University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Planning under uncertainty for real-world multiagent systems

Planning under uncertainty for real-world multiagent systems

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If you have a question about this talk, please contact Leandro Minku.

As distributed intelligent systems are becoming more ubiquitous in society, the need for intelligent decision making in multiagent systems grows. The decision-making problem is particularly challenging when uncertainty is involved, and has not yet been solved satisfactorily. In this talk I will give an overview of our recent work, which targets planning in real-world multiagent systems.

For an agent in isolation, planning under uncertainty has been studied using decision-theoretic models like Partially Observable Markov Decision Processes (POMDPs). I will discuss how we use POMD Ps for problems involving active cooperative perception, in which a mobile robot interacts with a network of surveillance cameras. In the multiagent case, related models such as Decentralized POMD Ps have been gaining popularity. I will present recent algorithmic advances in optimal Dec-POMDP solving, as well as techniques for exploiting local interactions between agents. Finally, I will detail how multiagent planning under uncertainty can be applied to networks of robots and sensors.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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