We present new algorithms for local planning over Markov decision processes. The base-level algorithm possesses several interesting features for control of computation, based on s...
Abstract. Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment effectively changes. Moreover, other agents’ actions are oft...
—We examine the use of teleological metareasoning for self-adaptation in game-playing software agents. The goal of our work is to develop an interactive environment in which the ...
Joshua Jones, Chris Parnin, Avik Sinharoy, Spencer...
We present a new method for carrying out state estimation in multiagent settings that are characterized by continuous or large discrete state spaces. State estimation in multiagen...
Agent interactions where the agents hold conflicting goals could be modelled as adversarial argumentation games. In many real-life situations (e.g., criminal litigation, consumer ...