Approximating n-player behavioural strategy nash equilibria using coevolution

Abstract

Coevolutionary algorithms are plagued with a set of problems related to intransitivity that make it questionable what the end product of a coevolutionary run can achieve. With the introduction of solution concepts into coevolution, part of the issue was alleviated, however efficiently representing and achieving game theoretic solution concepts is still not a trivial task. In this paper we propose a coevolutionary algorithm that approximates behavioural strategy Nash equilibria in n-player zero sum games, by exploiting the minimax solution concept. In order to support our case we provide a set of experiments in both games of known and unknown equilibria. In the case of known equilibria, we can confirm our algorithm converges to the known solution, while in the case of unknown equilibria we can see a steady progress towards Nash.

Implementing feedback in creative systems: A workshop approach

“…In particular, the ability to extract new higher order patterns that describe exceptions is an example of "learning from feedback." Deep learning and evolutionary models increasingly use this sort of idea to facilitate strategic discovery (Samothrakis & Lucas, 2011). Similar ideas are considered in business applications under the heading "process mining" (Van Der Aalst, 2011).…”