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 coordination in scale-free networks
Web Intelligence and Agent Systems
Emergence of norms through social learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Norm Emergence with Biased Agents
International Journal of Agent Technologies and Systems
Norm internalization in artificial societies
AI Communications - European Workshop on Multi-Agent Systems (EUMAS) 2009
Social instruments for convention emergence
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Norm Establishment via Metanorms in Network Topologies
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 03
Social instruments for robust convention emergence
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
The impact of social placement of non-learning agents on convention emergence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Robust convention emergence in social networks through self-reinforcing structures dissolution
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Emergence of social norms through collective learning in networked agent societies
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Social conventions are useful self-sustaining protocols for groups to coordinate behavior without a centralized entity enforcing coordination. We perform an in-depth study of different network structures, to compare and evaluate the effects of different network topologies on the success and rate of emergence of social conventions. While others have investigated memory for learning algorithms, the effects of memory or history of past activities on the reward received by interacting agents have not been adequately investigated. We propose a reward metric that takes into consideration the past action choices of the interacting agents. The research question to be answered is what effect does the history based reward function and the learning approach have on convergence time to conventions in different topologies. We experimentally investigate the effects of history size, agent population size and neighborhood size the emergence of social conventions.