A REGION‐BASED MULTI‐ISSUE NEGOTIATION PROTOCOL FOR NONMONOTONIC UTILITY SPACES

Nonmonotonic utility spaces are found in multi‐issue negotiations where the preferences on the issues yield multiple local optima. These negotiations are specially challenging because of the inherent complexity of the search space and the difficulty of learning the opponent’s preferences. Most current solutions successfully address moderately complex preference scenarios, while solutions intended to operate in highly complex spaces are constrained by very specific preference structures. To overcome these problems, we propose the Region‐Based Multi‐issue Negotiation Protocol (RBNP) for bilateral automated negotiation. RBNP is built upon a nonmediated recursive bargaining mechanism which efficiently modulates a region‐based joint exploration of the solution space. We empirically show that RBNP produces outcomes close to the Pareto frontier in reasonable negotiation times, and show that it provides a significantly better performance when compared to a generic Similarity‐Based Multi‐issue Negotiation Protocol (SBNP), which has been successfully used in many negotiation models. We have paid attention to the strategic issues, proposing and evaluating several concession mechanisms, and analyzing the equilibrium conditions. Results suggest that RBNP may be used as a basis to develop negotiation mechanisms in nonmonotonic utility spaces.     

Compromising Adjustment Strategy Based on TKI Conflict Mode for Multi‐Times Bilateral Closed Negotiations

Bilateral multi‐issue closed negotiation is an important class for real‐life negotiations. Usually, negotiation problems have constraints such as a complex and unknown opponent's utility in real time, or time discounting. In the class of negotiation with some constraints, the effective automated negotiation agents can adjust their behavior depending on the characteristics of their opponents and negotiation scenarios. Recently, the attention of this study has focused on the interleaving learning with negotiation strategies from the past negotiation sessions. By analyzing the past negotiation sessions, agents can estimate the opponent's utility function based on exchanging bids. In this article, we propose a negotiation strategy that estimates the opponent's strategies based on the past negotiation sessions. Our agent tries to compromise to the estimated maximum utility of the opponent by the end of the negotiation. In addition, our agent can adjust the speed of compromise by judging the opponent's Thomas–Kilmann conflict mode and search for the Pareto frontier using past negotiation sessions. In the experiments, we demonstrate that the proposed agent has better outcomes and greater search technique for the Pareto frontier than existing agents in the linear and nonlinear utility functions.     

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Electronic negotiation systems have been devised to create an electronic marketplace for bargaining, auctions, reverse auctions, and exchanges between multiple buyers and sellers. Most studies of negotiation systems concentrate on negotiation process modeling and data modeling - rather than on strategies and efficiency - for a multiple-criteria decision making (MCDM) problem in which many criteria are taken into account as attributes for decision making. This study proposes an active collaboration and negotiation framework (ACNF), which is a negotiation support system that uses active documents with embedded business logics or business rules that can adapt to different collaborative strategies in a business-to-business (B2B) environment. The risk preferences of negotiators are modeled and measured by utility functions that provide mathematical tools to compute the relative value of different courses of action. The system is demonstrated, and three experiments are conducted to validate its performance. The experiments show that the negotiation process is very efficient, and the results are both close to the efficient point - and the Pareto frontier - and are fair to both negotiating parties. The framework can be used to efficiently and effectively achieve a settlement in various multiple-criteria bargaining schemes in the electronic marketplace     

Building a Multiple-Criteria Negotiation Support System

Electronic negotiation systems have been devised to create an electronic marketplace for bargaining, auctions, reverse auctions, and exchanges between multiple buyers and sellers. Most studies of negotiation systems concentrate on negotiation process modeling and data modeling?rather than on strategies and efficiency?for a multiple-criteria decision making (MCDM) problem in which many criteria are taken into account as attributes for decision making. This study proposes an active collaboration and negotiation framework (ACNF), which is a negotiation support system that uses active documents with embedded business logics or business rules that can adapt to different collaborative strategies in a business-to-business (B2B) environment. The risk preferences of negotiators are modeled and measured by utility functions that provide mathematical tools to compute the relative value of different courses of action. The system is demonstrated, and three experiments are conducted to validate its performance. The experiments show that the negotiation process is very efficient, and the results are both close to the efficient point?or the Pareto frontier?and are fair to both negotiating parties. The framework can be used to efficiently and effectively achieve a settlement in various multiple-criteria bargaining schemes in the electronic marketplace.     

