The challenge we address is to reason about projected resource usage within a hierarchical task execution framework in order to improve agent effectiveness. Specifically, we seek ...
Decentralized MDPs provide a powerful formal framework for planning in multi-agent systems, but the complexity of the model limits its usefulness. We study in this paper a class o...
Raphen Becker, Shlomo Zilberstein, Victor R. Lesse...
In this paper the application of reinforcement learning to Tetris is investigated, particulary the idea of temporal difference learning is applied to estimate the state value funct...
Forming effective coalitions is a major research challenge in the field of multi-agent systems. Central to this endeavour is the problem of determining the best set of agents that...
Talal Rahwan, Sarvapali D. Ramchurn, Viet Dung Dan...
Memory-bounded techniques have shown great promise in solving complex multi-agent planning problems modeled as DEC-POMDPs. Much of the performance gains can be attributed to pruni...