Learning Teammate Models for Ad Hoc Teamwork (2012)
Samuel Barrett, Peter Stone, Sarit Kraus, and Avi Rosenfeld
Robust autonomous agents should be able to cooperate with new teammates effectively by employing ad hoc teamwork. Reasoning about ad hoc teamwork allows agents to perform joint tasks while cooperating with a variety of teammates. As the teammates may not share a communication or coordination algorithm, the ad hoc team agent adapts to its teammates just by observing them. Whereas most past work on ad hoc teamwork considers the case where the ad hoc team agent has a prior model of its teammate, this paper is the first to introduce an agent that learns models of its teammates autonomously. In addition, this paper presents a new transfer learning algorithm that can be used when the ad hoc agent only has limited observations about potential teammates.
In AAMAS Adaptive Learning Agents (ALA) Workshop, June 2012.

Samuel Barrett Ph.D. Student sbarrett [at] cs utexas edu
Peter Stone Faculty pstone [at] cs utexas edu