Automated Negotiation and Decision Making in Multiagent Environments
EASSS '01 Selected Tutorial Papers from the 9th ECCAI Advanced Course ACAI 2001 and Agent Link's 3rd European Agent Systems Summer School on Multi-Agent Systems and Applications
Aggregation operators: new trends and applications
Aggregation operators: new trends and applications
Solving fuzzy optimization problems by evolutionary algorithms
Information Sciences: an International Journal
Commercial applications of agents: lessons, experiences and challenges
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
The Agents Are All Busy Doing Stuff!
IEEE Intelligent Systems
Aggregation Functions: A Guide for Practitioners
Aggregation Functions: A Guide for Practitioners
Weighted maximum entropy OWA aggregation with applications to decision making under risk
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A Systematic Fuzzy Decision-Making Process to Choose the Best Model Among a Set of Competing Models
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We consider a problem of mediated group decision making where a number of agents provide a preference function over a set of alternatives. Then, using such information, a new agent provides its own preferences, and, after that, a mediation step is applied to aggregate the individual preferences in order to obtain a group-preference function. Finally, the most supported alternative is selected. Two key aspects are that the preference functions of the former agents may or may not have uncertainty, and that the mediation process rewards those agents that are open to other alternatives besides their most preferred ones. The question for the new agent is how to score its alternatives in such a way that its most preferred one gets the biggest group support. We propose to define such scoring or preference function as the solution of a nonlinear optimization problem. The model also takes into account that imprecision could exist in the preference functions. Through extensive simulations (varying the number of agents, alternatives, etc.), we conclude that the proposal is feasible and effective. Additionally, the usefulness of the mediation process rewarding openness is empirically confirmed.