Learning automata: an introduction
Learning automata: an introduction
On the origin of convention: evidence from symmetric bargaining games
International Journal of Game Theory
Adaptive agents in a persistent shout double auction
Proceedings of the first international conference on Information and computation economies
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent learning using a variable learning rate
Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Cryptography and data security
Cryptography and data security
A Roadmap of Agent Research and Development
Autonomous Agents and Multi-Agent Systems
Human Problem Solving
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
Artificial agents learning human fairness
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
A Short Introduction to Computational Social Choice
SOFSEM '07 Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science
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
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
Requirements and challenges for building service-oriented pervasive middleware
Proceedings of the 2009 international conference on Pervasive services
Learning to reach agreement in a continuous ultimatum game
Journal of Artificial Intelligence Research
Incorporating BDI Agents into Human-Agent Decision Making Research
ESAW '09 Proceedings of the 10th International Workshop on Engineering Societies in the Agents World X
Human-inspired computational fairness
Autonomous Agents and Multi-Agent Systems
Fair Mechanisms for Recurrent Multi Unit Combinatorial Auctions
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Fairness In Recurrent Auctions With Competing Markets And Supply Fluctuations
Computational Intelligence
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Multi-agent systems are complex systems in which multiple autonomous entities, called agents, cooperate in order to achieve a common or personal goal. These entities may be computer software, robots, and also humans. In fact, many multi-agent systems are intended to operate in cooperation with or as a service for humans. Typically, multi-agent systems are designed assuming perfectly rational, self-interested agents, according to the principles of classical game theory. Recently, such strong assumptions have been relaxed in various ways. One such way is explicitly including principles derived from human behavior. For instance, research in the field of behavioral economics shows that humans are not purely self-interested. In addition, 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 an overview of work in the area of fairness in multi-agent systems. More precisely, we first look at the classical agent model, that is, rational decision making. We then provide an outline of descriptive models of fairness, that is, models that explain how and why humans reach fair decisions. Then, we look at prescriptive, computational models for achieving fairness in adaptive multi-agent systems. We show that results obtained by these models are compatible with experimental and analytical results obtained in the field of behavioral economics.