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Volumn , Issue , 2005, Pages 241-250

Learning action models from plan examples with incomplete knowledge

Author keywords

[No Author keywords available]

Indexed keywords

INCOMPLETE KNOWLEDGE; INTERMEDIATE STATE; LEARNING PROBLEM; PROPOSITIONAL SATISFIABILITY; STATISTICAL DISTRIBUTION; THEORETICAL FOUNDATIONS; TIME-CONSUMING TASKS; TRAINING EXAMPLE;

EID: 34447128616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (31)

References (20)
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  • 2
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    • Inductive learning of reactive action models
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  • 4
    • 0007980271 scopus 로고    scopus 로고
    • A two-phase exact algorithm for max-sat and weighted max-sat problems
    • Borchers, B. and Furman, J. 1999. A two-phase exact algorithm for max-sat and weighted max-sat problems. Journal of Combinatorial Optimization 2(4):299-306.
    • (1999) Journal of Combinatorial Optimization , vol.2 , Issue.4 , pp. 299-306
    • Borchers, B.1    Furman, J.2
  • 5
    • 0035443448 scopus 로고    scopus 로고
    • The model checking integrated planning system (mips)
    • Edelkamp, S. and Helmert, M. 2001. The model checking integrated planning system (mips). Al Magazine 22(3):67- 71.
    • (2001) Al Magazine , vol.22 , Issue.3 , pp. 67-71
    • Edelkamp, S.1    Helmert, M.2
  • 6
    • 2842560201 scopus 로고
    • Strips: A new approach to the application of theorem proving to problem solving
    • Fikes, R. E. and Nilsson, N. J. 1971. Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2:189-208.
    • (1971) Artificial Intelligence , vol.2 , pp. 189-208
    • Fikes, R.E.1    Nilsson, N.J.2
  • 7
    • 0030703966 scopus 로고    scopus 로고
    • Automatically selecting and using primary effects in planning: Theory and experiments
    • Fink, E. and Yang, Q. 1997. Automatically selecting and using primary effects in planning: Theory and experiments. Artificial Intelligence 89(l-2):285-315.
    • (1997) Artificial Intelligence , vol.89 , Issue.1-2 , pp. 285-315
    • Fink, E.1    Yang, Q.2
  • 10
    • 85148686343 scopus 로고
    • Learning by experimentation: Incremental refinement of incomplete planning domains
    • Gil, Y. 1994. Learning by experimentation: Incremental refinement of incomplete planning domains. In Eleventh Intl Confon Machine Learning, 87-95.
    • (1994) Eleventh Intl Confon Machine Learning , pp. 87-95
    • Gil, Y.1
  • 11
    • 0030352389 scopus 로고    scopus 로고
    • Pushing the envelope: Planning, propositional logic, and stochastic search
    • Menlo Park, California: AAAI Press
    • Kautz, H. and Selman, B. 1996. Pushing the envelope: Planning, propositional logic, and stochastic search. In Proceedings of the Thirteenth National Conference on AI (AAAI 96), 1194-1201. Menlo Park, California: AAAI Press.
    • (1996) Proceedings of the Thirteenth National Conference on AI (AAAI 96) , pp. 1194-1201
    • Kautz, H.1    Selman, B.2
  • 14
    • 0030358980 scopus 로고    scopus 로고
    • Searching for planning operators with context-dependent and probabilistic effects
    • Menlo Park, California: AAAI Press
    • Oates, T. and Cohen, P. R. 1996. Searching for planning operators with context-dependent and probabilistic effects. In Proceedings of the Thirteenth National Conference on AI (AAAI 96), 865-868. Menlo Park, California: AAAI Press.
    • (1996) Proceedings of the Thirteenth National Conference on AI (AAAI 96) , pp. 865-868
    • Oates, T.1    Cohen, P.R.2
  • 15
    • 33847369585 scopus 로고
    • Using the event calculus to intetgrate planning and learning in an intelligent autonomous agent
    • IOS Press
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    • (1994) Current Trends in AI Planning , pp. 254-265
    • Sablon, G.1    Bruynooghe, M.2
  • 17
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    • Wang, X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.