On the Generation of Alternative Explanations with Implications for Belief Revision

Mar 20, 2013

Eugene Santos Jr

Mar 20, 2013

Eugene Santos Jr

* Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)

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Belief Updating by Enumerating High-Probability Independence-Based Assignments

Feb 27, 2013

Eugene Santos Jr., Solomon Eyal Shimony

Independence-based (IB) assignments to Bayesian belief networks were originally proposed as abductive explanations. IB assignments assign fewer variables in abductive explanations than do schemes assigning values to all evidentially supported variables. We use IB assignments to approximate marginal probabilities in Bayesian belief networks. Recent work in belief updating for Bayes networks attempts to approximate posterior probabilities by finding a small number of the highest probability complete (or perhaps evidentially supported) assignments. Under certain assumptions, the probability mass in the union of these assignments is sufficient to obtain a good approximation. Such methods are especially useful for highly-connected networks, where the maximum clique size or the cutset size make the standard algorithms intractable. Since IB assignments contain fewer assigned variables, the probability mass in each assignment is greater than in the respective complete assignment. Thus, fewer IB assignments are sufficient, and a good approximation can be obtained more efficiently. IB assignments can be used for efficiently approximating posterior node probabilities even in cases which do not obey the rather strict skewness assumptions used in previous research. Two algorithms for finding the high probability IB assignments are suggested: one by doing a best-first heuristic search, and another by special-purpose integer linear programming. Experimental results show that this approach is feasible for highly connected belief networks.
Feb 27, 2013

Eugene Santos Jr., Solomon Eyal Shimony

* Appears in Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI1994)

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Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks

Feb 13, 2013

Eugene Santos Jr., Solomon Eyal Shimony, Edward Williams

Feb 13, 2013

Eugene Santos Jr., Solomon Eyal Shimony, Edward Williams

* Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)

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Cost-Sharing in Bayesian Knowledge Bases

Feb 06, 2013

Solomon Eyal Shimony, Carmel Domshlak, Eugene Santos Jr

Feb 06, 2013

Solomon Eyal Shimony, Carmel Domshlak, Eugene Santos Jr

* Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)

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