scispace - formally typeset
Search or ask a question

Showing papers on "Admissible heuristic published in 2000"


Proceedings ArticleDOI
14 Apr 2000
TL;DR: A new admissible heuristic for planning is formulated, used to guide an IDA* search, and empirically evaluate the resulting optimal planner over a number of domains.
Abstract: HSP and HSPr are two recent planners that search the state-space using an heuristic function extracted from Strips encodings. HSP does a forward search from the initial state recomputing the heuristic in every state, while HSPr does a regression search from the goal computing a suitable representation of the heuristic only once. Both planners have shown good performance, often producing solutions that are competitive in time and number of actions with the solutions found by Graphplan and SAT planners. HSP and HSPr. however, are not optimal planners. This is because the heuristic function is not admissible and the search algorithms are not optimal. In this paper we address this problem. We formulate a new admissible heuristic for planning, use it to guide an IDA* search, and empirically evaluate the resulting optimal planner over a number of domains. The main contribution is the idea underlying the heuristic that yields not one but a whole family of polynomial and admissible heuristics that trade accuracy for efficiency. The formulation is general and sheds some light on the heuristics used in HSP and Graphplan, and their relation. It exploits the factored (Strips) representation of planning problems, mapping shortest-path problems in state-space into suitably defined shortest-path problems in atom-space. The formulation applies with little variation to sequential and parallel planning, and problems with different action costs.

370 citations


Proceedings Article
30 Jul 2000
TL;DR: In this article, the running time of admissible heuristic search algorithms is predicted as a function of the solution depth and the heuristic evaluation function, and it is shown that an admissible evaluation function reduces the effective depth of search, rather than the effective branching factor.
Abstract: In the past several years, significant progress has been made in finding optimal solutions to combinatorial problems. In particular, random instances of both Rubik's Cube, with over 1019 states, andt he 5 × 5 sliding-tile puzzle, with almost 1025 states, have been solved optimally. This progress is not the result of better search algorithms, but more effective heuristic evaluation functions. In addition, we have learned how to accurately predict the running time of admissible heuristic search algorithms, as a function of the solution depth and the heuristic evaluation function. One corollary of this analysis is that an admissible heuristic function reduces the effective depth of search, rather than the effective branching factor.

35 citations


Book ChapterDOI
26 Jul 2000
TL;DR: This work has learned how to accurately predict the running time of admissible heuristic search algorithms, as a function of the solution depth and the heuristic evaluation function.
Abstract: In the past several years, significant progress has been made in finding optimal solutions to combinatorial problems. In particular, random instances of both Rubik's Cube, with over 1019 states, andt he 5 × 5 sliding-tile puzzle, with almost 1025 states, have been solved optimally. This progress is not the result of better search algorithms, but more effective heuristic evaluation functions. In addition, we have learned how to accurately predict the running time of admissible heuristic search algorithms, as a function of the solution depth and the heuristic evaluation function. One corollary of this analysis is that an admissible heuristic function reduces the effective depth of search, rather than the effective branching factor.

29 citations