Bargaining theory with applications
Bargaining theory with applications
On agent-based software engineering
Artificial Intelligence
Foundations of genetic programming
Foundations of genetic programming
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
On Fairness in an Alternating-Offers Bargaining Model with Evolutionary Agents
PRIMA 2001 Proceedings of the 4th Pacific Rim International Workshop on Multi-Agents, Intelligent Agents: Specification, Modeling, and Applications
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Annals of Mathematics and Artificial Intelligence
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International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
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Applied Soft Computing
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IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An approach for obtaining bargaining set in multi-choice games
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
A space search optimization algorithm with accelerated convergence strategies
Applied Soft Computing
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This paper explores the possibility of using evolutionary algorithms (EAs) to automatically generate efficient and stable strategies for complicated bargaining problems. This idea is elaborated by means of case studies. We design artificial players whose learning and self-improving capabilities are powered by EAs, while neither game-theoretic knowledge nor human expertise in game theory is required. The experimental results show that a co-evolutionary algorithm (CO-EA) selects those solutions which are identical or statistically approximate to the known game-theoretic solutions. Moreover, these evolved solutions clearly demonstrate the key game-theoretic properties on efficiency and stability. The performance of CO-EA and that of a multi-objective evolutionary algorithm (MOEA) on the same problems are analyzed and compared. Our studies suggest that for real-world bargaining problems, EAs should automatically design bargaining strategies bearing the attractive properties of the solution concepts in game theory.