Hostname: page-component-74656f9884-8r8nv Total loading time: 0 Render date: 2026-11-08T09:31:59.954Z Has data issue: false hasContentIssue false
  • English
  • Français

ARMS: an automatic knowledge engineering tool for learning action models for AI planning

Published online by Cambridge University Press:  01 June 2007

KANGHENG WU ,
QIANG YANG  and
YUNFEI JIANG
KANGHENG WU
Affiliation:
Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong; e-mail: kanghengwu@hotmail.com, qyang@cs.ust.hk Software Institute, Sun Yat-Sen University (Zhongshan University), Guangzhou, China e-mail: lncsri05@zsu.edu.cn
QIANG YANG
Affiliation:
Department of Computer Science, Hong Kong University of Science and Technology, Hong Kong; e-mail: kanghengwu@hotmail.com, qyang@cs.ust.hk
YUNFEI JIANG
Affiliation:
Software Institute, Sun Yat-Sen University (Zhongshan University), Guangzhou, China e-mail: lncsri05@zsu.edu.cn
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

We present an action model learning system known as ARMS (Action-Relation Modelling System) for automatically discovering action models from a set of successfully observed plans. Current artificial intelligence (AI) planners show impressive performance in many real world and artificial domains, but they all require the definition of an action model. ARMS is aimed at automatically learning action models from observed example plans, where each example plan is a sequence of action traces. These action models can then be used by the human editors to refine. The expectation is that this system will lessen the burden of the human editors in designing action models from scratch. In this paper, we describe the ARMS in detail. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted SAT) problem and solves it using a weighted MAXSAT solver. Furthermore, we show empirical evidence that ARMS can indeed learn a good approximation of the finally action models effectively.

