Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
Motivated by the success of ensemble methods in machine learning and other areas of natural language processing, we developed a multistrategy and multi-source approach to question...
Jennifer Chu-Carroll, Krzysztof Czuba, John M. Pra...
Abstract. The original Semantic Web vision was explicit in the need for intelligent autonomous agents that would represent users and help them navigate the Semantic Web. We argue t...
Gunnar Aastrand Grimnes, Peter Edwards, Alun D. Pr...
— We formulate a coverage optimization problem for mobile visual sensor networks as a repeated multi-player game. Each visual sensor tries to optimize its own coverage while mini...
We present a universal mechanism that can be combined with existing trust models to extend their capabilities towards efficient modelling of the situational (contextdependent) tr...