Evolutionary dynamics of spatial games
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
From genetic evolution to emergence of game strategies
Proceedings of the NATO advanced research workshop and EGS topical workshop on Chaotic advection, tracer dynamics and turbulent dispersion
Emergence of Collective Behavior in Evolving Populations of Flying Agents
Genetic Programming and Evolvable Machines
Emergence of collective behavior in evolving populations of flying agents
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The cognitive and communicative demands of cooperation
Games, Actions and Social Software
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A framework for studying the evolution of cooperative behaviour in a random environment, using evolution of finite state strategies, is presented. The interaction between agents is modelled by a repeated game with random observable payoffs. The agents are thus faced with a more complex situation, compared to the Prisoner's Dilemma that has been widely used for investigating the conditions for cooperation in evolving populations (Matsuo 1985; Axelrod 1987; Miller 1989; Lindgren 1992; Ikegami 1994; Lindgren & Nordahl 1994; Lindgren 1997). Still, there are robust cooperating strategies that usually evolve in a population of agents. In the cooperative mode, these strategies selects an action that allows for maximizing the payoff sum of both players in each round, regardless of the own payoff. Two such players maximize the expected total long-term pay-off. If the opponent deviates from this scheme, the strategy invokes a punishment action, which aims to lower the opponent's score for the rest of the (possibly infinitely) repeated game. The introduction of mistakes to the game actually pushes evolution towards more cooperative strategies even though the game becomes more difficult.