We show that finding the maximum a-posteriori probability (MAP) instantiation of all the random variables given the evidence is NP-hard for Bayesian belief networks, even if the size of the representation is linear in n.Expand

We extend the CP-nets formalism to handle another class of very natural qualitative statements one often uses in expressing preferences in daily life - statements of relative importance of attributes.Expand

Conflict-Based Search (CBS) and its generalization, Meta-Agent CBS are amongst the strongest newly introduced algorithms for Multi-Agent Path Finding. This paper introduces ICBS, an improved version… Expand

We introduce cost-based abduction as a minimization problem on a DAG (directed acyclic graph) and prove the equivalence of the problem to the Bayesian network MAP (maximum a posteriori probability) solution of the system.Expand

We develop a theoretical basis for metalevel decisions in the statistical framework of Bayesian selection problems, arguing (as others have done) that this is more appropriate than the bandit framework.Expand

We propose a new algorithm, FSVI, that uses the underlying MDP to traverse the belief space towards rewards, finding sequences of useful backups, and show how it scales up better than HSVI.Expand

We propose a sampling scheme that is "aware" of VOI, achieving an algorithm that in empirical evaluation outperforms both UCT and the other proposed algorithms.Expand