Objective

A multi-agent system (MAS) is a particular instance of a complex system. Modeling and control of such a system may benefit immensely from representing it at multiple levels of abstraction, i.e. using a hierarchy of representations. This means that the system's state is represented at multiple resolutions (in terms of time and abstraction) simultaneously for different levels in the hierarchy, and similarly, that actions are taken at multiple resolutions. The project aims to develop scalable, theoretically sound methods for constructing and updating hierarchical state representations for multi-agent systems. We are working on establishing criteria for hierarchical state representations, e.g. based on 1) utility for actions; 2) the Markov property; 3) uncertainty, etc. Furthermore, we are developing algorithms that enforce/approximate these criteria, based on the theory of MDPs, POMDPs, HPOMDPs, DBNs, and Utile Distinction methods. Our main application domain is road traffic management.

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Funding

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