Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
The traditional high-level algorithms for rigid body simulation work well for moderate numbers of bodies but scale poorly to systems of hundreds or more moving, interacting bodies...
Abstract. Since the demise of the Overnet network, the Kad network has become not only the most popular but also the only widely used peer-to-peer system based on a distributed has...
Thomas Locher, David Mysicka, Stefan Schmid, Roger...
We present techniques for incrementally managing schedules in domains where activities accrue quality as a function of the time and resources allocated to them and the goal is to ...
Anthony Gallagher, Terry L. Zimmerman, Stephen F. ...
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
Lihong Li, Michael L. Littman, Christopher R. Mans...