Partially observable Markov decision processes (POMDPs) are an intuitive and general way to model sequential decision making problems under uncertainty. Unfortunately, even approx...
Tao Wang, Pascal Poupart, Michael H. Bowling, Dale...
Abstract. This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief, Desire, Intention) agents. Despite the relevance...
Systems on chip (SoC) have much in common with traditional (networked) distributed systems in that they consist of largely independent components with dedicated communication inte...
We initiate the study of probabilistic parallel programs with dynamic process creation and synchronisation. To this end, we introduce probabilistic split-join systems (pSJSs), a mo...
tail defines the level of abstraction used to implement the model's components. A highly detailed model will faithfully simulate all aspects of machine operation, whether or n...