Planning under uncertainty is an important and challenging problem in multiagent systems. Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful fr...
Today many formalisms exist for specifying complex Markov chains. In contrast, formalisms for specifying rewards, enabling the analysis of long-run average performance properties,...
This paper describes some initial steps towards sensor based path planning in an unknown static environment. The method is a based on a sensor-based incremental construction of a o...
The wireless networking environment presents formidable challenges to the study of broadcasting and multicasting problems. In this paper we focus on the problem of multicast tree c...
Jeffrey E. Wieselthier, Gam D. Nguyen, Anthony Eph...
We consider a class of Markov chains known for its closed form transient and steady-state distributions. We show that some absorbing chains can be also seen as members of this clas...