Epilogue and references.
Exercises.
8. Reasoning in Uncertain Situations.
Introduction.
Logic-based abductive inference.
Abduction: alternatives to logic.
The stochastic approach to uncertainty.
Epilogue and references.
Exercises.
Logic-based abductive inference.
Abduction: alternatives to logic.
The stochastic approach to uncertainty.
Epilogue and references.
Exercises.
IV. MACHINE LEARNING.
9. Machine Learning:
Symbol-Based.
10. Machine Learning: Connectionist.
11. Machine Learning: Social and Emergent.
Introduction.
A framework for symbol-based learning.
Version space search.
The ID3 decision tree induction algorithm.
Inductive bias and learnability.
Knowledge and learning.
Unsupervised learning.
Reinforcement learning.
Epilogue and references.
Exercises.
A framework for symbol-based learning.
Version space search.
The ID3 decision tree induction algorithm.
Inductive bias and learnability.
Knowledge and learning.
Unsupervised learning.
Reinforcement learning.
Epilogue and references.
Exercises.
10. Machine Learning: Connectionist.
Introduction.
Foundations for connectionist networks.
Perceptron learning.
Backpropagation learning.
Competitive learning.
Hebbian coincidence learning.
Attractor networks or “Memories.”
Epilogue and references.
Exercises.
Foundations for connectionist networks.
Perceptron learning.
Backpropagation learning.
Competitive learning.
Hebbian coincidence learning.
Attractor networks or “Memories.”
Epilogue and references.
Exercises.
11. Machine Learning: Social and Emergent.
Social and emergent models
of learning.
The genetic algorithm.
Classifier systems and genetic programming.
Artificial life and society-based learning.
Epilogue and references.
Exercises.
The genetic algorithm.
Classifier systems and genetic programming.
Artificial life and society-based learning.
Epilogue and references.
Exercises.
V. ADVANCED TOPICS FOR AI PROBLEM SOLVING.
12. Automated
Reasoning.
13. Understanding Natural Language.
Introduction to weak
methods in theorem proving.
The general problem solver and difference tables.
Resolution theorem proving.
PROLOG and automated reasoning.
Further issues in automated reasoning.
Epilogue and references.
Exercises.
The general problem solver and difference tables.
Resolution theorem proving.
PROLOG and automated reasoning.
Further issues in automated reasoning.
Epilogue and references.
Exercises.
13. Understanding Natural Language.
Role of knowledge in
language understanding.
Deconstructing language: a symbolic analysis.
Syntax.
Syntax and knowledge with ATN parsers.
Stochastic tools for language analysis.
Natural language applications.
Epilogue and references.
Exercises.
Deconstructing language: a symbolic analysis.
Syntax.
Syntax and knowledge with ATN parsers.
Stochastic tools for language analysis.
Natural language applications.
Epilogue and references.
Exercises.
VI. LANGUAGES AND PROGRAMMING TECHNIQUES FOR ARTIFICIAL INTELLIGENCE.
14. An Introduction to
PROLOG.
15. An Introduction to LISP.
Introduction.
Syntax for predicate calculus programming.
Abstract data types (ADTs) in PROLOG.
A production system example in PROLOG.
Designing alternative search strategies.
A PROLOG planner.
PROLOG: meta-predicates, types, and unification.
Meta-interpreters in PROLOG.
Learning algorithms in PROLOG.
Natural language processing in PROLOG.
Epilogue and references.
Exercises.
Syntax for predicate calculus programming.
Abstract data types (ADTs) in PROLOG.
A production system example in PROLOG.
Designing alternative search strategies.
A PROLOG planner.
PROLOG: meta-predicates, types, and unification.
Meta-interpreters in PROLOG.
Learning algorithms in PROLOG.
Natural language processing in PROLOG.
Epilogue and references.
Exercises.
15. An Introduction to LISP.
Introduction.
LISP: a brief overview.
Search in LISP: a functional approach to the farmer, wolf, goat, and cabbage problem.
Higher-order functions and procedural abstraction.
Search strategies in LISP.
Pattern matching in LISP.
A recursive unification function.
Interpreters and embedded languages.
Logic programming in LISP.
Streams and delayed evaluation.
An expert system shell in LISP.
Semantic networks and inheritance in LISP.
Object-oriented programming using CLOS.
Learning in LISP: the ID3 algorithm.
Epilogue and references.
Exercises.
LISP: a brief overview.
Search in LISP: a functional approach to the farmer, wolf, goat, and cabbage problem.
Higher-order functions and procedural abstraction.
Search strategies in LISP.
Pattern matching in LISP.
A recursive unification function.
Interpreters and embedded languages.
Logic programming in LISP.
Streams and delayed evaluation.
An expert system shell in LISP.
Semantic networks and inheritance in LISP.
Object-oriented programming using CLOS.
Learning in LISP: the ID3 algorithm.
Epilogue and references.
Exercises.
VII. EPILOGUE.
16. Artificial
Intelligence as Empirical Enquiry.
Bibliography.
Author Index.
Subject Index.
Introduction.
Artificial intelligence: a revised definition.
The science of intelligent systems.
AI: current issues and future directions.
Epilogue and references.
Artificial intelligence: a revised definition.
The science of intelligent systems.
AI: current issues and future directions.
Epilogue and references.
Bibliography.
Author Index.
Subject Index.
L'auteur - George F. Luger
George Luger
is currently a Professor of Computer Science and Psychology at the University of New Mexico. His research interests include modeling human intelligence and building intelligent control systems. He received his PhD at the University of Pennsylvania and has worked as a research fellow at the University of Edinburgh.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Addison Wesley |
| Auteur(s) | George F. Luger |
| Parution | 01/08/2001 |
| Édition | 4eme édition |
| Nb. de pages | 856 |
| Format | 19 x 24 |
| Couverture | Relié |
| Poids | 1550g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780201648669 |
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