Abstract. Innovations such as optimistic exploration, function approximation, and hierarchical decomposition have helped scale reinforcement learning to more complex environments, ...
We propose a theoretical framework for specification and analysis of a class of learning problems that arise in open-ended environments that contain multiple, distributed, dynamic...
In this paper we propose a model for human learning and decision making in environments of repeated Cliff-Edge (CE) interactions. In CE environments, which include common daily in...
— In this paper we create a framework to model and characterize the impact of time-varying fading communication links on the performance of a mobile sensor network. We propose co...
We explore the relationship between a natural notion of unsupervised learning studied by Kearns et al. (STOC '94), which we call here "learning to create" (LTC), an...