ﻻ يوجد ملخص باللغة العربية
We present tournament results and several powerful strategies for the Iterated Prisoners Dilemma created using reinforcement learning techniques (evolutionary and particle swarm algorithms). These strategies are trained to perform well against a corpus of over 170 distinct opponents, including many well-known and classic strategies. All the trained strategies win standard tournaments against the total collection of other opponents. The trained strategies and one particular human made designed strategy are the top performers in noisy tournaments also.
The Prisoners Dilemma game has a long history stretching across the social, biological, and physical sciences. In 2012, Press and Dyson developed a method for analyzing the mapping of the 8-dimensional strategy profile onto the 2-dimensional payoff s
The Axelrod library is an open source Python package that allows for reproducible game theoretic research into the Iterated Prisoners Dilemma. This area of research began in the 1980s but suffers from a lack of documentation and test code. The goal o
Since the introduction of zero-determinant strategies, extortionate strategies have received considerable interest. While an interesting class of strategies, the definitions of extortionate strategies are algebraically rigid, apply only to memory-one
We present insights and empirical results from an extensive numerical study of the evolutionary dynamics of the iterated prisoners dilemma. Fixation probabilities for Moran processes are obtained for all pairs of 164 different strategies including cl
The Iterated Prisoners Dilemma with Choice and Refusal (IPD/CR) is an extension of the Iterated Prisoners Dilemma with evolution that allows players to choose and to refuse their game partners. From individual behaviors, behavioral population structu