— We study the problem of dynamic learning by a social network of agents. Each agent receives a signal about an underlying state and communicates with a subset of agents (his nei...
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...
In a large distributed system it is often infeasible or even impossible to perform diagnosis using a single model of the whole system. Instead, several spatially distributed local...
Learning reusable sequences can support the development of expertise in many domains, either by improving decisionmaking quality or decreasing execution speed. This paper introduc...