My research attempts to address on-line action selection in reinforcement learning from a Bayesian perspective. The idea is to develop more effective action selection techniques b...
We address the problem of optimally controlling stochastic environments that are partially observable. The standard method for tackling such problems is to define and solve a Part...
Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
We describe an architecture for exploring very large design spaces, for example, spaces that arise when design candidates are generated by combining components systematically from...
John R. Josephson, B. Chandrasekaran, Mark Carroll...
Recent work has demonstrated that treating resource reasoning separately from causal reasoning can lead to improved planning performance and rational resource management where inc...