Designing distributed controllers for self-reconfiguring modular robots has been consistently challenging. We have developed a reinforcement learning approach which can be used bo...
Recent decision-theoric planning algorithms are able to find optimal solutions in large problems, using Factored Markov Decision Processes (fmdps). However, these algorithms need ...
Thomas Degris, Olivier Sigaud, Pierre-Henri Wuille...
Abstract. We are developing a decentralized approach to trust based on referral systems, where agents adaptively give referrals to one another to find other trustworthy agents. In...
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...
We present a query architecture in which join operators are decomposed into their constituent data structures (State Modules, or SteMs), and dataflow among these SteMs is managed ...
Vijayshankar Raman, Amol Deshpande, Joseph M. Hell...