Representing agent policies compactly is essential for improving the scalability of multi-agent planning algorithms. In this paper, we focus on developing a pruning technique that...
Intelligent agents designed to work in complex, dynamic environments must respond robustly and flexibly to environmental and circumstantial changes. An agent must be capable of de...
John Thangarajah, James Harland, David N. Morley, ...
Currently, state of the art virtual agents lack the ability to display emotion as seen in actual humans, or even in hand-animated characters. One reason for the emotional inexpres...
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...
In this paper, we report on an interactive system and the results ofa formal user study that was carried out with the aim of comparing two approaches to estimating users' int...
Boris Brandherm, Helmut Prendinger, Mitsuru Ishizu...
We present an extension of the Dynamics Based Control (DBC) paradigm to environment models based on Predictive State Representations (PSRs). We show an approximate greedy version ...
Ariel Adam, Zinovi Rabinovich, Jeffrey S. Rosensch...
Predictive state representations (PSRs) are models that represent the state of a dynamical system as a set of predictions about future events. The existing work with PSRs focuses ...
Britton Wolfe, Michael R. James, Satinder P. Singh