Decision theory: an introduction to the mathematics of rationality
Decision theory: an introduction to the mathematics of rationality
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Automatic synthesis of new behaviors from a library of available behaviors
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Realising deterministic behavior from multiple non-deterministic behaviors
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Agent composition synthesis based on ATL
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Qualitative approximate behavior composition
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
Automatic behavior composition synthesis
Artificial Intelligence
Supremal realizability of behaviors with uncontrollable exogenous events
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Behavior composition optimization
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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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.