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
Multiagent learning using a variable learning rate
Artificial Intelligence
Learning to Be Thoughtless: Social Norms and Individual Computation
Computational Economics
Collective Intelligence and Braess' Paradox
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Norm Governed Multiagent Systems: The Delegation of Control to Autonomous Agents
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Emergence of coordination in scale-free networks
Web Intelligence and Agent Systems
Cooperative Multi-Agent Learning: The State of the Art
Autonomous Agents and Multi-Agent Systems
A rule-based approach to norm-oriented programming of electronic institutions
ACM SIGecom Exchanges
Emergence of norms through social learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effects of social network topology and options on norm emergence
COIN'09 Proceedings of the 5th international conference on Coordination, organizations, institutions, and norms in agent systems
On the convergence of autonomous agent communities
Multiagent and Grid Systems
Norm convergence in populations of dynamically interacting agents
AICS'09 Proceedings of the 20th Irish conference on Artificial intelligence and cognitive science
The influence of random interactions and decision heuristics on norm evolution in social networks
Computational & Mathematical Organization Theory
Emergence of norms for social efficiency in partially iterative non-coordinated games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Emergence and stability of social conventions in conflict situations
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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
Manipulating convention emergence using influencer agents
Autonomous Agents and Multi-Agent Systems
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
Robust Regulation Adaptation in Multi-Agent Systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Effective norms, emerging from sustained individual interactions over time, can complement societal rules and significantly enhance performance of individual agents and agent societies. Researchers have used a model that supports the emergence of social norms via learning from interaction experiences where each interaction is viewed as a stage game. In this social learning model, which is distinct from an agent learning from repeated interactions against the same player, an agent learns a policy to play the game from repeated interactions with multiple learning agents. The key research question is to characterize when and how the entire population of homogeneous learners converge to a consistent norm when multiple action combinations yield the same optimal payoff. In this paper we study two extensions to the social learning model that significantly enhances its applicability. We first explore the effects of heterogeneous populations where different agents may be using different learning algorithms. We also investigate norm emergence when agent interactions are physically constrained. We consider agents located on a grid where an agent is more likely to interact with other agents situated closer to it than those that are situated afar. The key new results include the surprising acceleration in learning with limited interaction ranges. We also study the effects of pure-strategy players, i.e., nonlearners in the environment.