The evolution of intelligent decision making in gaming
Cybernetics and Systems
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
Methods for Competitive Co-Evolution: Finding Opponents Worth Beating
Proceedings of the 6th International Conference on Genetic Algorithms
Evolving behaviors in the iterated prisoner's dilemma
Evolutionary Computation
APT Agents: Agents That Are Adaptive, Predictable, and Timely
FAABS '00 Proceedings of the First International Workshop on Formal Approaches to Agent-Based Systems-Revised Papers
A strategy with novel evolutionary features for the iterated prisoner's dilemma
Evolutionary Computation
Journal of Artificial Intelligence Research
Evolution and incremental learning in the iterated prisoner's dilemma
IEEE Transactions on Evolutionary Computation
Evolutionary Computation: Where we are and where we're headed
Fundamenta Informaticae
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Evolutionary programming experiments are conducted to examine the relationship between the durations of encounters and the evolution of cooperative behavior in the iterated prisoner's dilemma. A population of behavioral strategies represented by finite-state machines is evolved over successive generations, with selection made on the basis of individual fitness. Each finite-state machine is given an additional evolvable parameter corresponding to the maximum number of moves it will execute in any encounter. A series of Monte Carlo trials indicates distinct relationships between encounter length and cooperation; however, no causal relationship can be positively identified.