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7 papers · 1 filter
Symbolic Generalization for On-line Planning
Zhengzhu Feng, Eric A. Hansen, Shlomo Zilberstein
Symbolic representations have been used successfully in off-line planning algorithms for Markov decision processes. We show that they can also improve the performance of on-line pl…
An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Changhe Yuan, Brandon Malone
Recently two search algorithms, A* and breadth-first branch and bound (BFBnB), were developed based on a simple admissible heuristic for learning Bayesian network structures that o…
Sparse Stochastic Finite-State Controllers for POMDPs
Eric A. Hansen
Bounded policy iteration is an approach to solving infinite-horizon POMDPs that represents policies as stochastic finite-state controllers and iteratively improves a controller by…
Most Relevant Explanation: Properties, Algorithms, and Evaluations
Changhe Yuan, Xiaolu Liu, Tsai-Ching Lu +1
Most Relevant Explanation (MRE) is a method for finding multivariate explanations for given evidence in Bayesian networks [12]. This paper studies the theoretical properties of MRE…
Solving Multistage Influence Diagrams using Branch-and-Bound Search
Changhe Yuan, Xiaojian Wu, Eric A. Hansen
A branch-and-bound approach to solving influ- ence diagrams has been previously proposed in the literature, but appears to have never been implemented and evaluated - apparently du…
Improving the Scalability of Optimal Bayesian Network Learning with External-Memory Frontier Breadth-First Branch and Bound Search
Brandon Malone, Changhe Yuan, Eric A. Hansen +1
Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A*…