A weighted sum genetic algorithm to support multiple-partymultiple-objective negotiations

Negotiations are a special class of group decision-making problems that can be formulated as constrained optimization problems and are characterized by high degrees of conflict among the negotiation participants. A variety of negotiation support techniques have been used to help find solutions acceptable to all parties in a negotiation. The paper presents an approach that employs a genetic algorithm (GA) for finding acceptable solutions for multiparty multiobjective negotiations. The GA approach is consistent with the complex nature of real-world negotiations and is therefore capable of addressing more realistic negotiation scenarios than previous techniques in the literature allow. In addition to the traditional genetic operators of reproduction, crossover, and mutation, the search is enhanced with a new operator called trade. The trade operator simulates concessions that might be made by parties during the negotiation process. GA performance with the trade operator is compared to a traditional GA, nonlinear programming, a hill-climber, and a random search. Experimental results show the GA with the trade operator performs better than these other more traditional approaches     

STUDYING THE EFFECTS OF STRESS ON NEGOTIATION BEHAVIOR

Negotiation is a collaborative activity that requires the participation of different parties whose behaviors influence the outcome of the whole process. The work presented here focuses on the identification of such behaviors and their impact on the negotiation process. The premise for this study is that identifying and cataloging the behavior of parties during a negotiation may help to clarify the role that stress plays in the process. To do so, an experiment based on a negotiation game was implemented. During this experiment, behavioral and contextual information about participants was acquired. The data from this negotiation game were analyzed in order to identify the conflict styles used by each party and to extract behavioral patterns from the interactions, useful for the development of plans and suggestions for the associated participants. The work highlights the importance of the knowledge about social interactions as a basis for informed decision support in situations of conflict.     

Manufacturer warranty service outsourcing strategies in a dual‐channel supply chain

Warranty management for durable products has received increasing attention in recent years. In addition to conventionally keeping warranty in‐house, more manufacturers choose to outsource warranty service to agents. This paper explores outsourcing strategies for manufacturer warranty services in a dual‐channel supply chain by which the demand‐enhancing service can be undertaken by different supply chain parties. We show the results of three alternative outsourcing strategies for manufacturers whereby (a) the retailer undertakes the service, (b) a third party undertakes the service, and (c) both the retailer and a third party undertake the service simultaneously. According to Stackelberg game theory, we build a two‐echelon supply chain model and discuss the corresponding expressions for supply chain members' equilibrium decisions. Additionally, we compare the profits of each supply chain member and derive interesting managerial insights. When the base market size is relatively large, Scenario R helps the manufacturer and the retailer reach a “win‐win” situation.     

A self-protecting and self-healing framework for negotiating services and trust in autonomic communication systems

In a federation of heterogeneous nodes that organize themselves, the lack of a trusted third party does not allow establishing a priori trust relationships among strangers. Automated trust negotiation (TN) is a promising approach to establish sufficient trust among parties, allowing them to access sensitive data and services in open environments. Although the literature on TN is growing, two key issues have still to be addressed. The first one concerns a typical feature of real-life negotiations: we are usually willing to trade the disclosure of personal attributes in exchange for additional services and only in a particular order (according to our preferences). The second one concerns dependability. By their nature TN systems are used in unreliable contexts where it is important not only to protect negotiations against malicious attack (self-protection), but also against accidental failures (self-healing). In this paper we address these issues proposing a novel dependable negotiation framework where services, needed credentials, and behavioral constraints on the disclosure of privileges are bundled together.     

An agent architecture for multi-attribute negotiation using incomplete preference information

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 cooperation with, among others, Dutch Telecom KPN. The approach can be characterized as cooperative one-to-one multi-criteria negotiation in which the privacy of both parties is protected as much as desired. We model a mechanism in which agents are able to use any amount of incomplete preference information revealed by the negotiation partner in order to improve the efficiency of the reached agreements. Moreover, we show that the outcome of such a negotiation can be further improved by incorporating a “guessing” heuristic, by which an agent uses the history of the opponent’s bids to predict his preferences. Experimental evaluation shows that the combination of these two strategies leads to agreement points close to or on the Pareto-efficient frontier. The main original contribution of this paper is that it shows that it is possible for parties in a cooperative negotiation to reveal only a limited amount of preference information to each other, but still obtain significant joint gains in the outcome.     

