We present a method for transferring knowledge learned in one task to a related task. Our problem solvers employ reinforcement learning to acquire a model for one task. We then tra...
Lisa Torrey, Trevor Walker, Jude W. Shavlik, Richa...
We present a framework for active learning in the multiple-instance (MI) setting. In an MI learning problem, instances are naturally organized into bags and it is the bags, instea...
This paper describes a dynamic computer-based business learning environment and the results from applying it in a real-world business organization. We argue for using learning too...
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
An agent's trust decision strategy consists of the agent's policies for making trust-related decisions, such as who to trust, how trustworthy to be, what reputations to ...