Emergent coordination through the use of cooperative state-changing rules
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Socially conscious decision-making
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Mixing and Memory: Emergent Cooperation in an Information Marketplace
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Believing Others: Pros and Cons
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Stable strategies for sharing information among agents
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The Knowledge Engineering Review
Adapting to agents' personalities in negotiation
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Predicting agent strategy mix of evolving populations
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Computational Logic in Multi-Agent Systems
Supporting collaborative activity
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
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Recent works in multi-agent systems have identified agent behaviors that can develop and sustain mutually beneficial cooperative relationships with like-minded agents and can resist exploitation from selfish agents. Researchers have proposed the use of a probabilistic reciprocity scheme that uses summary information from past interactions to decide whether or not to honor a request for help from another agent. This behavior has been found to be close to optimal in homogeneous groups and outperform exploiters in mixed groups. A major shortcoming of these experiments, however, is that the composition of the group in term of agent behaviors is fixed. We believe that real-life rational agents, on the contrary, will change their behaviors based on observed performances of different behavioral traits with the goal of maximizing performance. In this paper, we present results from experiments on two distinct domains with population groups whose behavioral composition changes based on the performance of the agents. Based on the experimental results, we identify ecological niches for variants of exploitative selfish agents and robust reciprocative agents.