This paper proposes a metric learning based approach for human activity recognition with two main objectives: (1) reject unfamiliar activities and (2) learn with few examples. We s...
Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
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...
Abstract. We present some techniques for handling planning problems with numerical expressions that can be specified using the standard planning language PDDL. These techniques ar...
Exploiting linear type structure, we introduce a new theory bisimilarity for the π-calculus in which we abstract away not only τ-actions but also non-τ actions which do not aff...