Technical Note: \cal Q-Learning
Machine Learning
On the emergence of social conventions: modeling, analysis, and simulations
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Emergence of social conventions in complex networks
Artificial Intelligence
Emergence of coordination in scale-free networks
Web Intelligence and Agent Systems
Aspects of active norm learning and the effect of lying on norm emergence in agent societies
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
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Social conventions are useful self-sustaining protocols for groups to coordinate behavior without a centralized entity enforcing coordination. The emergence of such conventions in different multi agent network topologies has been investigated by several researchers. In the literature we can observe two different approaches taken by agents to reach conventions: direct interactions with other agents that affects its payoff, or, an observance approach based on a majority rule, where agents change their state depending on their neighbors states. However, we proposed a mixed strategy, where agents are able to do both: interact and observe. The research question to be answered in this work is the percentage of each approach an agent should take in order to reach conventions faster.