Probabilistic AI planning methods that minimize expected execution cost have a neutral attitude towards risk. We demonstrate how one can transform planning problems for risk-sensi...
Hybrid probabilistic programs framework [5] is a variation of probabilistic annotated logic programming approach, which allows the user to explicitly encode the available knowledge...
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...
In this paper, we present a motion planning framework for a fully deployed autonomous unmanned aerial vehicle which integrates two sample-based motion planning techniques, Probabi...
We present a new general framework for online istic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The l is an extension of the existing Abstract Hidden ...