The ability to express derived predicates in the formalization of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of “Rule-Action Graphs”, particular graphs of actions and rules representing derived predicates. We propose some techniques for representing such rules and reasoning with them, which are integrated into a framework for planning through local search and rule-action graphs. We also present some heuristics for guiding the search of a rule-action graph representing a valid plan. Finally, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach. The results of our experiments also show that our planner performs quite well compared to other state-of-the-art planners handling derived predicates.
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This work is a revised and significantly extended version of a paper appearing in the Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-2005) .
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Gerevini, A.E., Saetti, A. & Serina, I. Planning in domains with derived predicates through rule-action graphs and local search. Ann Math Artif Intell 62, 259–298 (2011). https://doi.org/10.1007/s10472-011-9240-3
- Automated planning
- Domain-independent planning
- Efficient planning
- Planning with derived predicates
- Heuristic search for planning
Mathematics Subject Classifications (2010)