Decision theoretic behavior composition

The behavior composition problem involves realizing a virtual target behavior (i.e., the desired module) by suitably coordinating the execution of a set of partially controllable available components (e.g., agents, devices, processes, etc.) running in a shared partially predictable environment. All existing approaches to such problem have been framed within strict uncertainty settings. In this work, we propose a framework for automatic behavior composition which allows the seamless integration of classical behavior composition with decision-theoretic reasoning. Specifically, we consider the problem of maximizing the ¿expected realizability¿ of the target behavior in settings where the uncertainty can be quantified. Unlike previous proposals, the approach developed here is able to (better) deal with instances that do not accept ¿exact¿ solutions, thus yielding a more practical account for real domains. Moreover, it is provably strictly more general than the classical composition framework. Besides formally defining the problem and what counts as a solution, we show how a decision-theoretic composition problem can be solved by reducing it to the problem of finding an optimal policy in a Markov decision process.