When correct priors are known, Bayesian algorithms give optimal decisions, and accurate confidence values for predictions can be obtained. If the prior is incorrect however, these...
Thomas Melluish, Craig Saunders, Ilia Nouretdinov,...
A well-studied problem in the electric power industry is that of optimally scheduling preventative maintenance of power generating units within a power plant. We show how these pr...
We are designing a computational architecture for a "learning economy" based on personal software agents who represent users in a virtual society and assist them in find...
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
The present paper analyzes a learning experience run at University of Macerata, during a post degree course for in service teachers and mature students. The course was delivered e...