Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Foundations of logic programming
Foundations of logic programming
Data structures and network algorithms
Data structures and network algorithms
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
ACM Transactions on Programming Languages and Systems (TOPLAS) - The MIT Press scientific computation series
Artificial Intelligence
A PROLOG technology theorem prover: implementation by an extended PROLOG compiler
Proc. of the 8th international conference on Automated deduction
Conspiracy numbers for min-max search
Artificial Intelligence
Incremental computation and the incremental evaluation of functional programs
Incremental computation and the incremental evaluation of functional programs
Incremental, approximate planning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Pruning duplicate nodes in depth-first search
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
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This paper applies the idea of conspiracy numbers to derive two heuristic algorithms for searching and/or trees. The first algorithm is an AO* best-first algorithm but the standard guarantees do not apply usefully to it because it conforms to the economic principle of sunk costs. The second algorithm works depth-first and guides the search done by an iterative deepening SLD-resolution theorem prover that we have implemented. To avoid repeated effort, the prover caches successes and failures. It exploits the fact that a new goal matches a cached goal if it is a substitution instance of the latter, not just if the two are identical. Experimental results indicate that conspiracy numbers and especially the new caching scheme are effective in practice.