Niching methods for genetic algorithms
Niching methods for genetic algorithms
Negotiation and cooperation in multi-agent environments
Artificial Intelligence - Special issue on economic principles of multi-agent systems
A genetic agent-based negotiation system
Computer Networks: The International Journal of Computer and Telecommunications Networking
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Determining Successful Negotiation Strategies: An Evolutionary Approach
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Optimal agendas for multi-issue negotiation
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Using Similarity Criteria to Make Negotiation Trade-Offs
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
An agenda-based framework for multi-issue negotiation
Artificial Intelligence
Automated Multi-Attribute Negotiation with Efficient Use of Incomplete Preference Information
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Optimal Negotiation of Multiple Issues in Incomplete Information Settings
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Modeling complex multi-issue negotiations using utility graphs
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Easishop: Ambient intelligence assists everyday shopping
Information Sciences: an International Journal
Intelligent environment for monitoring Alzheimer patients, agent technology for health care
Decision Support Systems
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
A multi-issue negotiation protocol among agents with nonlinear utility functions
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
Decision making of negotiation agents using markov chains
Multiagent and Grid Systems - Negotiation and Scheduling Mechanisms for Multiagent Systems
The crowding approach to niching in genetic algorithms
Evolutionary Computation
SHOMAS: Intelligent guidance and suggestions in shopping centres
Applied Soft Computing
Effective bidding and deal identification for negotiations in highly nonlinear scenarios
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Agent behaviors in virtual negotiation environments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Ambient Intelligence: A New Multidisciplinary Paradigm
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Studying the impact of negotiation environments on negotiation teams' performance
Information Sciences: an International Journal
A utility concession curve data fitting model for quantitative analysis of negotiation styles
Expert Systems with Applications: An International Journal
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Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers.