Skip to main content Accessibility help
×
Home

Generation of macro-operators via investigation of action dependencies in plans

  • Lukáš Chrpa (a1)

Abstract

There are many approaches for solving planning problems. Many of these approaches are based on ‘brute force’ search methods and they usually do not care about structures of plans previously computed in particular planning domains. By analyzing these structures, we can obtain useful knowledge that can help us find solutions to more complex planning problems. The method described in this paper is designed for gathering macro-operators by analyzing training plans. This sort of analysis is based on the investigation of action dependencies in training plans. Knowledge gained by our method can be passed directly to planning algorithms to improve their efficiency.

Copyright

References

Hide All
Armano, G., Cherch, G., Vargiu, E. 2005. DHG: a system for generating macro-operators from static domain analysis. In Proceedings of Artificial Intelligence and Applications (AIA), Innsbruck, Austria, 1823.
Armano, G., Cherch, G., Vargiu, E. 2003. A parametric hierarchical planner for experimenting abstraction techniques. Proceedings of IJCAI, Acapulco, Mexico, 936941.
Blum, A., Furst, M. 1997. Fast planning through planning graph analysis. Artificial Intelligence 90(1–2), 281300.
Bonet, B., Geffner, H. 1999. Planning as heuristic search: new results. In Proceedings of ECP, Durham, UK, Lecture Notes in Computer Science 1809, 360–372. Springer.
Botea, A., Enzenberger, M., Muller, M., Schaeffer, J. 2005. Macro-FF: improving AI planning with automatically learned macro-operators. Journal of Artificial Intelligence Research 24, 581621.
Chrpa, L. 2008. Generation of macro-operators via investigation of actions dependencies in plans. In Proceedings of KEPS, Sydney, Australia. http://ktiml.mff.cuni.cz/~bartak/KEPS2008/
Chrpa, L., Bartak, R. 2008a. Looking for planning problems solvable in polynomial time via investigation of structures of action dependencies. In Proceedings of SCAI, Stockholm, Sweden. IOS Press, 173, 175–180.
Chrpa, L., Bartak, R. 2008b. Towards getting domain knowledge: plans analysis through investigation of actions dependencies. In Proceedings of FLAIRS, Coconut Grove, Florida, USA. AAAI Press, 531536.
Chrpa, L., Bartak, R. 2009. Reformulating planning problems by eliminating unpromising actions. In Proceedings of SARA, Lake Arrowhead, California, USA. AAAI Press, 5057.
Chrpa, L., Surynek, P., Vyskocil, J 2007. Encoding of planning problems and their optimizations in linear logic. In Proceedings of INAP/WLP. Technical Report 434, Bayerische Julius–Maximilians–Universität Würzburg, 47–58.
Coles, A., Fox, M., Smith, K. A. 2007. Online identification of useful macro-actions for planning. In Proceedings of ICAPS, Providence, RI, USA. AAAI Press, 97104.
Coles, A., Smith, K. A. 2007. Marvin: a heuristic search planner with online macro-action learning. Journal of Artificial Intelligence Research 28, 119156.
Dawson, C., Siklóssy, L. 1977. The role of preprocessing in problem solving systems. In Proceedings of IJCAI 1, Cambridge, MA, USA, 465471.
Fikes, R., Nilsson, L. 1971. STRIPS: a new approach to the application of theorem proving to problem solving. Artificial Inteligence 2(3–4), 189208.
Fox, M., Long, D. 1999. The detection and exploitation of symmetry in planning problems. In Proceedings of IJCAI, Stockholm, Sweden, 956961.
Geffner, H. 1990. Causal theories of nonmonotonic reasoning. In Proceedings of AAAI, Boston, Massachusetts, USA. AAAI Press, 524530.
Gerevini, A., Serina, I. 2002. LPG: a planner based on local search for planning graphs with action costs. In Proceedings of AIPS, Toulouse, France. AAAI Press, 1322.
