Evaluating NDF-based negotiation mechanism within an agent-based environment

Abstract

We investigate the properties of a negotiation mechanism that is based on negotiation decision functions (NDFs) in an agent-based system. The study employs both analytical and simulation approaches. Analysis and evaluation of negotiation tactics and strategies indicated that negotiation deadline significantly influences the convergence performance of the negotiation. Some important properties of the negotiation convergence are analyzed, and a set of experiments are carried out among agents for typical negotiation tactics and strategies to investigate negotiation fairness with respect to the mean difference of deal values and convergence characteristics. A preliminary study on the efficiency of NDF as compared to TAC agents and Pareto optimal is done as well.

Introduction

Negotiation is critical in resolving conflicts of multi-agent systems in distributed artificial intelligence. Single-shot negotiation models (such as contract net) are typically employed in research and practice along the lines first developed by Davis and Smith [1]. However, no guarantee is given regarding the global optimum for this class of single-shot negotiation models owing to the myopic nature of the class [2]. Negotiation is characterized by iterations among agents. Information is shared extensively among agents and uncover the intentions of opponents through such communication. Negotiation is commonly addressed in the domain of multi-agent systems, and focuses on the exchange of partial plans to attain a global goal [3]. Kraus et al. [4] addressed the issue of negotiation time. Meanwhile, Kraus and Lehmann [5] established an artificial diplomat system to address negotiation issues. Greenwald and Stone [6] also presented a TAC market game run over the Internet and operating within a travel shopping scenario, and discussed the strategies developed for trading agents. Multi-agent negotiation shows promise, particularly in recent applications of intelligent production control systems (e.g. [7], [8]). Furthermore, Faratin et al. [9] proposed negotiation decision function (NDF) as a basis of formal negotiation. In-depth experimental results regarding NDF refer to the ones of [10]. Faratin et al. [11] further proposed a trade-off algorithm of negotiation to increase social welfare.

Negotiation models based on disclosure of information among agents (such as game theory) limit themselves to real applications, while NDF-based negotiation is characterized by its autonomous (private) behavior, consideration of timing, and issues. NDF-based negotiation can be applied to numerous real world application domains, such as industry production control, and pricing in e-markets. Hence, empirical and analytical investigation of NDF-based negotiation is encouraged to better understand its properties and efficiency. Besides, such a study can provide a strong basis for building an incentive mechanism in which agents use certain negotiation parameters to achieve socially desirable outcomes.

The rest of this paper is organized as follows. Section 2 discusses NDFs and how they can be used as building blocks of the multiply negotiation model. Section 3 analyses some important properties of the NDF-based negotiation model, and Section 4 evaluates the model for a specified set of negotiation tactics and strategies, via simulation. Section 5 summarizes the work.