Two new notions of abduction in Bayesian networks

Belgian/Netherlands Artificial Intelligence Conference

Volumn , Issue , 2010, Pages

  Kwisthout, Johan ^a  

^a RADBOUD UNIVERSITY NIJMEGEN   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

COGNITIVE SCIENCE; MOST PROBABLE EXPLANATION; PROBABILISTIC INFERENCE;

ARTIFICIAL INTELLIGENCE;

BAYESIAN NETWORKS;

EID: 84874011333     PISSN: 15687805     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. C. L. Baker, R. Saxe, and J. B. Tenenbaum. Action understanding as inverse planning. Cognition, 113(3):329-349, 2009.
2. H. L. Bodlaender, F. van den Eijkhof, and L. C. van der Gaag. On the complexity of the MPA problem in probabilistic networks. In F. van Harmelen, editor, Proceedings of the 15th European Conference on Artificial Intelligence, pages 675-679, 2002.
3. R. H. Cuijpers, H. T. van Schie, M. Koppen, W. Erlhagen, and H. Bekkering. Neural networks. Goals and means in action observation: A computational approach, 19:311-322, 2006.
4. R. G. Downey and M. R. Fellows. Parameterized Complexity. Springer Verlag, Berlin, 1999.
5. K. T. Fann. Peirce's theory of abduction. Springer, Berlin, 1970.
6. M. R. Garey and D. S. Johnson. Computers and Intractability. A Guide to the Theory of NPCompleteness. W. H. Freeman and Co., San Francisco, CA, 1979.
7. J. T. Gill. Computational complexity of Probabilistic Turing Machines. SIAM Journal of Computing, 6(4):675-695, 1977.
8. F. V. Jensen and T. D Nielsen. Bayesian Networks and Decision Graphs. Springer Verlag, New York, NY, second edition, 2007.
9. J. Kwisthout. Most Probable Explanations in Bayesian networks: complexity and tractability. Technical Report ICIS-R10001, Radboud University Nijmegen, 2010.
10. C. Lacave and F. J. Diez. A review of explanation methods for Bayesian networks. The Knowledge Engineering Review, 17(2):107-127, 2002.
11. D. Marr. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. Freeman, New York, NY, 1982.
12. D. Nilsson. An efficient algorithm for finding the M most probable configurations in probabilistic expert systems. Statistics and Computing, 8:159-173, 1998.
13. J. D. Park and A. Darwiche. Complexity results and approximation settings for MAP explanations. Journal of Artificial Intelligence Research, 21:101-133, 2004.
14. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Palo Alto, CA, 1988.
15. S. E. Shimony. Finding MAPs for belief networks is NP-hard. Artificial Intelligence, 68(2):399-410, 1994.
16. N. Stewart, N. Chater, and G. D. A. Brown. Decision by sampling. Cognitive Psychology, 53:1-26, 2006.
17. B. K. Sy. Reasoning MPE to multiply connected belief networks using message-passing. In P. Rosenbloom and P. Szolovits, editors, Proceedings of the Tenth National Conference on Artificial Intelligence, pages 570-576. AAAI Press, Arlington, Va, 1992.
18. I. van Rooij and T. Wareham. Parameterized complexity in cognitive modeling: Foundations, applications and opportunities. The Computer Journal, 51(3):385-404, 2008.
19. E. Vul, N. D. Goodman, T. L. Griffiths, and J. B. Tenenbaum. One and done? optimal decisions from very few samples. In 31st Annual Meeting of the Cognitive Science Society, 2009.
20. C. Yuan, T. Lu, and M. Druzdzel. Annealed MAP. In J. Halpern, editor, Proceedings of the Twentieth Conference Annual Conference on Uncertainty in Artificial Intelligence, pages 628-635. AUAI Press, Arlington, Va, 2004.
21. A. Yuille and D. Kersten. Vision as Bayesian inference: analysis by synthesis? TRENDS in Cognitive Sciences, 10(7):301-308, 2006.