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Volumn , Issue , 2012, Pages 1536-1542

Algorithmic and Human Teaching of Sequential Decision Tasks

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

EID: 84959210949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (44)

References (30)
  • 2
    • 0000710299 scopus 로고
    • Queries and concept learning
    • Angluin, D. 1988. Queries and concept learning. Machine Learning 2:319-342.
    • (1988) Machine Learning , vol.2 , pp. 319-342
    • Angluin, D.1
  • 6
    • 42649095134 scopus 로고    scopus 로고
    • Measuring teachability using variants of the teaching dimension
    • Balbach, F. 2008. Measuring teachability using variants of the teaching dimension. Theoretical Computer Science 397(1-3):94-113.
    • (2008) Theoretical Computer Science , vol.397 , Issue.1-3 , pp. 94-113
    • Balbach, F.1
  • 11
    • 0027636611 scopus 로고
    • Learning and development in neural networks: The importance of starting small
    • Elman, J. L. 1993. Learning and development in neural networks: The importance of starting small. Cognition 48(1):71-9.
    • (1993) Cognition , vol.48 , Issue.1 , pp. 71-79
    • Elman, J. L.1
  • 16
    • 67650691734 scopus 로고    scopus 로고
    • Near-optimal nonmyopic value of information in graphical models
    • Krause, A., and Guestrin, C. 2005a. Near-optimal nonmyopic value of information in graphical models. In Uncertainty in AI.
    • (2005) Uncertainty in AI
    • Krause, A.1    Guestrin, C.2
  • 20
    • 0000095809 scopus 로고
    • An analysis of approximations for maximizing submodular set functions
    • Nemhauser, G.; Wolsey, L.; and Fisher, M. 1978. An analysis of approximations for maximizing submodular set functions. Mathematical Programming 14(1):265-294.
    • (1978) Mathematical Programming , vol.14 , Issue.1 , pp. 265-294
    • Nemhauser, G.1    Wolsey, L.2    Fisher, M.3
  • 21
    • 80053212134 scopus 로고    scopus 로고
    • Apprenticeship learning using inverse reinforcement learning and gradient methods
    • Neu, G., and Szepesvári, C. 2007. Apprenticeship learning using inverse reinforcement learning and gradient methods. In Uncertainty in Artificial Intelligence (UAI), 295-302.
    • (2007) Uncertainty in Artificial Intelligence (UAI) , pp. 295-302
    • Neu, G.1    Szepesvári, C.2
  • 22
    • 72449199041 scopus 로고    scopus 로고
    • Training parsers by inverse reinforcement learning
    • Neu, G., and Szepesvári, C. 2009. Training parsers by inverse reinforcement learning. Machine learning 77(2):303-337.
    • (2009) Machine learning , vol.77 , Issue.2 , pp. 303-337
    • Neu, G.1    Szepesvári, C.2
  • 24
    • 68949137209 scopus 로고    scopus 로고
    • Active learning literature survey
    • University of Wisconsin-Madison
    • Settles, B. 2010. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison.
    • (2010) Computer Sciences Technical Report 1648
    • Settles, B.1
  • 28
    • 85162076891 scopus 로고    scopus 로고
    • Optimal bayesian recommendation sets and myopically optimal choice query sets
    • Viappiani, P., and Boutilier, C. 2010. Optimal bayesian recommendation sets and myopically optimal choice query sets. Advances in Neural Information Processing Systems 23:2352-2360.
    • (2010) Advances in Neural Information Processing Systems , vol.23 , pp. 2352-2360
    • Viappiani, P.1    Boutilier, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.