Titlebook: Search in Artificial Intelligence; Laveen Kanal,Vipin Kumar Textbook 1988 Springer-Verlag New York Inc. 1988 Operations Research.artificia

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A General Branch-and-Bound Formulation for AND/OR Graph and Game Tree Search, proofs. Furthermore, the procedure is applicable to a very large class of problems, and thus provides a way of synthesizing algorithms for new applications. The procedure searches an AND/OR graph in a top-down manner (by selectively developing various potential solutions) and can be viewed as a general branch-and-bound procedure.

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Network-Based Heuristics for Constraint-Satisfaction Problems, and the simplicity of tree-structured CSPs. The advice is generated by simplifying the pending subproblems into trees, counting the number of consistent solutions in each simplified subproblem, and comparing these counts to decide among the choices pending in the original problem.

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Textbook 1988ns research. Combinatorial optimization, decision analysis, game playing, learning, planning, pattern recognition, robotics and theorem proving are some of the areas in which search algbrithms playa key role. Less than a decade ago the conventional wisdom in artificial intelligence was that the best

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An Algebra for Search Problems and Their Solutions,conjunct in the set that can possibly be extended to a minimal feasible conjunct. The operators themselves are based on computationally feasible dominance relations that order certain pairs of conjuncts, and whose axioms allow us to infer the orderings of the feasible extensions of such conjuncts.

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The Optimality of A*, but if we confine the performance tests to cases where the estimates are also consistent, then A* is indeed optimal. Additionally, we show that A* is optimal over a subset of the latter class containing all . algorithms that are guided by path-dependent evaluation functions.

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Average-Case Analysis of Heuristic Search in Tree-Like Networks,number of goal nodes is polynomial in .. the normalizing function for the error is logarithmic. Pearl’s contention that the average-case analysis of weighted heuristic search is not too attractive is also verified. It is hoped that the general approach described here will encourage similar studies o

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Optimal Path-Finding Algorithms,lem difficulty. Macro-operators store solutions to previously solved subproblems in order to speed-up solutions to new problems, and are subject to a multiplicative time-space tradeoff. Finally, an analysis of abstraction concludes that abstraction hierarchies can reduce exponential problems to line

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