Perceptually grounded self-diagnosis and self-repair of domain knowledge

Knowledge-Based Systems

Volumn 27, Issue , 2012, Pages 281-301

  Jones, Joshua K ^a   Goel, Ashok K ^a  

^a Georgia Institute of Technology   (United States)

Author keywords

Compositional classification; Knowledge engineering; Knowledge representation; Metareasoning; Symbol grounding

Indexed keywords

DOMAIN KNOWLEDGE; EMPIRICAL EVALUATIONS; EXPERIENTIAL LEARNING; INTELLIGENT SOFTWARE AGENT; META-KNOWLEDGE; METAREASONING; SELF ADAPTATION; SELF-DIAGNOSIS; SELF-REPAIR; SYMBOL GROUNDING;

INTELLIGENT AGENTS; KNOWLEDGE ENGINEERING; REPAIR; SOFTWARE AGENTS;

KNOWLEDGE REPRESENTATION;

EID: 84855939650     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2011.09.012     Document Type: Article

References (54)

1. M. Minsky, Steps towards artificial intelligence, in: E.A. Feigenbaum, J. Feldman (Eds.), Computers and Thought, 1963, pp. 406-450.
2. M. Minsky, P. Singh, and A. Sloman The St. Thomas common sense symposium: designing architectures for human-level intelligence AI Magazine 25 2004 113 124
3. R. Brachman Systems that know what they are doing IEEE Intelligent Systems 2002 67 71
4. M. Cox, and A. Raja Metareasoning: Thinking about Thinking 2011 MIT Press
5. M.T. Cox Metacognition in computation: a selected research review Artificial Intelligence 169 2005 104 141
6. M.L. Anderson, and T. Oates A review of recent research in metareasoning and metalearning AI Magazine 28 2007 7 16
7. M.T. Cox, A. Raja, Metareasoning: A Manifesto, Technical Report, BBN TM-2028, BBN Technologies, 2007.
8. A.K. Goel, N. Soundararajan, B. Chandrasekaran, Complexity in classificatory reasoning, in: AAAI, pp. 421-425.
9. B. Chandrasekaran, and A. Goel From numbers to symbols to knowledge structures: artificial Intelligence perspectives on the classification task IEEE Transactions on Systems, Man and Cybernetics 18 1988 415 424
10. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification and Scene Analysis 1973 Wiley New York
11. W.J. Clancey Heuristic classification Artificial Intelligence 27 1985 289 350
12. A. Goel, and T. Bylander Computational feasibility of structured matching IEEE Transactions on Pattern Analysis and Machine Intelligence 11 1989 1312 1316
13. S.J. Russell, Tree-structured bias, in: AAAI, pp. 641-645.
14. B. Chandrasekaran, Generic tasks as building blocks for knowledge-based systems: the diagnosis and routine design examples, in: The Knowledge Engineering Review, vol. 3, 1988, pp. 183-210.
15. A. Samuel Some studies in machine learning using the game of checkers IBM Journal of Research and Development 3 1957 210 229
16. T. Bylander, T. Johnson, and A. Goel Structured matching: a task-specific technique for making decisions Knowledge Acquisition 3 1991 1 20
17. D.E. Rumelhart, and J.L. Mcclelland Parallel Distributed Processing: Explorations in the Microstructure of Cognition vols. 1 & 2 1986 MIT Press
18. J. Jones, Empirically-Based Self-Diagnosis and Repair of Domain Knowledge, Ph.D. Thesis, Georgia Institute of Technology, 2010.
19. R. Kohavi The power of decision tables Proceedings of the European Conference on Machine Learning 1995 Springer Verlag 174 189
20. Leading indicators of employment, Australian Economic Indicators 1350.0, 2004.
21. R.H. Webb, and T.S. Rowe An index of leading indicators for inflation Economic Quarterly 1995 75 96
22. RBC US leading economic indicator, Economics Department, RBC Financial Group, 2005.
23. P. Tadepalli, S.J. Russell, Learning from examples and membership queries with structured determinations, in: Machine Learning, vol. 32, pp. 245-295.
24. S. Whiteson, N. Kohl, R. Miikkulainen, and P. Stone Evolving keepaway soccer players through task decomposition Machine Learning 59 1 2005 5 30
25. A.D. Shapiro Structured Induction in Expert Systems 1987 Addison-Wesley Longman Publishing Co., Inc. Boston, MA, USA
26. G.G. Towell, and J.W. Shavlik Knowledge-based artificial neural networks Artificial Intelligence 70 1994 119 165
27. T.M. Mitchell, S.B. Thrun, Explanation-based neural network learning for robot control, in: C.L. Giles, S.J. Hanson, J.D. Cowan (Eds.), Advances in Neural Information Processing Systems 5, Proceedings of the IEEE Conference in Denver, Morgan Kaufmann, San Mateo, CA, 1993.
