— Partially Observable Markov Decision Processes (POMDPs) offer a powerful mathematical framework for making optimal action choices in noisy and/or uncertain environments, in par...
This paper introduces a choice-based method that for the first time makes it possible to quantitatively measure regret theory, one of the most popular models of decision under unc...
Partially observable Markov decision processes (POMDPs) have been
successfully applied to various robot motion planning tasks under uncertainty.
However, most existing POMDP algo...
Haoyu Bai, David Hsu, Wee Sun Lee, and Vien A. Ngo
We consider complex scheduling problems that can be captured as optimization under hard and soft constraints. The objective of such an optimization problem is to satisfy as many h...
We present a mechanism for recovering consistent data from inconsistent set of assertions. For a common family of knowledge-bases we also provide an efficient algorithm for doing ...