PLANNING GRAPH HEURISTICS FOR SOLVING CONTINGENT PLANNING PROBLEMS

Incheol Kim, Hyunsik Kim

Abstract

In order to extract domain-independent heuristics from the specification of a planning problem, it is necessary to relax the given problem and then solve the relaxed one. In this paper, we present a new planning graph, Merged Planning Graph(MPG), and GD heuristics for solving contingent planning problems including both uncertainty about the initial state and non-deterministic action effects. MPG is a new version of the relaxed planning graph for solving the contingent planning problems. In addition to the traditional delete relaxations of deterministic actions, MPG makes the effect-merge relaxations of both sensing and non-deterministic actions. Parallel to the forward expansion of MPG, the computation of GD heuristics proceeds with analysis of interactions among goals and/or subgoals. GD heuristics estimate the minimal reachability cost to achieve the given goal set by excluding redundant action costs. Through experiments in several problem domains, we show that GD heuristics are more informative than the traditional max and additive heuristics. Moreover, in comparison to the overlap heuristics, GD heuristics require much less computational effort for extraction.

References

  1. Bonet, B., Geffner, H., 2001. GPT: A Tool for Planning with Uncertainty and Partial Information, In IJCAI'01, International Joint Conference on Artificial Intelligence. MITPress.
  2. Hoffmann, J., Brafman, R., 2005. Contingent Planning via Heuristic Forward Search with Implicit Belief States, In ICAPS'05, International Conference on Automated Planning and Scheduling. MITPress.
  3. Bryce, D., Kambhampati, S., Smith, D., 2006. Planning Graph Heuristics for Belief Space Search, Journal of Artificial Intelligence Research, Vol. 26, pp.35-99.
  4. Bonet, B., Geffner, H., 2005. mGPT: A Probabilistic Planner Based on Heuristic Search, Journal of Artificial Intelligence Research, Vol.24, pp.933-944.
  5. Hoffmann, J., Nebel, B., 2001. The FF Planning System: Fast Plan Generation through Heuristic Search, Journal of Artificial Intelligence Research, Vol.14, pp.253-302.
Download


Paper Citation


in Harvard Style

Kim I. and Kim H. (2012). PLANNING GRAPH HEURISTICS FOR SOLVING CONTINGENT PLANNING PROBLEMS . In Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8425-95-9, pages 515-519. DOI: 10.5220/0003830505150519


in Bibtex Style

@conference{icaart12,
author={Incheol Kim and Hyunsik Kim},
title={PLANNING GRAPH HEURISTICS FOR SOLVING CONTINGENT PLANNING PROBLEMS},
booktitle={Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2012},
pages={515-519},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003830505150519},
isbn={978-989-8425-95-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - PLANNING GRAPH HEURISTICS FOR SOLVING CONTINGENT PLANNING PROBLEMS
SN - 978-989-8425-95-9
AU - Kim I.
AU - Kim H.
PY - 2012
SP - 515
EP - 519
DO - 10.5220/0003830505150519