KBFS: K-Best-First Search

We introduce a new algorithm, K-best-first search (KBFS), which is a generalization of the well known best-first search (BFS). In KBFS, each iteration simultaneously expands the K best nodes from the open-list (rather than just the best as in BFS). We claim that KBFS outperforms BFS in domains where the heuristic function has large errors in estimation of the real distance to the goal state or does not predict dead-ends in the search tree. We present empirical results that confirm this claim and show that KBFS outperforms BFS by a factor of 15 on random trees with dead-ends, and by a factor of 2 and 7 on the Fifteen and Twenty-Four tile puzzles, respectively. KBFS also finds better solutions than BFS and hill-climbing for the number partitioning problem. KBFS is only appropriate for finding approximate solutions with inadmissible heuristic functions.

[1]  Richard E. Korf,et al.  Depth-First Iterative-Deepening: An Optimal Admissible Tree Search , 1985, Artif. Intell..

[2]  Peter Shell,et al.  Improving Search through Diversity , 1994, AAAI.

[3]  Richard E. Korf,et al.  Improved Limited Discrepancy Search , 1996, AAAI/IAAI, Vol. 1.

[4]  Vipin Kumarxz,et al.  On the Eeciency of Parallel Backtracking , 1992 .

[5]  James A. Hendler,et al.  PRA*: Massively Parallel Heuristic Search , 1995, J. Parallel Distributed Comput..

[6]  Matthew L. Ginsberg,et al.  Limited Discrepancy Search , 1995, IJCAI.

[7]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[8]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[9]  Richard M. Karp,et al.  The Differencing Method of Set Partitioning , 1983 .

[10]  Richard E. Korf,et al.  Best-First Minimax Search , 1996, Artif. Intell..

[11]  Richard E. Korf,et al.  Disjoint pattern database heuristics , 2002, Artif. Intell..

[12]  Richard E. Korf,et al.  Single-Agent Parallel Window Search , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Ambuj Mahanti,et al.  Massively Parallel Heuristic Search for Approximate Optimization Problems , 1991, The Sixth Distributed Memory Computing Conference, 1991. Proceedings.

[14]  Richard M. Karp,et al.  Searching for an Optimal Path in a Tree with Random Costs , 1983, Artif. Intell..

[15]  P. P. Chakrabarti,et al.  Heuristic Search in Restricted Memory , 1989, Artif. Intell..

[16]  E. KorfRichard.  Depth-first iterative-deepening: an optimal admissible tree search , 1985 .

[17]  Stuart J. Russell.  Eecient Memory-bounded Search Methods , 1992 .

[18]  Toby Walsh.  Depth-bounded Discrepancy Search , 1997, IJCAI.

[19]  Amitava Bagchi,et al.  Fast Recursive Formulations for Best-First Search That Allow Controlled Use of Memory , 1989, IJCAI.

[20]  J. Gaschnig.  Performance measurement and analysis of certain search algorithms. , 1979 .

[21]  Ira Pohl,et al.  Heuristic Search Viewed as Path Finding in a Graph , 1970, Artif. Intell..

[22]  Jin Wang,et al.  The Advantages of Using Depth and Breadth Components in Heuristic Search , 1988, ISMIS.

[23]  Jonathan Schaeffer,et al.  Domain-Dependent Single-Agent Search Enhancements , 1999, IJCAI.

[24]  Richard E. Korf,et al.  A Complete Anytime Algorithm for Number Partitioning , 1998, Artif. Intell..

[25]  Stuart J. Russell.  Efficient Memory-Bounded Search Methods , 1992, ECAI.

[26]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[27]  Richard E. Korf,et al.  Linear-Space Best-First Search , 1993, Artif. Intell..

[28]  Pedro Meseguer.  Interleaved Depth-First Search , 1997, IJCAI.

[29]  Vipin Kumar,et al.  On the Efficiency of Parallel Backtracking , 1993, IEEE Trans. Parallel Distributed Syst..

[30]  Dana S. Nau,et al.  Pathology on Game Trees Revisited, and an Alternative to Minimaxing , 1983, Artif. Intell..

[31]  Rina Dechter,et al.  Generalized best-first search strategies and the optimality of A* , 1985, JACM.