Abstract: Much existing work in reinforcement learning involves environments that are either intentionally neutral, lacking a role for cooperation and competition, or intentionally simple, when agents need imagine nothing more than that they are playing versions of themselves or are happily cooperative. Richer game theoretic notions become important as these constraints are relaxed. For humans, this encompasses issues that concern utility, such as envy and guilt, and that concern inference, such as recursive modeling of other players, I will discuss some our work in this direction using the framework of interactive partially observable Markov decision-processes, illustrating deception, scepticism, threats and irritation. This is joint work with Nitay Alon, Andreas Hula, Read Montague, Jeff Rosenschein and Lion Schulz.
Bio: Peter Dayan studied mathematics at Cambridge University and received his doctorate from the University of Edinburgh. After postdoctoral research at the Salk Institute and the University of Toronto he moved to the Massachusetts Institute of Technology (MIT) in Boston as assistant professor in 1995. In 1998, he moved to London to help co-found the Gatsby Computational Neuroscience Unit, which became one of the best-known institutions for research in theoretical neuroscience, and was its Director from 2002 to 2017. He was also Deputy Director of the Max Planck/UCL Center for Computational Psychiatry and Ageing Research.
In 2018, he moved to Tübingen to become a Director of the Max Planck Institute for Biological Cybernetics.
Peter Dayan’s research focuses on decision-making processes in the brain, the role of neuromodulators as well as neuronal malfunctions in psychiatric diseases. Dayan has long worked at the interface between natural and engineered systems for learning and choice, and is also regarded as a pioneer in the field of Artificial Intelligence.