Abstract. We adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal stra...
Abstract. Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more recently active learning are two standard ...
Charlie K. Dagli, ShyamSundar Rajaram, Thomas S. H...
Abstract- Multiagent reinforcement learning for multirobot systems is a challenging issue in both robotics and artificial intelligence. With the ever increasing interests in theor...
Abstract. We consider the problem of learning an unknown (overcomplete) basis from an unknown sparse linear combination. Introducing the "sparse coding neural gas" algori...
Abstract. We present a biologically inspired vision system able to incrementally learn multiple visual categories by interactively presenting several hand-held objects. The overall...
Stephan Kirstein, Heiko Wersing, Horst-Michael Gro...