Being able to ensure that a multiagent system will not generate undesirable behaviors is essential within the context of critical applications (embedded systems or real-time system...
Caroline Chopinaud, Amal El Fallah-Seghrouchni, Pa...
In many dynamic open systems, agents have to interact with one another to achieve their goals. Here, agents may be self-interested and when trusted to perform an action for another...
W. T. Luke Teacy, Jigar Patel, Nicholas R. Jenning...
Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
The application of reinforcement learning algorithms to Partially Observable Stochastic Games (POSG) is challenging since each agent does not have access to the whole state inform...
Alessandro Lazaric, Mario Quaresimale, Marcello Re...
Object detection using Haar-like features is formulated as a maximum likelihood estimation. Object features are described by an arbitrary Bayesian Network (BN) of Haar-like featur...