An Effective Negotiation Strategy for Quantitative and Qualitative Issues in Multi-Agent Systems

Abstract

Automated negotiation is an efficient approach for interaction in multi-agent systems in which agents exchange offers and counteroffers to conclude an agreement. This paper addresses the problem of offer formulation during the interaction between buyer and seller software agents for the purpose of reaching an agreement over quantitative and qualitative issues at once. In order to improve the outcome of the negotiation process, a hybrid negotiation method is presented and verified. Offer formulation is based on fuzzy similarity and preference-based methods. The preference-based mechanism is used for quantitative issues, while the fuzzy similarity technique is used for qualitative issues. The preference-based mechanism takes into account the preferences of the opponent when generating offers; the agent makes greater concessions on the issues which the opponent prefers more. The fuzzy-similarity method formulates an offer that considers offering a deal that is more similar to the one received by the opponent during the last round of negotiation. The experiments consists of two parts. The first part compares the hybrid strategy with the basic one. The findings reveal that the hybrid strategy is better in all performance measures, namely, utility rate, agreement rate, and Nash product rate. The second part of the experimental work compares four mechanisms of offer generating mechanisms: basic, preference-based, fuzzy similarity, and hybrid. The results show that the hybrid negotiation strategy performs equal or better that other negotiation strategies. More details can be found in the paper.