We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an age...
Abstract. This paper proposes an entropy based Markov chain (EMC) fusion technique and demonstrates its applications in multisensor fusion. Self-entropy and conditional entropy, wh...
We study the problem of dynamic spectrum sensing and access in cognitive radio systems as a partially observed Markov decision process (POMDP). A group of cognitive users cooperati...
Jayakrishnan Unnikrishnan, Venugopal V. Veeravalli
Mobile Ad hoc Networks (MANETs) rely on dynamic configuration decisions to efficiently operate in a rapidly changing environment of limited resources. The ability of a MANET to ma...
Mark E. Orwat, Timothy E. Levin, Cynthia E. Irvine
Both human and automated tutors must infer what a student knows and plan future actions to maximize learning. Though substantial research has been done on tracking and modeling stu...
Anna N. Rafferty, Emma Brunskill, Thomas L. Griffi...