Bayesian networks (BN) are particularly well suited to capturing vague and uncertain knowledge. However, the capture of this knowledge and associated reasoning from human domain e...
Jonathan D. Pfautz, Zach Cox, Geoffrey Catto, Davi...
We propose a framework for policy generation in continuoustime stochastic domains with concurrent actions and events of uncertain duration. We make no assumptions regarding the co...
An increasing number of planners can handle uncertainty in the domain or in action outcomes. However, less work has addressed building plans when the planner's world can chan...
The Cluster-Weighted Modeling (CWM) is emerging as a versatile tool for modeling dynamical systems. It is a mixture density estimator around local models. To be specific, the inpu...
Computing a good policy in stochastic uncertain environments with unknown dynamics and reward model parameters is a challenging task. In a number of domains, ranging from space ro...