References

Agrawal, Rakesh and Srikant, Ramakrishnan, 1994 Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB). Morgan Kaufmann, pp. 487–499.Google Scholar
Andrew, Garland and Neal, Lesh, 2002 Plan evaluation with incomplete action descriptions. In Proceedings of the Seventeenth National Conference on Artificial Intelligence 2002. AAAI, pp. 461–467.Google Scholar
Bacchus, Fahiem and Kabanza, Froduald, 2000 Using temporal logics to express search control knowledge for planning. Artificial Intelligence 116 123–191.Google Scholar
Benson, Scott, 1995 Inductive learning of reactive action models. In International Conference on Machine Learning, pp. 47–54.Google Scholar
Blum, Avrim and Furst, Merrick, 1997 Fast planning through planning graph analysis. Artificial Intelligence 90 281–300.CrossRefGoogle Scholar
Blythe, Jim, Kim, Jihie, Ramachandran, Surya and Gil, Yolanda, 2001 An integrated environment for knowledge acquisition. In Intelligent User Interfaces. ACM, pp. 13–20.Google Scholar
Borchers, Brian and Furman, Judith, 1999 A two-phase exact algorithm for max-sat and weighted max-sat problems. Journal of Combinatorial Optimization 2(4) 299–306.CrossRefGoogle Scholar
Bresina, John, Jonsson, Ari, Morris, Paul and Rajan, Kanna, 2005 Activity planning for the mars exploration rovers. In Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling (ICAPS). AAAI, pp. 40–49.Google Scholar
Cheeseman, Peter, Kanefsky, Bob and Taylor, William M., 1991 Where the really hard problems are. In Proceedings of the Seventh International Joint Conference on Artificial Intelligence (IJCAI). Morgan Kaufmann, pp. 331–337.Google Scholar
Edelkamp, Stefan, and Mehler, Tilman, 2005 Knowledge acquisition and knowledge engineering in the modplan workbench. In Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling (ICAPS). AAAI, pp. 26–33.Google Scholar
Fikes, Richard E. and Nilsson, Nils J., 1971 Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2 189–208.CrossRefGoogle Scholar
Fink, Eugene and Yang, Qiang, 1997 Automatically selecting and using primary effects in planning: Theory and experiments. Artificial Intelligence 89(1–2) 285–315.CrossRefGoogle Scholar
Fox, Maria and Long, Derek, 2003 PDDL2.1: An extension to PDDL for expressing temporal planning domains. Journal of Artificial Intelligence Research 20 61–124.Google Scholar
Garland, Andrew and Lesh, Neal, 2002 Plan evaluation with incomplete action descriptions. In Proceedings of the Eighteenth National Conference on AI (AAAI 2002). AAAI, pp. 461–467.Google Scholar
Gil, Yolanda, 1994 Learning by experimentation: Incremental refinement of incomplete planning domains. In Eleventh Intl Conf on Machine Learning. Morgan Kaufmann, pp. 87–95.Google Scholar
Kautz, Henry and Selman, Bart, 1996 Pushing the envelope: Planning, propositional logic, and stochastic search. In Proceedings of the Thirteenth National Conference on Artificial Intelligence(AAAI). AAAI, pp. 1194–1201.Google Scholar
Kautz, Henry A. and Allen, James F., 1986 Generalized plan recognition. In Proceedings of the Fifth National Conference on Artificial Intelligence (AAAI). AAAI, pp. 32–37.Google Scholar
McCluskey, Thomas Leo, Liu, D. and Simpson, Ron M., 2003 Gipo ii: Htn planning in a tool-supported knowledge engineering environment. In Proceedings of the International Conference on Automated Planning and Scheduling(ICAPS). AAAI, pp. 92–101.Google Scholar
McCluskeyThomas Leo, Thomas Leo,Richardson, N. and Simpson, Ron M., 2002 An Interactive Method for Inducing Operator Descriptions. In Proceedings of the 6th International Conference on AI Planning and Scheduling (AIPS-2002). AAAI.Google Scholar
Moskewicz, Matthew W., Madigan, Conor F., Zhao, Ying, Zhang, Lintao and Malik, Sharad, 2001 Chaff: Engineering an efficient sat solver. In Proceedings of the 38th Design Automation Conference (DAC). ACM.Google Scholar
Myers, Karen L., 2005 Metatheoretic Plan Summarization and Comparison. In Proceedings of the ICAPS-05 Workshop on Mixed-initiative Planning and Scheduling. AAAI.Google Scholar
Nau, Dana S., Au, Tsz-Chiu, Ilghami, Okhtay, Kuter, Ugur, William Murdock, J., Wu, Dan and Yaman, Fusun, 2003 Shop2: An htn planning system. Journal of Artificial Intelligence Research 20 379–404.CrossRefGoogle Scholar
Nau, Dana S., Au, Tsz-Chiu, Ilghami, Okhtay, Kuter, Ugur, William Murdock, J., Wu, Dan and Yaman, Fusun, 2005 Applications of shop and shop2. IEEE Intelligent Systems, 20(2) 34–41.CrossRefGoogle Scholar
Oates, Tim and Cohen, Paul R., 1996 Searching for planning operators with context-dependent and probabilistic effects. In Proceedings of the Thirteenth National Conference on AI (AAAI 96). AAAI, pp. 865–868.Google Scholar
Pednault, Edwin P. D., 1986 Formulating multiagent, dynamic-world problems in the classical planning framework. In Reasoning about Actions and Plans: Proceedings of the 1986 Workshop. Morgan Kaufmann, pp. 47–82.Google Scholar
Pistore, Marco, Traverso, Paolo and Bertoli, Piergiorgio, 2005 Automated composition of web services by planning in asynchronous domains. In Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling(ICAPS). AAAI, pp. 2–11.Google Scholar
Sablon, Gunther and Boulanger, Dmitri, 1994 Using the event calculus to intetgrate planning and learning in an intelligent autonomous agent. In Current Trends in AI Planning. IOS Press, pp. 254–265.Google Scholar
Shen, Weimin, 1994 Autonomous Learning from the Environment. Computer Science Press, W.H. Freeman and Company.Google Scholar
Wang, Xuemei, 1995 Learning by observation and practice: An incremental approach for planning operator acquisition. In Proceedings of the Twelfth International Conference on Machine Learning(ICML). Morgan Kaufmann, pp. 549–557.Google Scholar
Winner, Elly and Veloso, Manuela, 2002 Analyzing plans with conditional effects. In Proceedings of the Sixth International Conference on AI Planning and Scheduling(AIPS). AAAI.Google Scholar
Yang, Qiang, Wu, Kangheng and Jiang, Yunfei, 2005 Learning action models from plan examples with incomplete knowledge. In Proceedings of the Fifteenth International Conference on Automated Planning and Scheduling(ICAPS). AAAI, pp. 241–250.Google Scholar
Younes, Haakan L. S. and Littman, Michael L., 2004 PPDDL1.0: An Extension to PDDL for Expressing Planning Domains with Probabilistic Effects. In CMU-CS-04-167, Carnegie Mellon University.Google Scholar
Wu, D., Sirin, E., Hendler, J., Nau, D. and Parsia, B., 2003 Automatic web services composition using SHOP2. In Twelfth International World Wide Web Conference (WWW2003). ACM.CrossRefGoogle Scholar
Zhang, Hantao, 1997 SATO: an efficient propositional prover. In Proceedings of the International Conference on Automated Deduction (CADE). Springer, pp. 272–275.Google Scholar