This paper describes a new agent framework that fuses an HTN planner, through its underlying conceptual model, with the mental attitudes of the BDI agent architecture, thus exploi...
Gerhard Wickler, Stephen Potter, Austin Tate, Mich...
Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation trade...
Since navigational aspects of XPath correspond to first-order definability, it has been proposed to use the analogy with the very successful technique of translating LTL into aut...
In our research we study rational agents which learn how to choose the best conditional, partial plan in any situation. The agent uses an incomplete symbolic inference engine, emp...
So far, most equilibrium concepts in game theory require that the rewards and actions of the other agents are known and/or observed by all agents. However, in real life problems, a...