Coherence, Explanation, and Bayesian Networks
This paper discusses the relevance of coherence to deciding between competing explanations. It provides a basic definition of coherence in probabilistic terms, which yields a coherence measure and can easily be extended from the coherence of two beliefs to the coherence of n beliefs. Using this definition, the coherence of a set of beliefs can be obtained by making simple extensions to a Bayesian network. The basic definition suggests a strategy for revising beliefs since a decision to reject a belief can be based on maximising the coherence of the remaining beliefs. It is also argued that coherence can provide a suitable approach for inference to the best explanation.
Unable to display preview. Download preview PDF.
- 1.Harman, G.: Change in view: Principles of reasoning. Cambridge, MA: MIT Press (1986).Google Scholar
- 2.Lipton, P.: Inference to the Best Explanation. London: Routledge (1991).Google Scholar
- 3.Lycan, W.: Judgement and Justification. Cambridge: Cambridge University Press (1988).Google Scholar
- 6.Thagard, P.: Probabilistic Networks and Explanatory Coherence. Cognitive Science Quarterly 1 93–116 (2000).Google Scholar
- 7.Neapolitan, R.: Probabilistic Reasoning in Expert Systems, New York: John Wiley (1990).Google Scholar
- 8.Pearl, J.: Probabilistic Reasoning in Intelligent Systems, San Mateo: Morgan Kaufman (1988).Google Scholar
- 10.Bovens, L., Hartmann, S.: Coherence, Belief Expansion and Bayesian Networks, Proceedings of the 8th International Workshop on Non-Monotonic Reasoning, NMR’2000 Breckenridge, Colorado, USA, April 9–11, (2000).Google Scholar
- 11.Glass, D.H.: A Probabilistic Account of Coherence, Proceedings of the International Conference on AI, IC-AI’2002 Las Vegas, Nevada, USA, June 24–27, (2002).Google Scholar
- 12.McSherry, D.: Sequential Diagnosis in the Independence Bayesian Framework, in D. Bustard, W. Liu, R Sterritt (Eds.) Soft-Ware 2002:Computing in an Imperfect Word, 217–231, Springer (2002).Google Scholar