분석적 학습

참고문헌

1. Readings in Planning Allen,J.;Hendler,J.;Tate,A.
2. Proceedings of AAAI-92 Learning 10,000 chunks : What's it like out there? Doorenbos,B.;Tambe,M.;Newell,A.
3. Artificial Intelligence v.40 Introduction : Paradigms for Machine Learning Carbonell,J.G.
4. Proceedings of AAAI-86 The FERMI system : Inducing iterative macro-operators from experience Cheng,P.W.;Carbonell,J.G.
5. Machine Learning v.1 no.2 Explanation-based learning : An alternative view DeJong,G.;Mooney,R.
6. Proceedings of the Eighth National Conference on Artificial Intelligence Why PRODIGY/EBL works Etzioni,O.
7. Ph. D. Thesis, School of Computer Science, Carnegie Mellon University A Structural Theory of Explanation-Based Learning Etzioni,O.
8. Artificial Intelligence v.2 no.3 STRIPS : A new approach to the application of theorem proving to problem solving Fikes,R.E.;Nilsson,N.
9. Artificial Intelligence v.3 Learning and executing generalized robot plans Fikes,R.E.;Hart,P.E.;Nilsson,N.J.
10. Machine Learning v.4 A study of explanation-based methods for inductive learning Flann,N.S.;Deitterich,T.G.
11. Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling, and Control A framework for evaluating search control strategies Gratch,J.M.;DeJong,G.F.
12. Proceedings of IJCAI-89 Incorporating redundant learned rules : A preliminary formal analysis of EBL Greiner,R.;Likuski,J.
13. Proceedings of the Third International Conference on Machine Learning Purpose-directed analogy : A summary of current research Kedar-Cabelli,S.T.;McCarry,L.T.
14. Proceedings of the Tenth International Conference on Machine Learning Constraining learning with search control Kim,J.;Rosenbloom,P.S.
15. Artificial Intelligence v.26 Macro-operators : A weak method for learning Korf,R.E.
16. Artificial Intelligence v.33 Planning as search Korf,R.E.
17. Machine Learning v.1 Chunking in Soar : The anatomy of a general learning mechanism Laird,J.E.;Rosenbloom,P.S.;Newell,A.
18. Artificial Intelligence v.33 no.1 Soar : An architecture for general intelligence Laird,J.E.;Newell,A.;Rosenbloom,P.S.
19. Proceedings of AAAI-88 Rocovery from incorrect knowledge in Soar Laird,J.E.
20. Proceedings of the Second Pacific Rim International Conference on Artificial Intelligence Creating and coordinating multiple planning methods Lee,S.;Rosenbloom,P.S.
21. Proceedings of the Eleventh National Conference on Artificial Intelligence Granularity in multi-method planning Lee,S.;Rosenbloom,P.S.
22. Machnie Learning : An Artificial Intelligence Approach v.2 Concept learning in a rich input domain : Generalization-based memory Lebowitz,M.;R.S.Michalski(ed.);J.G.Carbonell(ed.);T.M.Mitchell(ed.)
23. Proceedings of IJCAI-89 Uitilization Filtering: A method for reducing the inherent harmfulness of deductively learned knowledge Markovitch,S.;Scott,P.D.
24. Ph. D. Thesis, Computer Science Department, Carnegie Mellon University Learning Effective Search Control Knowledge : An Explanation-Based Approach Minton,S.
25. Artificial Intelligence v.40 Explanation-based learning : A problem solving perspective Minton,S.;James,G.C.;Knoblock,C.A.;Kuokka,D.R.;Etzioni,O.;Gil,Y.
26. Technical Report CMU-CS-89-146, School of Computer Science, Carnegie Mellon University PRODIGY 2.0 : The manual and tutorial Minton,S.;Knoblock,C.A.;Kuokka,D.R.;Gil,Y.;Joseph,R.L.;Carbonell,J.G.
27. Machine Learning v.1 no.1 Explanation-based generalization : A unifying view Mitchell,T.M.;Keller,R.M.;Kedar-Cabelli,S.T.
28. Proceedings on IJCAI-89 The effect of rule use on the utility of explanation-based leaning Mooney,R.J.
29. Unified Theories of Cognition Newell,A.
30. Carnegie Mellon Computer Science : A 25-Year Commemorative Formulating the problem space computation model Newell,A.;Yost,G.R.;Laird,J.E.;Rosenbloom,P.S.;Altmann,E.;R.F.Rashid(ed.)
31. Principle of Artificial Intelligence Nilsson,N.J.
32. Doctoral dissertation, Computer Science Department, University of California Learning causal relationship An integration of empirical and explanat-based generalizer Pazzani,M.J.
33. Proceedings of the Fifth National Conference on Artificial Intelligence Mapping explanation-based generalization onto Soar Rosenbloom,P.S.;Laird,J.E.
34. Proceedings of the Workshop on Innovative Approaches to Planning, Scheduling, and Control Responding to impasse in memory driven behavior : A framework for planning Rosenbloom,P.S.;Lee,S.;Unruh,A.
35. Artificial Intelligence v.47 A Preliminary analysis of the Soar architecture as a basis of general intelligence Rosenbloom,P.S.;Laird,J.E.;Newell,A.
36. The Soar Papers Rosenbloom,P.S.(ed.);Laird,J.E.(ed.);Newell,A.(ed.)
37. Machine Learning Methods for Planning Bias in planning and explanation-based learning Rosenbloom,P.S.;Lee,S.;Unruh,A.;S.Minton(ed.)
38. Machine Learning Induction, Analogy and Discovery Chimpman,S.(ed.);Meyrowitz,A.(ed.)
39. the 13th Annual Conference of The Cognitive Science Society Empirical and analytical performance of iterative operators Shell,P.;Carbonell,J.
40. Machine Learning v.5 Acquiring recursive and iterative concepts with explanation-based learning Shavlik,J.W.
41. Machine Learning v.5 no.3 The problem of expensive chunks and its solution by restricting expressiveness Tambe,M.;Newell,A.;Rosenbloom,P.S.
42. Technical Report CMU-CS-89-210, School of Computer Science, Carnegie Mellon University Nonlinear problem solving using intelligent casual-commitment Veloso,M.
43. Machine Intelligence v.8 Achieving several goals simultaneously Waldinger,R.;E.Elcock;(ed.);Michie,D.(ed.)