Genetic algorithms for fuzzy controllers
AI Expert
What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Negotiation and cooperation in multi-agent environments
Artificial Intelligence - Special issue on economic principles of multi-agent systems
Applying case-based reasoning: techniques for enterprise systems
Applying case-based reasoning: techniques for enterprise systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Essentials of Fuzzy Modeling and Control
Essentials of Fuzzy Modeling and Control
Strongly Typed Genetic Programming in Evolving Cooperation Strategies
Proceedings of the 6th International Conference on Genetic Algorithms
A Service-Oriented Negotiation Model between Autonomous Agents
Proceedings of the 8th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: Multi-Agent Rationality
How Can an Agent Learn to Negotiate?
ECAI '96 Proceedings of the Workshop on Intelligent Agents III, Agent Theories, Architectures, and Languages
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Five Perspectives on Case Based Reasoning
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Using Agent's Eagerness and Competition in Automated Bidding Strategy
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Multistage Fuzzy Decision Making in Bilateral Negotiation with Finite Termination Times
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Using attribute and attitude assessment for bidding in automated auctions
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
An empirical study of interest-based negotiation
Autonomous Agents and Multi-Agent Systems
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Automated negotiation has been of particular interest due to the relevant role that negotiation plays among trading agents. This paper presents two types of agent architecture: Case-Based and Fuzzy, tomodel an agent negotiation strategy. At each step of the negotiation process these architectures fix the weighted combination of tactics to employ and the parameter values related to these tactics. When an agent is provided with a Case-Based architecture, it uses previous knowledge and information of the environment state to change its negotiation behaviour. On the other hand when provided with a Fuzzy architecture it employs a set of fuzzy rules to determine the values of the parameters of the negotiation model. In this paper we propose an evolutionary approach, applying genetic algorithms over populations of agents provided with the same architecture, to determine which negotiation strategy is more successful.