The Partially Observable Markov Decision Process has long been recognized as a rich framework for real-world planning and control problems, especially in robotics. However exact s...
Joelle Pineau, Geoffrey J. Gordon, Sebastian Thrun
In this paper, we propose the combination of different optimization techniques in order to solve “hard” two- and threeobjective optimization problems at a relatively low comp...
Ricardo Landa Becerra, Carlos A. Coello Coello, Al...
We discuss two approximation approaches, the primal-dual schema and the local-ratio technique. We present two relatively simple frameworks, one for each approach, which extend know...
We prove the statistical consistency of kernel Partial Least Squares Regression applied to a bounded regression learning problem on a reproducing kernel Hilbert space. Partial Lea...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...