Rules of encounter: designing conventions for automated negotiation among computers
Rules of encounter: designing conventions for automated negotiation among computers
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis
Artificial Intelligence Review
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Intelligent Mutation Rate Control in Canonical Genetic Algorithms
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Towards Genetically Optimised Multi-Agent Multi-Issue Negotiations
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 1 - Volume 01
A Comparative Study of Game Theoretic and Evolutionary Models of Bargaining for Software Agents
Artificial Intelligence Review
An evolutionary learning approach for adaptive negotiation agents: Research Articles
International Journal of Intelligent Systems - Learning Approaches for Negotiation Agents and Automated Negotiation
From market-driven agents to market-oriented grids (position paper)
ACM SIGecom Exchanges
Equilibrium analyses of market-driven agents
ACM SIGecom Exchanges
A survey of bargaining models for grid resource allocation
ACM SIGecom Exchanges
Guest Editorial: Agent-based Grid computing
Applied Intelligence
New methods for competitive coevolution
Evolutionary Computation
Agent behaviors in virtual negotiation environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
A weighted sum genetic algorithm to support multiple-partymultiple-objective negotiations
IEEE Transactions on Evolutionary Computation
Elitism-based compact genetic algorithms
IEEE Transactions on Evolutionary Computation
Agents that react to changing market situations
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Flexible negotiation agent with relaxed decision rules
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Equilibria, prudent Compromises,and the "Waiting" game
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Grid Commerce, Market-Driven G-Negotiation, and Grid Resource Management
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Toward market-driven agents for electronic auction
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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There are very few existing works that adopt genetic algorithms (GAs) for evolving the most successful strategies for different negotiation situations. Furthermore, these works did not explicitly model the influence of market dynamics. The contribution of this work is developing bargaining agents that can both: 1) react to different market situations by adjusting their amounts of concessions and 2) evolve their best-response strategies for different market situations and constraints using an aggregative fitness GA (AFGA). While many existing negotiation agents only optimize utilities, the AFGA in this work is used to evolve best-response strategies of negotiation agents that optimize their utilities, success rates, and negotiation speed in different market situations. Given different constraints and preferences of agents in optimizing utilities, success rates, and negotiation speed, different best-response strategies can be evolved using the AFGA. A testbed consisting of both: 1) market-driven agents (MDAs)--negotiation agents that make adjustable amounts of concessions taking into account market rivalry, outside options, and time preferences and 2) GA-MDAs--MDAs augmented with an AFGA, was implemented. Empirical results show that GA-MDAs achieved higher utilities, higher success rates, and faster negotiation speed than MDAs in a wide variety of market situations.