Markov Decision Processes (MDPs), currently a popular method for modeling and solving decision theoretic planning problems, are limited by the Markovian assumption: rewards and dy...
Meta-level control manages the allocation of limited resources to deliberative actions. This paper discusses efforts in adding meta-level control capabilities to a Markov Decision...
In multiagent planning, it is often convenient to view a problem as two subproblems: agent local planning and coordination. Thus, we can classify agent activities into two categor...
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
— Partially Observable Markov Decision Processes (POMDPs) provide a rich mathematical model to handle realworld sequential decision processes but require a known model to be solv...