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Volumn 1, Issue , 2006, Pages 913-919

Learning partially observable action schemas

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTATION THEORY; LARGE SCALE SYSTEMS; MATHEMATICAL MODELS;

EID: 33750699618     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (22)

References (17)
  • 1
    • 84880732671 scopus 로고    scopus 로고
    • Learning partially observable deterministic action models
    • MK
    • E. Amir. Learning partially observable deterministic action models. In IJCAI '05. MK, 2005.
    • (2005) IJCAI '05
    • Amir, E.1
  • 4
    • 0000854197 scopus 로고    scopus 로고
    • Learning the structure of dynamic probabilistic networks
    • MK
    • N. Friedman, K. Murphy, and S. Russell. Learning the structure of dynamic probabilistic networks. In Proc. UAI '98. MK, 1998.
    • (1998) Proc. UAI '98
    • Friedman, N.1    Murphy, K.2    Russell, S.3
  • 5
    • 84880688943 scopus 로고    scopus 로고
    • Learning probabilistic relational models
    • MK
    • Nir Friedman, Lise Getoor, Daphne Koller, and Avi Pfeffer. Learning probabilistic relational models. In IJCAI '99, pages 1300-1307. MK, 1999.
    • (1999) IJCAI '99 , pp. 1300-1307
    • Friedman, N.1    Getoor, L.2    Koller, D.3    Pfeffer, A.4
  • 6
    • 33750684125 scopus 로고    scopus 로고
    • Learning probabilistic relational models
    • Lise Getoor. Learning probabilistic relational models. Lecture Notes in Computer Science, 1864:1300-1307, 2000.
    • (2000) Lecture Notes in Computer Science , vol.1864 , pp. 1300-1307
    • Getoor, L.1
  • 8
    • 85148686343 scopus 로고
    • Learning by experimentation: Incremental refinement of incomplete planning domains
    • Y. Gil. Learning by experimentation: Incremental refinement of incomplete planning domains. In Proc. ICML-94, 1994.
    • (1994) Proc. ICML-94
    • Gil, Y.1
  • 9
    • 85153938292 scopus 로고
    • Reinforcement learning algorithm for partially observable Markov decision problems
    • T. Jaakkola, S. P. Singh, and M. I. Jordan. Reinforcement learning algorithm for partially observable Markov decision problems. In Proc. NIPS'94, volume 7, 1994.
    • (1994) Proc. NIPS'94 , vol.7
    • Jaakkola, T.1    Singh, S.P.2    Jordan, M.I.3
  • 10
    • 0003861655 scopus 로고    scopus 로고
    • PhD thesis, Department of Computer Science, Brown University. Technical report CS-96-09
    • M. L. Littman. Algorithms for sequential decision making. PhD thesis, Department of Computer Science, Brown University, 1996. Technical report CS-96-09.
    • (1996) Algorithms for Sequential Decision Making
    • Littman, M.L.1
  • 11
    • 0022661717 scopus 로고
    • Applications of circumscription in formalizing common sense knowledge
    • J. McCarthy. Applications of circumscription in formalizing common sense knowledge. Artificial Intelligence, 28:89-116, 1986.
    • (1986) Artificial Intelligence , vol.28 , pp. 89-116
    • McCarthy, J.1
  • 12
    • 84957053329 scopus 로고
    • Machine invention of first-order predicates by inverting resolution
    • S. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In Proc. ICML-88, 1988.
    • (1988) Proc. ICML-88
    • Muggleton, S.1    Buntine, W.2
  • 16
    • 84969334117 scopus 로고
    • Learning by observation and practice: An incremental approach for planning operator acquisition
    • MK
    • X. Wang. Learning by observation and practice: an incremental approach for planning operator acquisition. In Proc. ICML-95, pages 549-557. MK, 1995.
    • (1995) Proc. ICML-95 , pp. 549-557
    • Wang, X.1
  • 17
    • 80055026555 scopus 로고    scopus 로고
    • Arms: Action-relation modelling system for learning action models
    • K. Wu, Q. Yang, and Y. Jiang. Arms: Action-relation modelling system for learning action models. Proc. ICAPS'05, 2005.
    • (2005) Proc. ICAPS'05
    • Wu, K.1    Yang, Q.2    Jiang, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.