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Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Multi-Issue Negotiation Processes by Evolutionary Simulation, Validationand Social Extensions
Computational Economics
On optimal outcomes of negotiations over resources
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Evolutionary game theory and multi-agent reinforcement learning
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Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
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Artificial Intelligence
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
Fairness in multi-agent systems
The Knowledge Engineering Review
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
Learning to cooperate in a continuous tragedy of the commons
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Journal of Artificial Intelligence Research
Learning to reach agreement in a continuous ultimatum game
Journal of Artificial Intelligence Research
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
Automated mechanism design with co-evolutionary hierarchical genetic programming techniques
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ACM Transactions on Autonomous and Adaptive Systems (TAAS) - Special Section: Extended Version of SASO 2011 Best Paper
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In many common tasks for multi-agent systems, assuming individually rational agents leads to inferior solutions. Numerous researchers found that fairness needs to be considered in addition to individual reward, and proposed valuable computational models of fairness. In this paper, we argue that there are two opportunities for improvement. First, existing models are not specifically tailored to addressing a class of tasks named social dilemmas, even though such tasks are quite common in the context of multi-agent systems. Second, the models generally rely on the assumption that all agents will and can adhere to these models, which is not always the case. We therefore present a novel computational model, i.e., human-inspired computational fairness. Upon being confronted with social dilemmas, humans may apply a number of fully decentralized sanctioning mechanisms to ensure that optimal, fair solutions emerge, even though some participants may be deciding purely on the basis of individual reward. In this paper, we show how these human mechanisms may be computationally modelled, such that fair and optimal solutions emerge from agents being confronted with social dilemmas.