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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 38 (4 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...
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 ..."
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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
Parallel Branch and Bound Algorithms on Hypercube Multiprocessors*
"... Branch and Bound (BB) algorithms are a generalization of many search algorithms used in Artificial Intelligence and Operations Research. This paper presents our work on implementing BB algorithms on hypercube multiprocessors. The Ol integer linear programming (ILP) problem is taken as an example b ..."
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Branch and Bound (BB) algorithms are a generalization of many search algorithms used in Artificial Intelligence and Operations Research. This paper presents our work on implementing BB algorithms on hypercube multiprocessors. The Ol integer linear programming (ILP) problem is taken as an example because it can be implemented to capture the essence of BB search algorithms without too many distracting problem specific details. A BB algorithm for the &l ILP problem is discussed. Two parallel implementations of the algorithm on hypercube multiprocessors are presented. The two implementations demonstrate some of the tradeoffs involved in implementing these algorithms on multiprocessors with no shared memory, su.ch as hypercubes. Experimental results from the NCUBE/six show the performance of the two implementations of the algorithm. Future research work is discussed. 1