Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
This paper provides algorithms that use an information-theoretic analysis to learn Bayesian network structures from data. Based on our three-phase learning framework, we develop e...
Jie Cheng, Russell Greiner, Jonathan Kelly, David ...
Abstract. In multi-robot applications, such as foraging or collection tasks, interference, which results from competition for space between spatially extended robots, can significa...
A digital control application for the frequency stabilization of optical frequency standards is presented. The standard employed is a monolithic neodymium in yttrium aluminum garn...
Optimal control problems involve the difficult task of determining time-varying profiles through dynamic optimization. Such problems become even more complex in practical situatio...