Cooperation enables intelligent systems to reciprocally and subtly divide the costs, share the risks, and distribute the utility. As such, it unlocks the true potential of cognitive networks in terms of the efficiency of resource expenditure (self-optimization), stable distributed control (self-organization), and sustainability (self-diagnosing and self-healing). Cooperative cost-, risk-, and resource sharing mechanisms have broad applicability to manage and optimize technological networks. They also find application in politics and economics. Nevertheless, inducing cooperation among cognitive entities is challenging due to several issues such as lack of information, conflicting interests, and excessive computational complexity. This project aims at developing rigorous decision-making mechanisms for cooperation. Such mechanisms enable cognitive entities to strategically collaborate in complicated scenarios, e.g., under uncertainty, given communication constraints, and inside dynamic environments. The building blocks of such mechanisms are reinforcement learning or inverse reinforcement learning, together with game theory. The outcome of such mechanism are efficient equilibria.

This project receives funding from Cyber Valley as a part of the program “Cyber Valley Research Fund”. The duration is 01.2022-12.2024.