Fingerprinting: Visualization and Automatic Analysis of Prisoner's Dilemma Strategies.

Daniel Ashlock, Eun-Youn Kim, and Wendy Ashlock
Submitted to IEEE Transactions on
Computational Intelligence and Artifical Intelligence in Games

Abstract PDF eprint

Fingerprinting is a technique that permits automatic classification of strategies for playing a game. In this study the evolution of strategies for playing the iterated prisoner's dilemma at three different noise levels is analyzed using fingerprinting and other techniques including a novel quantity, evolutionary velocity, derived from fingerprinting. The results are at odds with initial expectations and permit the detection of a critical difference in the evolution of agents with and without noise. Noise during fitness evaluation places a larger fraction of an agent's genome under selective pressure, resulting in substantially more efficient training. In this case efficiency is the production of superior competitive ability at a lower evolutionary velocity. Prisoner's dilemma playing agents are evolved for 6400 generations, taking samples at eight exponentially-spaced epochs. This permits assessment of the change in populations over long evolutionary time. Agents are evaluated for competitive ability between those evolved for different lengths of time and between those evolved using distinct noise levels. The presence of noise during agent training is found to convey a commanding competitive advantage. A novel analysis is done in which a tournament is run with no two agents from the same evolutionary line and one-third of agents from each noise level studied. This analysis simulates contributed agent tournaments without any genetic relation between agents. It is found that in early epochs the agents evolved without noise have the best average tournament rank, but that in later epochs they have the worst.