We propose a new `Mark-Ant-Walk' algorithm for robust and efficient covering of continuous domains by ant-like robots with very limited capabilities. The robots can mark plac...
Eliyahu Osherovich, Vladimir Yanovski, Israel A. W...
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...
State estimation in multiagent settings involves updating an agent’s belief over the physical states and the space of other agents’ models. Performance of the previous approac...
Inference in Markov Decision Processes has recently received interest as a means to infer goals of an observed action, policy recognition, and also as a tool to compute policies. ...
Single-agent reinforcement learners in time-extended domains and multi-agent systems share a common dilemma known as the credit assignment problem. Multi-agent systems have the st...