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Artificial intelligence : foundations of computational agents
Title
Artificial intelligence : foundations of computational agents / David L. Poole, Alan K. Mackworth.
ISBN
9780521519007 (hardback)
0521519004 (hardback)
Published
New York : Cambridge University Press, 2010.
Physical Description
xvii, 662 p. : ill. ; 26 cm.
Summary
"Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving"--Provided by publisher.
Format
Books
Language
English
Added to Catalog
April 26, 2012
Bibliography
Includes bibliographical references and index.
Contents
Part I. Agents in the World: What Are Agents and How Can They Be Built?: 1. Artificial intelligence and agents.
What is Artificial Intelligence?
A brief history of AI
Agents situated in environments
Knowledge representation
Dimensions of complexity
Prototypical applications
Overview of the book
Review
References and further reading
Exercises
2. Agent architectures and hierarchical control.
Agents
Agent systems
Hierarchical control
Embedded and simulated agents
Acting with reasoning
Review
References and further reading
Exercises
Part II. Representing and Reasoning:
3. States and searching.
Problem solving as search
State spaces
Graph searching
A generic searching algorithm
Uninformed search strategies
Heuristic search
More sophisticated search
Review
References and further reading
Exercises
4. Features and constraints:
Features and states
Possible worlds, variables, and constraints
Generate-and-test algorithms
Solving CSPs using Search
Consistency algorithms
Domain splitting
Variable elimination
Local search
Population-based methods
Optimization
Review
References and further reading
Exercises
5. Propositions and inference.
Propositions
Propositional definite clauses
Knowledge representation issues
Proving by contradictions
Complete knowledge assumption
Abduction
Causal models
Review
References and further reading
Exercises
6. Reasoning under uncertainty.
Probability
Independence
Belief networks
Probabilistic inference
Probability and time
Review
References and further reading
Exercises
Part III. Learning and Planning:
7. Learning: Overview and supervised learning.
Learning issues
Supervised learning
Basic models for supervised learning
Composite models
Avoiding overfitting
Case-based reasoning
Learning as refining the hypothesis space
Bayesian learning
Review
References and further reading
Exercises
8. Planning with certainty.
Representing states, actions, and goals
Forward planning
Regression planning
Planning as a CSP
Partial-order planning
Review
References and further reading
Exercises
9. Planning under uncertainty.
Preferences and utility
One-off decisions
Sequential decisions
The value of information and control
Decision processes
Review
References and further reading
Exercises
10. Multiagent systems.
Multiagent framework
Representations of games
Computing strategies with perfect information
Partially observable multiagent reasoning
Group decision making
Mechanism design
References and further reading
Exercises
11. Beyond supervised learning.
Clustering
Learning belief networks
Reinforcement learning
Review
References and further reading
Exercises
Part IV. Reasoning and individuals and relations:
12. Individuals and relations.
Exploiting structure beyond features
Symbols and semantics
Datalog: a relational rule language
Proofs and substitutions
Function symbols
Applications in natural language processing
Equality
Complete knowledge assumption
Review
References and further reading
Exercises
13. Ontologies and knowledge-based systems.
Knowledge sharing
Flexible representations
Ontologies and knowledge sharing
Querying users and other knowledge sources
Implementing knowledge-based systems
Review
References and further reading
Exercises
14. Relational planning, learning and probabilistic reasoning.
Planning with individuals and relations
Learning with individuals and relations
Probabilistic relational models
Review
References and further reading
Exercises
Part V. The Big Picture:
15. Retrospect and prospect.
Dimensions of complexity revisited
Social and ethical consequences
References and further reading
Appendix A. Mathematical preliminaries and notation:
Discrete mathematics
Functions, factors, and arrays
Relations and relational algebra.
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