Learning automata: an introduction
Learning automata: an introduction
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Multiagent learning using a variable learning rate
Artificial Intelligence
Evolutionary game theory and multi-agent reinforcement learning
The Knowledge Engineering Review
Robust and Scalable Coordination of Potential-Field Driven Agents
CIMCA '06 Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
If multi-agent learning is the answer, what is the question?
Artificial Intelligence
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
Emergence of norms through social learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Priority awareness: towards a computational model of human fairness for multi-agent systems
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Fairness in multi-agent systems
The Knowledge Engineering Review
Fairness in multi-agent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: doctoral mentoring program
Learning to cooperate in a continuous tragedy of the commons
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning to reach agreement in a continuous ultimatum game
Journal of Artificial Intelligence Research
Human-inspired computational fairness
Autonomous Agents and Multi-Agent Systems
Incorporating fairness into agent interactions modeled as two-player normal-form games
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Cooperative search for fair nurse rosters
Expert Systems with Applications: An International Journal
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Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually rational agents, according to the principles of classical game theory. However, research in the field of behavioral economics has shown that humans are not purely self-interested: they strongly care about fairness. Therefore, multi-agent systems that fail to take fairness into account, may not be sufficiently aligned with human expectations and may not reach intended goals. In this paper, we present a computational model for achieving fairness in adaptive multi-agent systems. The model uses a combination of Continuous Action Learning Automata and the Homo Egualis utility function. The novel contribution of our work is that this function is used in an explicit, computational manner. We show that results obtained by agents using this model are compatible with experimental and analytical results on human fairness, obtained in the field of behavioral economics.