Automated Multilateral Negotiation on Multiple Issues with Private Information

Abstract

In this paper, we propose and analyze a distributed negotiation strategy for a multi-agent, multi-attribute negotiation in which the agents have no information about the utility functions of other agents. We analytically prove that, if the zone of agreement is nonempty and the agents concede up to their reservation utilities, agents generating offers using our offer-generation strategy, namely the sequential projection strategy, will converge to an agreement acceptable to all the agents; the convergence property does not depend on the specific concession strategy. In considering agents’ incentive to concede during the negotiation, we propose and analyze a reactive concession strategy. Through computational experiments, we demonstrate that our distributed negotiation strategy yields performance sufficiently close to the Nash bargaining solution and that our algorithms are robust to potential deviation strategies. Methodologically, our paper advances the state of the art of alternating projection algorithms, in that we establish the convergence for the case of multiple, moving sets (as opposed to two static sets in the current literature). Our paper introduces a new analytical foundation for a broad class of computational group decision and negotiation problems.

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

“…• This dissertation, at the third stage, incorporates a bilateral multi-issue negotiation approach in the SALSA system and defines a VPP, which on behalf of prosumers, negotiates with the aggregator on aggregated power and price offer packages with no private information shared between them [68]. The negotiation approach assumes that the VPP and the aggregator (negotiators) have private nonlinear utility functions and start the negotiation with an offer package providing the highest possible utility value.…”

“…Due to the promising outlook of introducing prosumers into the smart grid, this dissertation employs an approach to enable the concurrent negotiation on power and price issues with packaged offers given that the negotiators have no prior knowledge about the flexibility information and utility functions of each other [68]. To model such approach, the following key elements are needed: i) notion of a solution to the negotiation problem, and ii) negotiation protocol and strategy.…”

“…We assume a very general hyper-quadric utility function [90] for negotiators, which is private, continuous, and strictly concave [68]. By "private," negotiators have no knowledge about other negotiator's utility function.…”

“…where U A ∈ [0, 1) is the aggregator's utility function, which follows the same rule as prosumers' [68].…”

“…where µ V ( ) , µ A ( ) ∈ [0, 1) are monotonically decreasing time-dependent concession values of the VPP and aggregator, respectively [68]. Their values only depend on each negotiator's reservation utility value and the number of negotiation iterations passed so far with the decay rate ∈ R >0 [92].…”