Probabilistic Boolean networks (PBNs) have been recently introduced as a paradigm for modeling genetic regulatory networks. One of the objectives of PBN modeling is to use the netw...
Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The p...
This paper summarizes research on a new emerging framework for learning to plan using the Markov decision process model (MDP). In this paradigm, two approaches to learning to plan...
Sridhar Mahadevan, Sarah Osentoski, Jeffrey Johns,...
We propose an integrated scheme for tracking the mobility of a user based on autoregressive models that accurately capture the characteristics of realistic user movements in wirel...
Recent research in decision theoretic planning has focussedon making the solution of Markov decision processes (MDPs) more feasible. We develop a family of algorithms for structur...
Craig Boutilier, Ronen I. Brafman, Christopher W. ...