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Pruning Duplicate Nodes in DepthFirst Search
 In AAAI National Conference
, 1993
"... Bestfirst search algorithms require exponential memory, while depthfirst algorithms require only linear memory. On graphs with cycles, however, depthfirst searches do not detect duplicate nodes, and hence may generate asymptotically more nodes than bestfirst searches. We present a technique for ..."
Abstract

Cited by 37 (3 self)
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Bestfirst search algorithms require exponential memory, while depthfirst algorithms require only linear memory. On graphs with cycles, however, depthfirst searches do not detect duplicate nodes, and hence may generate asymptotically more nodes than bestfirst searches. We present a technique for reducing the asymptotic complexity of depthfirst search by eliminating the generation of duplicate nodes. The automatic discovery and application of a finite state machine (FSM) that enforces pruning rules in a depthfirst search, has significantly extended the power of search in several domains. We have implemented and tested the technique on a grid, the Fifteen Puzzle, the TwentyFour Puzzle, and two versions of Rubik's Cube. In each case, the effective branching factor of the depthfirst search is reduced, reducing the asymptotic time complexity. IntroductionThe Problem Search techniques are fundamental to artificial intelligence. Bestfirst search algorithms such as breadthfirst se...
Backtracking
"... Contents 1 Introduction 3 2 Models of computation 6 3 The Set Union Problem 9 4 The WorstCase Time Complexity of a Single Operation 15 5 The Set Union Problem with Deunions 18 6 Split and the Set Union Problem on Intervals 22 7 The Set Union Problem with Unlimited Backtracking 26 1 Introduction A ..."
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Contents 1 Introduction 3 2 Models of computation 6 3 The Set Union Problem 9 4 The WorstCase Time Complexity of a Single Operation 15 5 The Set Union Problem with Deunions 18 6 Split and the Set Union Problem on Intervals 22 7 The Set Union Problem with Unlimited Backtracking 26 1 Introduction An equivalence relation on a finite set S is a binary relation that is reflexive symmetric and transitive. That is, for s; t and u in S, we have that sRs, if sRt then tRs, and if sRt and tRu then sRu. Set S is partitioned by R into equivalence classes where each class cointains all and only the elements that obey R pairwise. Many computational problems involve representing, modifying and tracking the evolution of equivalenc
Pruning uplieate Nodes in thFirst
"... Bestfirst search algorithms require exponential memory, while depthfirst algorithms require only linear memory. On graphs with cycles, however, depthfirst searches do not detect duplicate nodes, and hence may generate asymptotically more nodes than bestfirst searches. We present a technique for ..."
Abstract
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Bestfirst search algorithms require exponential memory, while depthfirst algorithms require only linear memory. On graphs with cycles, however, depthfirst searches do not detect duplicate nodes, and hence may generate asymptotically more nodes than bestfirst searches. We present a technique for reducing the asymptotic complexity of depthfirst search by eliminating the generation of duplicate nodes. The automatic discovery and application of a finite state machine (FSM) that enforces pruning rules in a depthfirst search, has significantly extended the power of search in several domains. We have implemented and tested the technique on a grid, the Fifteen Puzzle, the