Template-type: ReDIF-Paper 1.0
Author-Name: Ferraris Gianluigi
Author-Email: giangi@di.unipi.it
Author-Name: Fontana Magda
Author-Email: magda.fontana@unito.it
Author-Workplace-Name: University of Turin
Author-Workplace-Homepage: http://www.est.unito.it/
Title: Beating the Tit for Tat: Using a Genetic Algorithm to Build an Effective Adaptive Behavior
Abstract: Agents capable of adaptive behavior can be obtained by means of AI tools. Thanks to these, they 
develop the ability to vary their Behavior in order to achieve satisfying results in the simulated environment. 
In the paper, artificially intelligent agents play an iterated prisoner' s dilemma against agents that reproduce 
(in a fix way) strategies that have emerged in Axelrod' s toumament. The objective of the adaptive agent is to 
earn a payoff  higher than one of the Tit-for-tat, the strategy which has shown the better performance in the 
Axelrod's experimental setup. In the work, Genetic Algorithms are employed to produce and modify rules that are 
apt to achieve the set task. The adaptive dynamics is analysed in depth in order to understand the issues related 
to the codification of knowledge and to the evaluation of diverse strategies. In order to highlight different 
nuances of these matters we have amended the method as to improve it and experimented different knowledge's 
codifications.
Length: 24 pages
Creation-Date: 2006-01
File-URL: http://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2006dip/4_wp_fontanaferraris.pdf
File-Format: Application/PDF
Handle: RePEc:uto:dipeco:200604