Researchers often express probabilistic planning problems as Markov decision process models and then maximize the expected total reward. However, it is often rational to maximize ...
We study how to find plans that maximize the expected total utility for a given MDP, a planning objective that is important for decision making in high-stakes domains. The optimal...
Advances in the direct computation of Lyapunov functions using convex optimization make it possible to efficiently evaluate regions of attraction for smooth nonlinear systems. Her...
Russ Tedrake, Ian R. Manchester, Mark Tobenkin, Jo...
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...
Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is riskneutral in ...