Lazy Incremental Learning of Control Knowledge for Efficiently Obtaining Quality Plans
暂无分享,去创建一个
[1] Oren Etzioni,et al. PRODIGY: an integrated architecture for planning and learning , 1991, SGAR.
[2] R. Bronson. The Knowledge , 2002, Annals of Internal Medicine.
[3] R. Mooney,et al. Explanation-Based Learning: An Alternative View , 1986, Machine Learning.
[4] Tom M. Mitchell,et al. Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.
[5] Oren Etzioni,et al. PRODIGY4.0: The Manual and Tutorial , 1992 .
[6] Ray Bareiss,et al. Concept Learning and Heuristic Classification in WeakTtheory Domains , 1990, Artif. Intell..
[7] Manuela Veloso. Nonlinear problem solving using intelligent casual-commitment , 1989 .
[8] S. Kambhampati,et al. Learning Explanation-Based Search Control Rules for Partial Order Planning , 1994, AAAI.
[9] Pat Langley,et al. Learning Effective Search Heuristics , 1983, IJCAI.
[10] Allen Newell,et al. Chunking in Soar , 1986 .
[11] Raymond J. Mooney,et al. Combining FOIL and EBG to Speed-up Logic Programs , 1993, IJCAI.
[12] Oren Etzioni,et al. DYNAMIC: A New Role for Training Problems in EBL , 1992, ML.
[13] D. Kibler,et al. Instance-based learning algorithms , 2004, Machine Learning.
[14] N. Nilsson,et al. Readings in Artificial Intelligence , 1981 .
[15] James A. Hendler,et al. Massively parallel support for case-based planning , 1993, Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications.
[16] William W. Cohen. Learning Approximate Control Rules of High Utility , 1990, ML.
[17] Lawrence Birnbaum,et al. Proceedings of the eighth international workshop on Machine learning , 1991 .
[18] Leslie G. Valiant,et al. A theory of the learnable , 1984, CACM.
[19] J. R. Quinlan. Learning Logical Definitions from Relations , 1990 .
[20] Nils J. Nilsson,et al. Artificial Intelligence , 1974, IFIP Congress.
[21] Neeraj Bhatanagar. Learning by incomplete explanations of failures in recursive domains , 1992, ICML 1992.
[22] Manuela M. Veloso,et al. The Need for Different Domain-independent Heuristics , 1994, AIPS.
[23] John R. Anderson,et al. Machine learning - an artificial intelligence approach , 1982, Symbolic computation.
[24] Ingrid Zukerman,et al. Learning Search Control Rules for Planning: An Inductive Approach , 1991, ML.
[25] Manuela M. Veloso,et al. Flexible Strategy Learning: Analogical Replay of Problem Solving Episodes , 1994, AAAI.
[26] Manuela M. Veloso,et al. Learning strategy knowledge incrementally , 1994, Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94.
[27] David Ruby,et al. Learning Episodes for Optimization , 1992, ML.
[28] Jaime G. Carbonell,et al. Learning effective search control knowledge: an explanation-based approach , 1988 .
[29] Manuela M. Veloso,et al. Linkability: Examining Causal Link Commitments in Partial-order Planning , 1994, AIPS.
[30] Prasad Tadepalli,et al. Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem , 1989, IJCAI.
[31] Jaime G. Carbonell,et al. Control Knowledge to Improve Plan Quality , 1994, AIPS.
[32] Richard Fikes,et al. Learning and Executing Generalized Robot Plans , 1993, Artif. Intell..
[33] Alan Bundy,et al. Proceedings of the Eighth International Joint Conference on Artificial Intelligence , 1983 .
[34] Kristian J. Hammond,et al. Case-Based Planning: Viewing Planning as a Memory Task , 1989 .
[35] Manuela M. Veloso,et al. Incremental Learning of Control Knowledge for Nonlinear Problem Solving , 1994, ECML.
[36] Subbarao Kambhampati,et al. Explanation-Based Generalization of Partially Ordered Plans , 1991, AAAI.
[37] Allen Newell,et al. Chunking in Soar: The anatomy of a general learning mechanism , 1985, Machine Learning.
[38] Pavel Brazdil,et al. Proceedings of the European Conference on Machine Learning , 1993 .
[39] James A. Hendler,et al. Massively parallel support for case-based planning , 1994, IEEE Expert.
[40] STEVEN MINTON,et al. A reply to Zito-Wolf's book review ofLearning search control knowledge: An explanation-based approach , 2004, Machine Learning.
[41] Tara A. Estlin,et al. Hybrid learning of search control for partial-order planning , 1996 .
[42] Tom M. Mitchell,et al. Learning by experimentation: acquiring and refining problem-solving heuristics , 1993 .
[43] Oren Etzioni,et al. Why EBL Produces Overly-Specific Knowledge: A Critique of the PRODIGY Approaches , 1992, ML.
[44] David L. Waltz,et al. Toward memory-based reasoning , 1986, CACM.
[45] Eugene Fink,et al. Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..
[46] Daniel S. Weld,et al. A Domain-Independent Algorithm for Plan Adaptation , 1994, J. Artif. Intell. Res..
[47] J. Ross Quinlan,et al. Learning logical definitions from relations , 1990, Machine Learning.
[48] James A. Hendler,et al. Flexible reuse and modification in hierarchical planning: a validation structure-based approach , 1989 .
[49] Richard Waldinger,et al. Achieving several goals simultaneously , 1977 .
[50] Peter Clark,et al. Lazy Partial Evaluation: An Integration of Explanation-Based Generalization and Partial Evaluation , 1992, ML.
[51] Manuela M. Veloso,et al. Planning and Learning by Analogical Reasoning , 1994, Lecture Notes in Computer Science.