偏好向量引导的高维目标协同进化算法

多偏好向量引导的协同进化算法（PICEA-g）是将目标向量作为偏好,个体支配目标向量的个数作为适应值,以有效降低高维目标空间中非支配解的比例.但PICEA-g所获解集是近似Pareto前沿,而不是决策者真正感兴趣部分的Pareto最优解,导致算法在处理高维优化问题时性能下降和计算资源的浪费.鉴于此,提出一种基于偏好向量引导的高维目标协同进化算法（ASF-PICEA-g）：首先,利用ASF扩展函数将进化种群中的参考点映射至目标空间,并将其作为偏好向量引导种群进化的参考方向;然后,利用偏好区域选择策略获取两个临时参考点,进而构建决策者感兴趣区域（ROI）,确定随机偏好集产生的上下界范围,通过协同进化机制引导种群朝偏好区域收敛.将ASF-PICEA-g与g-NSGA-II和r-NSGA-II在3-20维的WFG系列和DTLZ系列测试函数上进行仿真实验,实验结果表明：ASF-PICEA-g在WFG系列测试函数上表现出了良好的性能,所得解集整体上优于对比算法;在DTLZ系列测试函数上略优于对比算法,尤其在10维以上目标空间,ASF-PICEA-g表现出更好的稳定性,所获解集有较好的收敛性和分布性.     

Multi-objective decision support system in numerical reliability optimization of modern electronic packaging

Nowadays, numerical prototyping methods in electronic packaging are widely used. This is mainly due to cost and time reduction and improved functionality and reliability of final products. Recently, there has been a lot of interest and work conducted on advanced numerical optimization, which can be directly applied to prototyping. So far, the optimization is focused on one criteria while neglecting problem of multi-objectivity, which is not the best approach from practical point of view. Nevertheless, such an approach is jusitified from the point of view of complex analysis, interdisciplinary issues and reduced accuracy of numerical models. In reality, there are usually many criteria which, in order to solve the problem, have to be taken into consideration. There are many multi-objective methods, of which the Pareto set approach is mostly cited in the literature. The “problem” of multi-objective optimization is that not a single optimal solution has resulted but the set of equivalent optimal solutions. This set of equivalent optimal solutions is referenced as “the Pareto set”. From the mathematical point of view, every value from this set can be treated as optimal for certain assumed constraints. However, there could be some additional conditions which cannot be applied to optimization process and some of the results from the Pareto set are more likely (i.e., the fabrication process will be more repeatable) then the others. So, the question is: which value from the Pareto set should be taken to further processing? There are two possibilities: asking an expert for the advice or use the decision making system. Decision making methods based on multi-objective optimization could be referenced as “Multiple criteria decision making” (MCDM) or “Multiple criterial decision aid” (MCDA) systems. There are several groups of these methods: (a) mathematical multi-objective programming, (b) artificial intelligence methods, (c) simple arithmetic methods, and (d) advanced mathematical methods. The current paper will focus on designing and application of the decision support system for multi-objective numerical reliability optimization of electronic packaging. The work will be based on the self developed numerical tool based on Python Scrippting language and will present its application to selected microelectronic packages based on its numerical model elaborated in ABAQUS.     

A negotiation framework for linked combinatorial optimization problems

We tackle the challenge of applying automated negotiation to self-interested agents with local but linked combinatorial optimization problems. Using a distributed production scheduling problem, we propose two negotiation strategies for making concessions in a joint search space of agreements. In the first strategy, building on Lai and Sycara (Group Decis Negot 18(2):169–187, 2009), an agent concedes on local utility in order to achieve an agreement. In the second strategy, an agent concedes on the distance in an attribute space while maximizing its local utility. Lastly, we introduce a Pareto improvement phase to bring the final agreement closer to the Pareto frontier. Experimental results show that the new attribute-space negotiation strategy outperforms its utility-based counterpart on the quality of the agreements and the Pareto improvement phase is effective in approaching the Pareto frontier. This article presents the first study of applying automated negotiation to self-interested agents each with a local, but linked, combinatorial optimization problem.     

Evolutionary multiobjective optimization using an outranking-based dominance generalization

One aspect that is often disregarded in the current research on evolutionary multiobjective optimization is the fact that the solution of a multiobjective optimization problem involves not only the search itself, but also a decision making process. Most current approaches concentrate on adapting an evolutionary algorithm to generate the Pareto frontier. In this work, we present a new idea to incorporate preferences into a multi-objective evolutionary algorithm (MOEA). We introduce a binary fuzzy preference relation that expresses the degree of truth of the predicate “x is at least as good as y”. On this basis, a strict preference relation with a reasonably high degree of credibility can be established on any population. An alternative x is not strictly outranked if and only if there does not exist an alternative y which is strictly preferred to x. It is easy to prove that the best solution is not strictly outranked. For validating our proposed approach, we used the non-dominated sorting genetic algorithm II (NSGA-II), but replacing Pareto dominance by the above non-outranked concept. So, we search for the non-strictly outranked frontier that is a subset of the Pareto frontier. In several instances of a nine-objective knapsack problem our proposal clearly outperforms the standard NSGA-II, achieving non-outranked solutions which are in an obviously privileged zone of the Pareto frontier.     