28. M.I. Jordan, R.A. Jacobs, Hierarchical Mixtures of Experts and the EM Algorithm, Technical Report AIM-1440, 1993.
29. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufman San Mateo, CA
30. M. Cox, and A. Ram Introspective multistrategy learning: on the construction of learning strategies Artificial Intelligence 112 1999 1 55
31. E. Stroulia, and A.K. Goel Evaluating problem-solving methods in evolutionary design: the autognostic experiments International Journal of Human-Computer Studies 51 1999 825 847 Special Issue on Evaluation Methodologies
32. L. Birnbaum, G. Collins, M. Freed, B. Krulwich, Model-based diagnosis of planning failures, in: Proceedings of the Eighth National Conference on Artificial Intelligence, 1990, pp. 318-323.
33. S. Fox, D.B. Leake, Using introspective reasoning to refine indexing, in: Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence, 1995.
34. S. Fox, and D. Leake Introspective reasoning for index refinement in case-based reasoning Journal of Experimental and Theoretical Artificial Intelligence 13 2001 63 88
35. B.C. Williams, and P.P. Nayak Immobile robots: AI in the new millennium AI Magazine 17 1996 16 35
36. J.W. Murdock, and A.K. Goel Learning about constraints by reflection AI'01: Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence 2001 Springer-Verlag London, UK 131 140
37. W. Murdock, A.K. Goel, Localizing planning using functional process models, in: Proceedings of the Thirteenth International Conference on Automated Planning and Scheduling (ICAPS-03), 2003.
38. J.W. Murdock, and A.K. Goel Meta-case-based reasoning: self-improvement through self-understanding Journal of Experimental and Theoretical Artificial Intelligence 20 2008 1 36
39. E. Stroulia, and A. Goel Functional representation and reasoning in reflective systems Journal of Applied Intelligence 9 1995 101 124 Special Issue on Functional Reasoning
40. E. Stroulia, A.K. Goel, Redesigning a problem-solver's operations to improve solution quality, in: 15th International Joint Conference on Artificial Intelligence (IJCAI-97), 1997, pp. 562-567.
41. J. Jones, C. Parnin, A. Sinharoy, S. Rugaber, A.K. Goel, Teleological metareasoning for automating software adaptation, in: Third IEEE Conference on Self-Adaptive and Self-Organizing Systems, San Francisco, pp. 198-205.
42. A. Goel, and J. Jones Metareasoning for self-adaptation in intelligent agents M. Cox, A. Raja, Metareasoning: Thinking about Thinking 2011 MIT Press
43. R. Laddaga Guest editor's introduction: creating robust software through self-adaptation IEEE Intelligent Systems 14 1999 26 29
44. P. Robertson, and R. Laddaga Model based diagnosis and contexts in self adaptive software O. Babaoglu, M. Jelasity, A. Montresor, C. Fetzer, L. Stefano, A.P.A. van Moorsel, M. van Steen, Self-star Properties in Complex Information Systems vol. 3460 2005 Springer Berlin, Heidelberg 403
45. M.L. Anderson, T. Oates, W. Chong, and D. Perlis The metacognitive loop I: enhancing reinforcement learning with metacognitive monitoring and control for improved perturbation tolerance Journal of Experimental and Theoretical Artificial Intelligence 18 2006 387 411
46. P. Ulam, J. Jones, A.K. Goel, Using model-based reflection to guide reinforcement learning, in: Fourth AAAI Conference on AI in Interactive Digital Entertainment (AIIDE-08), 2008.
47. M.L. Anderson, S. Fults, D.P. Josyula, T. Oates, D. Perlis, S. Wilson, and D. Wright A self-help guide for autonomous systems AI Magazine 29 2008 67 73
48. S. Hanks, M.E. Pollack, and P.R. Cohen Benchmarks, test beds, controlled experimentation, and the design of agent architectures AI Magazine 14 1993 17 42
49. J. Baumeister Advanced empirical testing Knowledge-Based Systems 24 2011 83 94
50. P. Langley Elements of Machine Learning Morgan Kaufmann Series in Machine Learning 1996 Morgan Kaufman
51. T.M. Mitchell Machine Learning 1st ed. 1997 McGraw-Hill, Inc. New York, NY, USA
52. D.H. Fisher Knowledge acquisition via incremental conceptual clustering Machine Learning 1987
53. S. Muggleton Inductive logic programming: theory and methods The Journal of Logic Programming 19-20 1994 629 679
54. Y. Kavurucu, P. Senkul, and I.H. Toroslu Concept discovery on relational databases: new techniques for search space pruning and rule quality improvement Knowledge-Based Systems 23 2010 743 756