Partially Observable Markov Decision Processes (POMDPs) are a well-established and rigorous framework for sequential decision-making under uncertainty. POMDPs are well-known to be...
We present a dual decomposition approach to the treereweighted belief propagation objective. Each tree in the tree-reweighted bound yields one subproblem, which can be solved with...
Agent-oriented programming has been motivated in part by the conception that high-level programming constructs based on common tions such as beliefs and goals provide appropriate a...
Incorporating constraints into a reactive BDI agent programming language can lead to better expressive capabilities as well as more efficient computation (in some instances). More...
The way a rational agent changes her belief in certain proposition/hypotheses in the light of new evidence lies in the heart of Bayesian inference. The basic natural assumption, a...