Research in multi-agent systems has led to the development of many multi-agent control architectures. However, we believe that there is currently no known optimal structure for mu...
Thuc Vu, Jared Go, Gal A. Kaminka, Manuela M. Velo...
This is foremost a methodological contribution. It focuses on the foundation of anticipation and the pertinent implications that anticipation has on learning (theory and experiment...
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...
The central problem of designing intelligent robot systems which learn by demonstrations of desired behaviour has been largely studied within the field of robotics. Numerous archi...
Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for ...
Kenneth D. Forbus, Karen Carney, Bruce L. Sherin, ...