We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equ...
Michael J. Kearns, Michael L. Littman, Satinder P....
We present a propositional logic to reason about the uncertainty of events, where the uncertainty is modeled by a set of probability measures assigning an interval of probability ...
In this work we focus on efficient heuristics for solving a class of stochastic planning problems that arise in a variety of business, investment, and industrial applications. The...
A serious problem in learning probabilistic models is the presence of hidden variables. These variables are not observed, yet interact with several of the observed variables. Dete...
In previous work [8] we presented a casebased approach to eliciting and reasoning with preferences. A key issue in this approach is the definition of similarity between user prefe...
In this paper, we present an efficient algorithm for performing stepwise selection in the class of decomposable models. We focus on the forward selection procedure, but we also di...
Amol Deshpande, Minos N. Garofalakis, Michael I. J...
It is "well known" that in linear models: (1) testable constraints on the marginal distribution of observed variables distinguish certain cases in which an unobserved ca...
Common wisdom has it that small distinctions in the probabilities quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop re...