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Understanding Representational Sensitivity in the Iterated Prisoner's Dilemma with Fingerprints
 

Summary: Understanding Representational Sensitivity in the
Iterated Prisoner's Dilemma with Fingerprints
Daniel Ashlock, Member IEEE Eun-Youn Kim Nicole Leahy
ABSTRACT
The iterated prisoner's dilemma is a widely used computa-
tional model of cooperation and conflict. Many studies report
emergent cooperation in populations of agents trained to play
prisoner's dilemma with an evolutionary algorithm. This study
varies the representation of the evolving agents resulting in
levels of emergent cooperation ranging from 0% to over 90%.
The representations used in this study are: directly encoded
finite state machines, cellularly encoded finite state machines,
feed forward neural networks, if-skip-action lists, parse trees
storing two types of Boolean functions, look-up tables, Boolean
function stacks, and Markov chains. An analytic tool for rapidly
identifying agent strategies and comparing across representations
called a fingerprint is used to compare the more complex
representations. Fingerprints provide functional signatures of
an agent's strategy in a manner that is independent of the
agent's representation. This study demonstrates conclusively that

  

Source: Ashlock, Dan - Department of Mathematics and Statistics, University of Guelph

 

Collections: Mathematics