We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...
In this paper we examine ensemble methods for regression that leverage or "boost" base regressors by iteratively calling them on modified samples. The most successful lev...
We believe that with regard to the information technology applications in education, one student one computing device will be the future and long-term trend. Many related studies ...
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
We report on our on-going effort to build an adaptive driver support system, Driver AdvocateTM , merging various AI techniques, in particular, agents, ontology, production systems...
Chung Hee Hwang, Noel Massey, Bradford W. Miller, ...