This book constitutes the refereed proceedings of the 5th International Symposium on Abstraction, Reformulation, and Approximation, SARA 2002, held in Kananaskis, Alberta, Canada, in August 2002. The 15 revised full papers and 6 short papers were carefully reviewed and selected for inclusion in the book. Also included are 4 invited papers and 14 abstracts. All current aspects of abstraction, reformulation, and approximation in the context of human common-sense reasoning, problem solving, and efficiently reasoning in complex domains are addressed. Among the application fields of these techniques are automatic programming, constraint satisfaction, design, diagnosis, machine learning, search, planning, reasoning, game playing, scheduling, and theorem proving.
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How can a computer program developer ensure that a program actually implements its intended purpose? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
abstract proof search, action timing discretization, defender string, dynamic bundling, gradual games, symbolic forward analysis, static bundling, bargain heuristic, extreme vertex representation, same specification class, base level states, deeper lookahead, affine formulas, active precondition, orthogonal polyhedra, optimization families, abstract percepts, cache agent, hierarchical reinforcement learning, optimization family, visitation counts, maze domain, admissible heuristics, dichotomous splitting, maximal compatibles Key Phrases - Capitalized Phrases (CAPs): (learn more)
Springer-Verlag Berlin Heidelberg, Morgan Kaufmann, University of Massachusetts, Stanford University, International Joint Conference, Lecture Notes, European Conference, New York, Computer-Aided Verification, San Francisco, Springer Verlag, University of Alberta, International Symposium, Neural Information Processing Systems, Number of Trials, Research Summary, Theory of Computing, Ames Research Center, Department of Computing Science, Towers of Hanoi, University of Nebraska-Lincoln, Askira Gelman, Carnegie Mellon University, Computational Intelligence, National Science Foundation New!
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How can a computer program developer ensure that a program actually implements its intended purpose? Read the first page Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
abstract proof search, action timing discretization, defender string, dynamic bundling, gradual games, symbolic forward analysis, static bundling, bargain heuristic, extreme vertex representation, same specification class, base level states, deeper lookahead, affine formulas, active precondition, orthogonal polyhedra, optimization families, abstract percepts, cache agent, hierarchical reinforcement learning, optimization family, visitation counts, maze domain, admissible heuristics, dichotomous splitting, maximal compatibles Key Phrases - Capitalized Phrases (CAPs): (learn more)
Springer-Verlag Berlin Heidelberg, Morgan Kaufmann, University of Massachusetts, Stanford University, International Joint Conference, Lecture Notes, European Conference, New York, Computer-Aided Verification, San Francisco, Springer Verlag, University of Alberta, International Symposium, Neural Information Processing Systems, Number of Trials, Research Summary, Theory of Computing, Ames Research Center, Department of Computing Science, Towers of Hanoi, University of Nebraska-Lincoln, Askira Gelman, Carnegie Mellon University, Computational Intelligence, National Science Foundation New!
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Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
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This book cites 57
books:
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- Reinforcement Learning (The Springer International Series in Engineering and Computer Science) by Richard S. Sutton on 5 pages
- Artificial Intelligence: Theory and Practice by Thomas Dean on page 48, page 49, and page 210
- Artificial Intelligence by Elaine Rich on page 194, and page 307
- Model Checking by Edmund M. Clarke Jr. on page 16, and page 166
- Computational Logic: Logic Programming and Beyond: Essays in Honour of Robert A. Kowalski, Part II (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence) (Pt. 2) by Antonis C. Kakas in Back Matter (1), and Back Matter (2)
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