A component-based generic agent architecture for multi-attribute (integrative) negotiation is introduced and its application is described in a prototype system for negotiation about cars, developed in co-operation with, among others, Dutch Telecom KPN. The approach can be characterised as co-operative one-toone multi-criteria negotiation in which the privacy of both parties is protected as much as possible. 1

Abstract. In multi-issue negotiations, issues may be negotiated independently or not. In the latter case, the utility associated with one issue depends on the value of another. These issue dependencies give rise to more complex, nonlinear utility spaces. As a consequence, the computational cost and complexity of negotiating interdependent issues is increased significantly compared to the case of independent issues. Several techniques have been proposed to deal with this increased complexity, including, for example, introducing a mediator in the negotiation setting. In this paper, we propose an alternative approach based on a weighted approximation technique to simplify the utility space. We show that given certain natural assumptions about the outcome of negotiation the application of this technique results in an outcome that closely matches with the outcome based on the original, interdependent utility structure. Moreover, using the approximated utility structure, each of the issues can be negotiated independently which ensures that the negotiation is computationally tractable. The approach is illustrated by applying and testing it in a case study. 1

This paper presents a decentralized model that allows self-interested agents to reach “win-win ” agreements in a multi-attribute negotiation. The model is based on an alternating-offer protocol. In each period, the proposing agent is allowed to make a limited number of offers. The responding agent can select the best out of these offers. In the case of rejection, agents exchange their roles and the negotiation proceeds to the next period. To make counteroffers, an agent first uses the heuristic of choosing the offer on an indifference (or “iso-utility”) curve/surface that is closest to the best offer made by the opponent in the previous period, and then taking this offer as the seed, chooses several other offers randomly in a specified neighborhood of this seed offer. Experimental results show that this model induces agents to reach near Pareto optimal agreements in general situations where agents have complex preferences on the attributes and incomplete information. This model does not require the presence of a mediator. Keywords: Multi-attribute negotiation, Pareto optimality, Win-win, Rational preference, Incomplete information, Self-interested agents

The significant time, effort, and resource expenditures needed to design and develop aerospace systems motivate on-going research into developing methods for generating, evaluating, and selecting candidate system solutions that can deliver more benefit for a given cost. Compounding the problem is the multiplicity of perspectives of the many stakeholders for such systems, altering the meaning of “benefit ” and “cost ” depending on the stakeholder considered. Tradespace exploration techniques have been used in the past to generate large datasets in order to gain insights into design-value, cost-benefit tradeoffs for complex aerospace systems. Using interactive tradespace exploration to support multi-stakeholder negotiations can reveal these tradeoffs not only for individuals, but also across a group. A method is introduced and applied to two aerospace cases in order to explore the potential for interactive tradespace exploration to support stakeholder negotiations. Preliminary results indicate the method to be a rapid and beneficial technique, which generated compromise alternatives, guided the elicitation of previously unarticulated information, and resulted in increased confidence and solution buy-in of participating stakeholders. I.

Semantic-based e-marketplaces have emerged in recent years, to ease the initial phase of transaction in e-marketplaces, where demand/supply descriptions can be complex and expressive. With the- long term- aim of building an e-marketplace fully exploiting rich semantic descriptions in all stages of a transaction, in this paper we propose a semantic-oriented approach to multi issue bilateral negotiation, which exploits a simple propositional logic. We present the theoretical setting, the negotiation protocol, whose outcome is Pareto-efficient, and illustrate the behavior with a simple example.

Abstract Negotiation has been studied in different communities both scientific and communities of practice. The social sciences and the mathematical sciences have investigated different aspects of negotiation with different goals: the goals of the social sciences are to understand the factors and reasoning processes that underlie human negotiation behavior. The goal of the mathematical sciences is to formulate mathematical models that capture elements of negotiation. Further, the mathematical models can be divided into analytic models (economic, operations research etc) and computational models. The aim of the analytic models is to provide guarantees of their behavior, characterizations of optimality, or provide managerial guidance to optimize negotiation activity. The computational models aim to provide computational tractability through approximation algorithms and heuristics. Most crucially, the computational research aims to have the models implemented in autonomous processes, called agents, that are able to incorporate realistic factors of negotiation (e.g. argumentation, information seeking, and cognitive factors) and engage in negotiations in a decentralized manner. Such agent models promise to contribute to our understanding of human information processing in negotiation. Additionally, they could be used for decision support of human decision makers. In the long run, such models can even become substitutes for human negotiators. In this chapter we will provide a selective review of the most important works in the analytic and computational negotiation literature, point out some differences and synergies and provide pointers to open questions and future research.

Abstract. While there has been a large body of negotiation literature in both Behavioral Science (behavioral) and Artificial Intelligence/Game Theory (computational) communities, there has not been an attempt to bridge the two communities to our best knowledge. In this paper, we compare and contrast the characteristics of behavioral and computational literature in negotiation. We discover that incorporating the strengths of two types of literature are valuable in expanding the horizon of research outlook. Key words: Negotiation, multi-disciplinary research 1

This paper provides an algorithm to compute the subgame perfect equilibrium strategies in perfect information finitehorizon alternating-offers bargaining in-bundle over multiple issues. We show that the agreement is achieved immediately and is Pareto efficient. We make a novel use of backward induction for multiple issues and we prove that, for linear multi-attribute utility functions, the problem of computing the equilibrium is tractable and the related complexity is polynomial with the number of issues and linear with the deadline of bargaining.

Quality related aspects relevant for service-based applications cover a broad field of research, including work on quality modeling, QoS and SLA negotiation, as well as constructive and analytical quality assurance (like testing, monitoring and static analysis). This deliverable provides a survey of this broad field of “service quality ” and identifies the key areas where research contributions are currently available. Based on this survey of the state of the art, important and emerging research challenges are identified that could be pursued in the future in order to close several of the gaps which emerge from the current state of the art on “service quality”. Copyright © 2008 by the S-Cube consortium – All rights reserved.

Eliminating issue dependencies in complex