We present a framework for decision making under uncertainty where the priorities of the alternatives can depend on the situation at hand. We design a logic-programming language, D...
Scheduling problems overall assume that it is possible to identify stable criteria definitions measuring the quality of alternatives. In real world problems however, this does not ...
We present a novel affective goal selection mechanism for decision-making in agents with limited computational resources (e.g., such as robots operating under real-time constraint...
A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressivel...
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...