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Volumn , Issue , 2005, Pages 231-240

Learning partial-order macros from solutions

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED METHODS; DOMAIN STRUCTURE; HEURISTIC TECHNIQUES; PROBLEM INSTANCES; PROBLEM SOLUTIONS; RANKING PROCEDURES; STRUCTURE INFORMATION; TRAINING PROCESS;

EID: 31144460882     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (24)

References (20)
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    • Botea, A.1    Miiller, M.2    Schaeffer, J.3
  • 5
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    • Marvin: Macro actions from reduced versions of the instance
    • Coles, A. and Smith, A. 2004. Marvin: Macro Actions from Reduced Versions of the Instance. In 4th International Planning Competition, 24-26.
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    • Coles, A.1    Smith, A.2
  • 6
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    • STRIPS: A new approach to the application of theorem proving to problem solving
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    • (1971) Artificial Intelligence , vol.5 , Issue.2 , pp. 189-208
    • Fikes, R.E.1    Nilsson, N.2
  • 8
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    • The ff planning system: Fast plan generation through heuristic search
    • Hoffmann, J., and Nebel, B. 2001. The FF Planning System: Fast Plan Generation Through Heuristic Search. JAIR 14:253-302.
    • (2001) JAIR , vol.14 , pp. 253-302
    • Hoffmann, J.1    Nebel, B.2
  • 10
    • 0000148778 scopus 로고
    • A Heuristic approach to the discovery of macro-operators
    • Iba, G. A. 1989. A Heuristic Approach to the Discovery of Macro-Operators. Machine Learning 3(4):285-317.
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    • Iba, G.A.1
  • 11
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    • Sokoban: Enhancing single- Agent search using domain knowledge
    • Junghanns, A. and Schaeffer, J. 2001. Sokoban: Enhancing Single-Agent Search Using Domain Knowledge. Artificial Intelligence 129( 1-2): 219-251.
    • (2001) Artificial Intelligence , vol.129 , Issue.1-2 , pp. 219-251
    • Junghanns, A.1    Schaeffer, J.2
  • 12
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    • Morgan Kaufmann. chapter Supporting Flexible Plan Reuse
    • Kambhampati, S. 1993. Machine Learning Methods for Planning. Morgan Kaufmann. chapter Supporting Flexible Plan Reuse, 397-434.
    • (1993) Machine Learning Methods for Planning , pp. 397-434
    • Kambhampati, S.1
  • 13
    • 0022045044 scopus 로고
    • Macro-operators: A weak method for learning
    • Korf, R. E. 1985. Macro-operators: A weak method for learning. Artificial Intelligence 26(1):35-77.
    • (1985) Artificial Intelligence , vol.26 , Issue.1 , pp. 35-77
    • Korf, R.E.1
  • 14
    • 0031200240 scopus 로고    scopus 로고
    • Engineering and compiling planning domain models to promote validity and efficiency
    • Mc Cluskey, T. L. and Porteous, J. M. 1997. Engineering and Compiling Planning Domain Models to Promote Validity and Efficiency. Artificial Intelligence 95:1-65.
    • (1997) Artificial Intelligence , vol.95 , pp. 1-65
    • Mc Cluskey, T.L.1    Porteous, J.M.2
  • 15
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    • Selectively generalizing plans for problem-solving
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  • 17
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    • Reviving partial order planning
    • Nebel, B ed
    • Nguyen, X. and Kambhampati, S. 2001. Reviving Partial Order Planning. In Nebel, B ed IJCA1-01, 459-466.
    • (2001) IJCA1-01 , pp. 459-466
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  • 20
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    • The yahsp planning system: Forward heuristic search with lookahead plans analysis
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.