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Hoffmann J, Nebel B (2001) The FF planning system: fast plan generation through heuristic search. Hoffmann J (2005) Where “ignoring delete lists” works: local search topology in planning benchmarks. Helmert M (2006) The fast downward planning system. Ghallab M, Nau D, Traverso P (2004) Automated planning, theory and practice. Appl Intell 36:932–959įox M, Long D (1998) The automatic inference of state invariants in tim. J Artif Intell Res 40:767–813ĭella Penna G, Magazzeni D, Mercorio F (2012) A universal planning system for hybrid domains. In: Working notes of ICAPS 2009 workshop on planning and learning, pp 37–44ĭe la Rosa T, Jiménez S, Fuentetaja R, Borrajo D (2011) Scaling up heuristic planning with relational decision trees. In: Proceedings of the 18th international conference on automated planning and schedulingĭe la Rosa T, Jiménez S, García-Durán R, Fernández F, García-Olaya A, Borrajo D (2009) Three relational learning approaches for lookahead heuristic planning. Knowl Eng Rev 20(3):283–287ĭe la Rosa T, Jiménez S, Borrajo D (2008) Learning relational decision trees for guiding heuristic planning.
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In: Proceedings of the 17th international conference on automated planning and scheduling (ICAPS-2007), Providence, RI, USAĬox M, Muñoz-Avlia H, Bergmann R (2005) Case-based planning.
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AAAI Press, Menlo ParkĬoles A, Fox M, Smith A (2006) A new local search algorithm for forward-chaining planning. In: Proceedings of the 16th international conference on automated planning and scheduling (ICAPS 2006). Artif Intell 129(1–2):5–33Ĭastillo L, Fernández-Olivares J, García-Pérez O, Palao F (2006) Bringing users and planning technology together. Morgan Kaufmann, Montreal, pp 1636–1642īonet B, Geffner H (2001) Planning as heuristic search. In: Mellish CS (ed) Proceedings of the 14th international joint conference on artificial intelligence, IJCAI-95, vol 2. AI Commun 7(1):39–59īlum A, Furst M (1995) Fast planning through planning graph analysis. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.Īamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations, and system approaches. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process.
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Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics.