Justifying multiply sectioned Bayesian networks

Proceedings - 4th International Conference on MultiAgent Systems, ICMAS 2000

Volumn , Issue , 2000, Pages 349-356

  Xiang, Y ^a   Lesser, V ^a  

^a UNIVERSITY OF MASSACHUSETTS   (United States)

Author keywords

belief network; communication; Decentralized interpretation; organization structure; uncertain reasoning

Indexed keywords

COMMUNICATION; MULTI AGENT SYSTEMS;

AGENT ORGANIZATION; DECENTRALIZED INTERPRETATION; LOCAL OBSERVATIONS; MULTIPLE AGENTS; MULTIPLY SECTIONED BAYESIAN NETWORKS; ORGANIZATION STRUCTURES; PARTIAL KNOWLEDGE; UNCERTAIN REASONING;

BAYESIAN NETWORKS;

EID: 84962053213     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMAS.2000.858473     Document Type: Conference Paper

References (18)

1. E. Castillo, J. Gutierrez, and A. Hadi. Expert Systems and Probabilistic Network Models. Springer, 1997.
2. D. Geiger and D. Heckerman. Knowledge representation and inference in similarity networks and Bayesian multinets. Artificial Intelligence, 82:45-72, 1996.
3. M.N. Huhns and D.M. Bridgeland. Multiagent truth maintenance. IEEE Trans. Sys., Man, and Cybernetics, 21(6):1437-1445, 1991.
4. F.V. Jensen. An introduction to Bayesian networks. UCL Press, 1996.
5. F.V. Jensen, S.L. Lauritzen, and K.G. Olesen. Bayesian updating in causal probabilistic networks by local computations. Computational Statistics Quarterly, (4):269-282, 1990.
6. U. Kjaerulff. Nested junction trees. In Proc. 13th Conf. on Uncertainty in Artificial Intelligence, pages 294-301, Providence, Rhode Island, 1997.
7. D. Koller and A. Pfeffer. Object-oriented Bayesian networks. In D. Geiger and P.P. Shenoy, editors, Proc. 13th Conf. on Uncertainty in Artificial Intelligence, pages 302-313, Providence, Rhode Island, 1997.
8. W. Lam. Abstraction in Bayesian belief networks and automatic discovery from past inference sessions. In Proc. of AAAI, pages 257-262, 1994.
9. S.L. Lauritzen and D.J. Spiegelhalter. Local computation with probabilities on graphical structures and their application to expert systems. J. Royal Statistical Society, Series B, (50):157-244, 1988.
10. C.L. Mason and R.R. Johnson. DATMS: a framework for distributed assumption based reasoning. In L. Gasser and M.N. Huhns, editors, Distributed Artificial Intelligence II, pages 293-317. Pitman, 1989.
11. R.E. Neapolitan. Probabilistic Reasoning in Expert Systems. John Wiley and Sons, 1990.
12. H.P. Nii. Blackboard systems: the blackboard model of problem solving and the evolution of blackboard architectures. AI Magazine, (2):38-53, 1986.
13. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
14. D. Poole, A. Mackworth, and R. Goebel. Computational Intelligence: A Logical Approach. Oxford University Press, 1998.
15. R.D. Shachter, B. D'Ambrosio, and B.A. Del Favero. Symbolic probabilistic inference in belief networks. In Proc. 8th Natl. Conf. on Artificial Intelligence, pages 126-131, 1990.
16. Y. Xiang. A probabilistic framework for cooperative multi-agent distributed interpretation and optimization of communication. Artificial Intelligence, 87(1-2):295-342, 1996.
17. Y. Xiang and H. Geng. Distributed monitoring and diagnosis with multiply sectioned Bayesian networks. In Proc. AAAI Spring symposium on AI in Eguipment Service, Maintenance and Support, 1999.
18. Y. Xiang, D. Poole, and M. P. Beddoes. Multiply sectioned Bayesian networks and junction forests for large knowledge based systems. Computational Intelligence, 9(2):171-220, 1993.