Ghallab, M., Nau, D., Traverso, P. 2004. Automated Planning, Theory and Practice. Morgan Kaufmann Publishers.
Gimenez, O., Jonsson, A. 2007. On the hardeness of planning problems with simple causal graphs. In Proceedings of ICAPS, Providence, RI, USA. AAAI Press, 152159.
Grandcolas, S., Pain-Barre, C. 2007. Filtering, decomposition and search space reduction for optimal sequential planning. In Proceedings of AAAI, Vancouver, British Columbia, Canada. AAAI Press, 993998.
Hoffmann, J., Porteous, J., Sebastia, L. 2004. Ordered landmarks in planning. Journal of Artificial Intelligence Research 22, 215278.
Hsu, C.-W., Wah, B. W., Huang, R., Chen, Y 2007. SGPlan. http://manip.crhc.uiuc.edu/programs/SGPlan/index.html
Iba, G.A. 1989. A Heuristic approach to the discovery of macro-operators. Machine Learning 3, 285317.
Iba, G. A. 1985. Learning by discovering macros in puzzle solving. In Proceedings of IJCAI, Los Angeles, California, USA, 640642.
Katz, M., Domshlak, C. 2007. Structural patterns of tracable sequentialy-optimal planning. In Proceedings of ICAPS, Providence, RI, USA. AAAI Press, 200207.
Kautz, H., Selman, B., Hoffmann, J 2006. Satplan: planning as satisfiability. In Proceedings of IPC. http://zeus.ing.unibs.it/ipc-5/booklet/deterministic11.pdf
Knoblock, C. 1994. Automatically generated abstractions for planning. Artificial Intelligence 68(2), 243302.
Korf, R. 1985. Macro-operators: a weak method for learning. Artificial Intelligence 26(1), 3577.
Kvanström, J., Magnusson, M. 2003. TALplanner in the third international planning competition: extensions and control rules. Journal of Artificial Intelligence Research 20, 343377.
Lin, F. 1995. Embracing causality in specifiing the indirect effects of actions. In Proceedings of IJCAI, Montréal, Québec, Canada. AAAI press, 19851991.
McCain, N., Turner, H. 1997. Causal theories of action and change. In Proceedings of AAAI, Providence, RI, USA. AAAI press, 460465.
McCluskey, T. L. 1987. Combining weak learning heuristics in general problem solvers. In Proceedings of IJCAI, Milan, Italy, 331333.
Mehlhorn, K. 1984. Data Structures and Algorithms 2: Graph Algorithms and NP-completeness. Springer-Verlag.
Minton, S. 1985. Selectively generalizing plans for problem-solving. In Proceedings of IJCAI, Los Angeles, California, USA, 596599.
Minton, S., Carbonell, J. G. 1987. Strategies for learning search control rules: an explanation-based approach. In Proceedings of IJCAI, Milan, Italy, 228235.
Nau, D., Au, T., Ilghami, O., Kuter, U., Mudrock, J., Wu, D., Yaman, F. 2003. SHOP2: an HTN planning system. Journal of Artificial Intelligence Research 20, 379404.
Nejati, N., Langley, P., Konik, T. 2006. Learning hierarchical task networks by observation. In Proceedings of ICML, Pittsburgh, Pennsylvania, USA, ACM International Conference Proceeding Series 148, 665–672.
Newton, M. H., Levine, J., Fox, M., Long, D. 2007. Learning macro-actions for arbitrary planners and domains. In Proceedings of ICAPS, Providence, Rhode Island, USA, 256263.
Richter, S., Westphal, M 2008. The LAMA planner using landmark counting in heuristic search In Proceedings of the 6th IPC. http://ipc.informatik.uni-freiburg.de/
Vidal, V., Geffner, H. 2006. Branching and Pruning: an optimal temporal POCL planner based on constraint programming. Artificial Intelligence 170(3), 298335.
Wu, K., Yang, Q., Jiang, Y. 2005. Arms: action-relation modelling system for learning action models. In Proceedings of ICKEPS. http://scom.hud.ac.uk/scomtlm/competition/papers/paper6.pdf

Related content

Powered by UNSILO

Generation of macro-operators via investigation of action dependencies in plans

  • Lukáš Chrpa (a1)

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.