Maximal-confirmation diagnoses

Knowledge-Based Systems

Volumn 24, Issue 4, 2011, Pages 467-477

  Roos, Nico ^a  

^a MAASTRICHT UNIVERSITY   (Netherlands)

Author keywords

Accuracy; Confirmation; Model based diagnosis; Preferences; Simple temporal networks

Indexed keywords

ACCURACY; CONFIRMATION; MODEL-BASED DIAGNOSIS; PREFERENCES; SIMPLE TEMPORAL NETWORKS;

FORECASTING;

MATHEMATICAL MODELS;

EID: 79952451559     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2010.12.002     Document Type: Article

References (43)

1. N. Roos, Preferring maximum confirmation diagnoses, in: BNAIC 2009, 2009.
2. R. Reiter A theory of diagnosis from first principles Artificial Intelligence 32 1987 57 95
3. J. de Kleer, and B.C. Williams Diagnosing multiple faults Artificial Intelligence 32 1987 97 130
4. D. Poole, Representing knowledge for logic-based diagnosis, in: International Conference on Fifth Generation Computer Systems, 1988, pp. 1282-1290.
5. J. de Kleer, B.C. Williams, Diagnosing with behaviour modes, in: IJCAI 89, 1989, pp. 104-109.
6. L. Console, and P. Torasso Hypothetical reasoning in causal models International Journal of Intelligence Systems 5 1990 83 124 (Pubitemid 20657940)
7. L. Console, and P. Torasso A spectrum of logical definitions of model-based diagnosis Computational Intelligence 7 1991 133 141
8. J. de Kleer, A. Mackworth, and R. Reiter Characterizing diagnoses and systems Artificial Intelligence 56 1992 197 222
9. P. Struss, What's in SD?: Towards a theory of modeling for diagnosis, in: W. Hamscher, L. Console, J. de Kleer (Eds.), Readings in Model-Based Diagnosis, 1992, pp. 419-449.
10. G. Tota, P. Torasso, Automatic abstraction in component-based diagnosis driven by system observability, in: IJCAI-03, 2003, pp. 394-400.
11. P. Dague, O. Jehl, P. Deves, P. Luciani, P. Taillibert, When oscillators stop oscillating, in: W. Hamscher, L. Console, J. de Kleer (Eds.), Readings in Model-Based Diagnosis, 1992, pp. 235-241.
12. D.J. Goldstone, Controlling inequality reasoning in a TMS-based analog diagnosis system, in: W. Hamscher, L. Console, J. de Kleer (Eds.), Readings in Model-Based Diagnosis, 1992, pp. 206-211.
13. W. Hamscher, ACP: reason maintenance and inference control for constraint propagation over intervals, in: AAAI, 1991, pp. 506-511.
14. J. Mauss, M. Tatar, Computing minimal conflicts for rich constraint languages, in: Thirteenth International Workshop on Principles of Diagnosis [DX-2002], 2002, pp. 170-181.
15. R. Dechter, I. Meiri, and J. Pearl Temporal constraint networks Artificial Intelligence 49 1991 61 95 (Pubitemid 21679936)
16. N. Roos, C. Witteveen, Diagnosis of simple temporal networks, in: ECAI 2008, 2008, pp. 593-597.
17. N. Roos, C. Witteveen, Models and methods for plan diagnosis, in: Formal Approaches to Multi-Agent Systems (FAMAS'06), 2006.
18. N. Roos, and C. Witteveen Models and methods for plan diagnosis Journal of Autonomous Agents and Multi-Agent Systems 19 2009 30 52
19. M.-O. Cordier, When abductive diagnosis fails to explain too precise observations: an extended spectrum of definitions based on abstracting observations, in: International Workshop on Principles of Diagnosis [DX-1998], 1998, pp. 24-31.
20. D. Spiegelhalter, Probabilistic reasoning in predictive expert systems, Uncertainty in Artificial Intelligence, 1986, pp. 47-67.
21. J. Pearl Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference 1988 Morgan Kaufman San Mateo, California
