University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Modelling and Understanding Cooperation in Societies of Artificial Agents

Modelling and Understanding Cooperation in Societies of Artificial Agents

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The analysis and modelling of the evolution of cooperation in competitive environments are of interest for economics, game theory, biology, psychology, and computer science just to name a few. Mathematical and computational models have been developed in order to extract insights on the underlying mechanisms. More recently, there has been an increasing interest in the study of societies based on artificial agents that can learn their strategies as they interact. The applications of this work are many: from the design of self-organising agent systems, including robotic ones, to the understanding of the emergence of cooperation in human and animal societies (and, possibly, in the future, in mixed environments composed of humans and artificial agents).

In this talk I will give an overview of our ongoing work in modelling societies of artificial learning agents. I will discuss the design of reinforcement learning architectures composed of autonomous agents that do not rely on centralised coordination. I will introduce examples of applications of machine learning algorithms to social dilemmas and cooperative games. Finally, I will discuss our ongoing work in this area and the open questions in this fascinating emerging field.

Bio: Mirco Musolesi is Full Professor of Computer Science at the Department of Computer Science at University College London and a Turing Fellow at the Alan Turing Institute, the UK National Institute for Data Science and Artificial Intelligence. He is also Full Professor of Computer Science at the University of Bologna. Previously, he held research and teaching positions at Dartmouth, Cambridge, St Andrews and Birmingham. The focus of his lab is on Machine Learning/Artificial Intelligence and their applications to a variety of practical and theoretical problems and domains, in particular networked systems, human-centred AI, computational social science and security & privacy. More information about his research profile can be found at:

This talk is part of the Data Science and Computational Statistics Seminar series.

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