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A New Algorithm for Finding MAP Assignments to Belief Networks

Mar 27, 2013

Solomon Eyal Shimony, Eugene Charniak

Mar 27, 2013

Solomon Eyal Shimony, Eugene Charniak

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Doing Better Than UCT: Rational Monte Carlo Sampling in Trees

Jul 25, 2012

David Tolpin, Solomon Eyal Shimony

Jul 25, 2012

David Tolpin, Solomon Eyal Shimony

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Observation Subset Selection as Local Compilation of Performance Profiles

Jun 13, 2012

Yan Radovilsky, Solomon Eyal Shimony

Jun 13, 2012

Yan Radovilsky, Solomon Eyal Shimony

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Rational Value of Information Estimation for Measurement Selection

Apr 16, 2010

David Tolpin, Solomon Eyal Shimony

Apr 16, 2010

David Tolpin, Solomon Eyal Shimony

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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

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Estimating the Probability of Meeting a Deadline in Hierarchical Plans

Dec 24, 2017

Liat Cohen, Solomon Eyal Shimony, Gera Weiss

Dec 24, 2017

Liat Cohen, Solomon Eyal Shimony, Gera Weiss

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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

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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

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Rational Deployment of Multiple Heuristics in IDA*

Nov 24, 2014

David Tolpin, Oded Betzalel, Ariel Felner, Solomon Eyal Shimony

Nov 24, 2014

David Tolpin, Oded Betzalel, Ariel Felner, Solomon Eyal Shimony

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Selecting Computations: Theory and Applications

Aug 09, 2014

Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony

Aug 09, 2014

Nicholas Hay, Stuart Russell, David Tolpin, Solomon Eyal Shimony

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Generic Preferences over Subsets of Structured Objects

Jan 15, 2014

Maxim Binshtok, Ronen I. Brafman, Carmel Domshlak, Solomon Eyal Shimony

Jan 15, 2014

Maxim Binshtok, Ronen I. Brafman, Carmel Domshlak, Solomon Eyal Shimony

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Towards Rational Deployment of Multiple Heuristics in A*

May 22, 2013

David Tolpin, Tal Beja, Solomon Eyal Shimony, Ariel Felner, Erez Karpas

May 22, 2013

David Tolpin, Tal Beja, Solomon Eyal Shimony, Ariel Felner, Erez Karpas

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