ausblenden:
Schlagwörter:
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Zusammenfassung:
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.
