Craig Boutilier

TL;DR: In this article, the authors present an overview and synthesis of MDP-related methods, showing how they provide a unifying framework for modeling many classes of planning problems studied in AI.

TL;DR: This work distinguishes reinforcement learners that are unaware of (or ignore) the presence of other agents from those that explicitly attempt to learn the value of joint actions and the strategies of their counterparts, and proposes alternative optimistic exploration strategies that increase the likelihood of convergence to an optimal equilibrium.

TL;DR: This paper proposes a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation, and provides a formal semantics for this model.

TL;DR: In this paper, the authors propose a formal notion of context-specific independence (CSI) based on regularities in the conditional probability tables (CPTs) at a node, and then focus on a particular qualitative representation scheme -tree-structured CPTs -for capturing CSI.

TL;DR: This paper proposes a formal notion of context-specific independence (CSI), based on regularities in the conditional probability tables (CPTs) at a node, and proposes a technique, analogous to (and based on) d-separation, for determining when such independence holds in a given network.