In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
We have constructed ADVISOR, a two-agent machine learning architecture for intelligent tutoring systems (ITS). The purpose of this architecture is to centralize the reasoning of a...
Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that...
In this paper, we propose a serving system consisting intelligent agents processing society information in a multi-user domain. The agents use the similarity information on the us...
This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formul...
Marilyn A. Walker, Jeanne Frommer, Shrikanth Naray...