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This book brings together some new insights and recent developments on the topics of search procedures in Artificial Intelligence and the relationships among search methods in Artificial Intelligence, Operations Research, and Engineering. The purpose of the book is to present these new insights and recent developments in a manner accessible to students and professionals in Computer Science, Engineering, Operations Research, and Applied Mathematics. The articles should provide the reader with a broad view of recent developments on search in AI and some of the relationships among branch and bound, heuristic search, and dynamic programming.
New models for discrete optimization problems, new results on the average case of complexity of the well known A algorithm, new results on the conditions under which A is optimal over other search algorithms, use of different sources of knowledge in heuristic search, new results on the constraint satisfaction problem, and a result showing the minimax back up rule does not do as well as the product rule in some real games.
List of contents
Contents: The CDP: A Unifying Formulation for Heuristic Search, Dynamic Programming, and Branch-and-Bound.- An Algebra for Search Problems and Their Solutions.- A General Branch-and-Bound Formulation for AND/OR Graph and Game Tree Search.- Average-Case Analysis of Heuristic Search in Tree-Like Networks.- The Optimality of A .- Network Search Algorithms with Modifiable Heuristics.- Optimal Path Finding Algorithms.- Developments with GPS.- Tree Search and Arc Consistency in Constraint Satisfaction Algorithms.- Backtrack-Free and Backtrack-Bounded Search.- Network-Based Heuristics for Constraint-Satisfaction Problems.- Fundamental Properties of Networks of Constraints: A New Formulation.- Comparison of the MINIMAX and PRODUCT Back-Up Rules in a Variety of Games.- Index.