22. N.L. Zhang, and D. Poole Exploiting causal independence in bayesian network inference Journal of Artificial Intelligence Research 5 1996 301 328 (Pubitemid 126646171)
23. C.G. Cassandras, and S. Lafortune Introduction to Discrete Event Systems 1999 Kluwer Academic Publishers.
24. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis Diagnosability of discrete event systems IEEE Transactions on Automatic Control 40 1995 1555 1575
25. M. Sampath, R. Sengupta, S. Lafortune, K. Sinnamohideen, and D. Teneketzis Failure diagnosis using discrete event models IEEE Transactions on Control Systems Technology 4 1996 105 124 (Pubitemid 126782836)
26. P. Baroni, G. Lamperti, P. Poglianob, and M. Zanella Diagnosis of large active systems Artificial Intelligence 110 1999 135 183
27. Y. Pencolé, and M. Cordier A formal framework for the decentralised diagnosis of large scale discrete event systems and its application to telecommunication networks Artificial Intelligence 164 2005 121 170 (Pubitemid 40454432)
28. D. Poole A methodology for using a default and abductive reasoning system International Journal of Intelligent Systems 5 1990 521 548
29. J. de Kleer, Focusing on probable diagnosis, in: AAAI 91, 1991, pp. 842-848.
30. I. Flesch, P. Lucas, T. van der Weide, Conflict-based diagnosis: Adding uncertainty to model-based diagnosis, in: IJCAI 2007, 2007, pp. 380-385.
31. S. Russell, and P. Norvig Artificial Intelligence, a modern approach (third edition) 2010 Pearson Education, Inc.
32. F. Bacchus, A.J. Grove, J.Y. Halpern, and D. Koller From statistical knowledge bases to degrees of belief Artificial Intelligence 87 1996 75 143 (Pubitemid 126371217)
33. A.J. Grove, J.Y. Halpern, and D. Koller Random worlds and maximum entropy Journal of Artificial Intelligence Research 2 1994 33 88
34. H.E. Kyburg Combinatorial semantics: semantics for frequent validity Computational Intelligence 13 1997 215 257
35. N. Roos How to reason with uncertain knowledge B. Bouchon-Meurier, R.R. Yager, L.A. Zadeh, Uncertainty in Knowledge Bases, IPMU'90 1991 Springer-Verlag 403 412
36. G. Friedrich, G. Gottlob, W. Nejdl, Physical impossibility instead of fault models, in: AAAI 90, 1990, pp. 331-336.
37. T. Bylander, D. Allemang, M.C. Tanner, J.R. Josephson, The computational complexity of abduction, Artificial Intelligence 49 (1-3) (1991) 25-60.
38. T. Eiter, and G. Gottlob The complexity of logic-based abduction Journal of the ACM 42 1 1995 3 42 http://doi.acm.org/10.1145/200836.200838
39. G. Tota, and P. Torasso On the use of OBDDs in model-based diagnosis: An approach based on the partition of the model Knowledge-Based Systems 19 2006 316 323 (Pubitemid 44209227)
40. E. Schwalb, and R. Dechter Processing disjunctions in temporal constraint networks Artificial Intelligence 93 1-2 1997 29 61 10.1016/S0004-37097)00009-X (Pubitemid 127401112)
41. L. Xu, and B.Y. Choueiry A new efficient algorithm for solving the simple temporal problem Proceedings of the 10th Int. Symposium on Temporal Representation and Reasoning and 4th International Conference on Temporal Logic 2003 IEEE Computer Society 210 220
42. 3C: A new algorithm for the simple temporal problem, in: ICAPS 2008, 2008, pp. 256-263.
43. J. de Kleer Dynamic domain abstraction through meta-diagnosis Abstraction, Reformulation, and Approximation (SARA) LNAI 4612 2007 Springer 109 123 (Pubitemid 47290221)