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Volumn , Issue , 2007, Pages 344-351

Discovering relational domain features for probabilistic planning

Author keywords

[No Author keywords available]

Indexed keywords

BEHAVIORAL RESEARCH; DECISION MAKING; DOMAIN KNOWLEDGE; ITERATIVE METHODS; STOCHASTIC SYSTEMS;

EID: 58349116432     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (14)

References (17)
  • 2
    • 0013464438 scopus 로고    scopus 로고
    • Integrating experimentation and guidance in relational reinforcement learning
    • Driessens, K., and Džeroski, S. 2002. Integrating experimentation and guidance in relational reinforcement learning. In ICML.
    • (2002) ICML
    • Driessens, K.1    Džeroski, S.2
  • 3
    • 33748273074 scopus 로고    scopus 로고
    • Graph kernels and gaussian processes for relational reinforcement learning
    • Driessens, K.; Ramon, J.; and Gartner, T. 2006. Graph kernels and gaussian processes for relational reinforcement learning. MLJ 64:91-119.
    • (2006) MLJ , vol.64 , pp. 91-119
    • Driessens, K.1    Ramon, J.2    Gartner, T.3
  • 4
    • 0035312760 scopus 로고    scopus 로고
    • Relational reinforcement learning
    • Dzeroski, S.; DeRaedt, L.; and Driessens, K. 2001. Relational reinforcement learning. MLJ 43:7-52.
    • (2001) MLJ , vol.43 , pp. 7-52
    • Dzeroski, S.1    DeRaedt, L.2    Driessens, K.3
  • 6
    • 0038362668 scopus 로고    scopus 로고
    • Learning generalized policies in planning domains using concept languages
    • Martin, M., and Geffner, H. 2000. Learning generalized policies in planning domains using concept languages. In KRR.
    • (2000) KRR
    • Martin, M.1    Geffner, H.2
  • 10
    • 33847202724 scopus 로고
    • Learning to predict by the methods of temporal differences
    • Sutton, R. S. 1988. Learning to predict by the methods of temporal differences. MLJ 3:9-44.
    • (1988) MLJ , vol.3 , pp. 9-44
    • Sutton, R.S.1
  • 11
    • 0029276036 scopus 로고
    • Temporal difference learning and td-gammon
    • Tesauro, G. 1995. Temporal difference learning and td-gammon. Comm. ACM 38(3):58-68.
    • (1995) Comm. ACM , vol.38 , Issue.3 , pp. 58-68
    • Tesauro, G.1
  • 13
    • 0012252296 scopus 로고
    • Tight performance bounds on greedy policies based on imperfect value functions
    • Technical report, Northeastern University
    • Williams, R. J., and Baird, L. C. 1993. Tight performance bounds on greedy policies based on imperfect value functions. Technical report, Northeastern University.
    • (1993)
    • Williams, R.J.1    Baird, L.C.2
  • 14
    • 51849117028 scopus 로고    scopus 로고
    • Feature-discovering approximate value iteration methods
    • Wu, J., and Givan, R. 2005. Feature-discovering approximate value iteration methods. In SARA.
    • (2005) SARA
    • Wu, J.1    Givan, R.2
  • 16
    • 58349118462 scopus 로고    scopus 로고
    • FF-Replan: A baseline for probabilistic planning
    • Yoon, S.; Fern, A.; and Givan, R. 2007. FF-Replan: A baseline for probabilistic planning. In ICAPS.
    • (2007) ICAPS
    • Yoon, S.1    Fern, A.2    Givan, R.3
  • 17
    • 31144453572 scopus 로고    scopus 로고
    • The first probabilistic track of the international planning competition
    • Younes, H.; Littman, M.; Weissman, D.; and Asmuth, J. 2005. The first probabilistic track of the international planning competition. JAIR 24:851-887.
    • (2005) JAIR , vol.24 , pp. 851-887
    • Younes, H.1    Littman, M.2    Weissman, D.3    Asmuth, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.