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Volumn , Issue , 2010, Pages 1268-1277

Reading between the lines: Learning to map high-level instructions to commands

Author keywords

[No Author keywords available]

Indexed keywords

ENVIRONMENT MODELS; EXTERNAL ENVIRONMENTS; MISSING INFORMATION; POLICY-GRADIENT REINFORCEMENT LEARNING ALGORITHM;

EID: 84859963996     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (85)

References (26)
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    • 0005000120 scopus 로고
    • Understanding natural language instructions: The case of purpose clauses
    • Barbara Di Eugenio. 1992. Understanding natural language instructions: the case of purpose clauses. In Proceedings of ACL, pages 120-127.
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    • Di Eugenio, B.1
  • 7
    • 80053432060 scopus 로고    scopus 로고
    • Reading to learn: Constructing features from semantic abstracts
    • Jacob Eisenstein, James Clarke, Dan Goldwasser, and Dan Roth. 2009. Reading to learn: Constructing features from semantic abstracts. In Proceedings of EMNLP, pages 958-967.
    • (2009) Proceedings of EMNLP , pp. 958-967
    • Eisenstein, J.1    Clarke, J.2    Goldwasser, D.3    Roth, D.4
  • 8
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    • Intentional context in situated natural language learning
    • Michael Fleischman and Deb Roy. 2005. Intentional context in situated natural language learning. In Proceedings of CoNLL, pages 104-111.
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    • Fleischman, M.1    Roy, D.2
  • 9
    • 60349084848 scopus 로고    scopus 로고
    • Model-based function approximation in reinforcement learning
    • Nicholas K. Jong and Peter Stone. 2007. Model-based function approximation in reinforcement learning. In Proceedings of AAMAS, pages 670-677.
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    • Jong, N.K.1    Stone, P.2
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    • User simulations for context-sensitive speech recognition in spoken dialogue systems
    • Oliver Lemon and Ioannis Konstas. 2009. User simulations for context-sensitive speech recognition in spoken dialogue systems. In Proceedings of EACL, pages 505-513.
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    • Lemon, O.1    Konstas, I.2
  • 13
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    • Learning semantic correspondences with less supervision
    • Percy Liang, Michael I. Jordan, and Dan Klein. 2009. Learning semantic correspondences with less supervision. In Proceedings of ACL, pages 91-99.
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    • Liang, P.1    Jordan, M.I.2    Klein, D.3
  • 14
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    • Walk the talk: Connecting language, knowledge, and action in route instructions
    • Matt MacMahon, Brian Stankiewicz, and Benjamin Kuipers. 2006. Walk the talk: connecting language, knowledge, and action in route instructions. In Proceedings of AAAI, pages 1475-1482.
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    • MacMahon, M.1    Stankiewicz, B.2    Kuipers, B.3
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    • Learning to connect language and perception
    • Raymond J. Mooney. 2008. Learning to connect language and perception. In Proceedings of AAAI, pages 1598-1601.
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    • Mooney, R.J.1
  • 20
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    • Optimizing dialogue management with reinforcement learning: Experiments with the njfun system
    • Satinder Singh, Diane Litman, Michael Kearns, and Marilyn Walker. 2002. Optimizing dialogue management with reinforcement learning: Experiments with the njfun system. Journal of Artificial Intelligence Research, 16:105-133.
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    • Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic
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    • On the integration of grounding language and learning objects
    • Chen Yu and Dana H. Ballard. 2004. On the integration of grounding language and learning objects. In Proceedings of AAAI, pages 488-493.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.