Pareto Bid Estimation for Multi-Issue Bilateral Negotiation under User Preference Uncertainty
Pallavi Bagga, Nicola Paoletti, Kostas Stathis · 2021
We study the problem of how an agent that negotiates over multiple issues with an opponent can make offers given that it has incomplete information about the user it represents and the opponent it plays against. To tackle this problem, we take a multi-objective optimization stance, where the negotiating agent estimates the preferences of both user and opponent to generate bids that are (near) Pareto-optimal. However, since the negotiating agent needs to approximate the actual preferences of two parties, uncertainty is involved. To handle this uncertainty, we propose a fuzzy approach consisting of a two-phase Pareto-bid generation step where Phase-I generates the non-dominated solutions using a fuzzy multi-objective evolutionary algorithm, and Phase II ranks them to find the best bid to offer the opponent using a fuzzy multiple-criteria decision-making method. Rigorous experimentation shows that the hybrid fuzzy approach of generating the (near) Pareto-optimal bids reduces the average distance to the Pareto curve and increases the average joint or social welfare utility of the agents leading to “win-win” situations.