A procedure for negotiating efficient and non-efficient compromises

An interactive procedure for group decision-making problems is presented in this paper. The procedure is based on the aspiration theory and utilizes both satisficing and optimizing approaches. The possiblity of decision-makers forming coalitions is taken into account. The outcome of the modelled decision process is a compromise decision which can fulfil fairness and equity criteria. The compromise may also be an efficient solution. The procedure can be the basis for a group decision suport systems and such a system for a microcomputer network is discussed. An example of negotiations between management and trade union is presented.     

A TWO‐STAGE WIN–WIN MULTIATTRIBUTE NEGOTIATION MODEL: OPTIMIZATION AND THEN CONCESSION

Many automated negotiation models have been developed to solve the conflict in many distributed computational systems. However, the problem of finding win–win outcome in multiattribute negotiation has not been tackled well. To address this issue, based on an evolutionary method of multiobjective optimization, this paper presents a negotiation model that can find win–win solutions of multiple attributes, but needs not to reveal negotiating agents’ private utility functions to their opponents or a third‐party mediator. Moreover, we also equip our agents with a general type of utility functions of interdependent multiattributes, which captures human intuitions well. In addition, we also develop a novel time‐dependent concession strategy model, which can help both sides find a final agreement among a set of win–win ones. Finally, lots of experiments confirm that our negotiation model outperforms the existing models developed recently. And the experiments also show our model is stable and efficient in finding fair win–win outcomes, which is seldom solved in the existing models.     

基于决策者偏好区域的多目标粒子群算法研究*

多目标优化问题中,决策者往往只对目标空间的某一区域感兴趣,因此需要在这一特定的区域能够得到比较稠密的Pareto解,但传统的方法却找出全部的Pareto前沿,决策效率不高。针对该问题,给出了基于决策者偏好区域的多目标粒子群优化算法。它只求出与决策者偏好区域相关的部分Pareto最优集,从而减少了进化代数,加快收敛速度,有利于决策者进行更有效的决策。算法把解与偏好区域的距离作为影响引导者选择和剪枝策略的一个因素,运用格栅方法实现解在Pareto边界分布的均匀性。仿真结果表明该算法是有效的。     

Multiobjective Markov chains optimization problem with strong Pareto frontier: Principles of decision making

In this paper, we present a novel approach for computing the Pareto frontier in Multi-Objective Markov Chains Problems (MOMCPs) that integrates a regularized penalty method for poly-linear functions. In addition, we present a method that make the Pareto frontier more useful as decision support system: it selects the ideal multi-objective option given certain bounds. We restrict our problem to a class of finite, ergodic and controllable Markov chains. The regularized penalty approach is based on the Tikhonov’s regularization method and it employs a projection-gradient approach to find the strong Pareto policies along the Pareto frontier. Different from previous regularized methods, where the regularizator parameter needs to be large enough and modify (some times significantly) the initial functional, our approach balanced the value of the functional using a penalization term (μ) and the regularizator parameter (δ) at the same time improving the computation of the strong Pareto policies. The idea is to optimize the parameters μ and δ such that the functional conserves the original shape. We set the initial value and then decrease it until each policy approximate to the strong Pareto policy. In this sense, we define exactly how the parameters μ and δ tend to zero and we prove the convergence of the gradient regularized penalty algorithm. On the other hand, our policy-gradient multi-objective algorithms exploit a gradient-based approach so that the corresponding image in the objective space gets a Pareto frontier of just strong Pareto policies. We experimentally validate the method presenting a numerical example of a real alternative solution of the vehicle routing planning problem to increase security in transportation of cash and valuables. The decision-making process explored in this work correspond to the most frequent computational intelligent models applied in practice within the Artificial Intelligence research area.     

Multi-criteria group decision making under uncertainty with application to air traffic safety

There are many methods for solving problems of multi-criteria group decision making under uncertainty conditions. It is quite often that decision makers cannot formulate unequivocally their individual preference relations between variants. Analysing the causes of a serious aircraft incident is an example where a group of experts is required to have a very detailed yet interdisciplinary knowledge. Obviously, each expert has only a fraction of such knowledge. Hence, experts can make fuzzy evaluations when they are not sure about them or it is not possible to gain full knowledge. There is a need for a method that in such a case takes into account the strength of preference expressed in the significance of each criterion. Both the significance of criteria and the scores assigned to variants can be represented using fuzzy expressions.The proposed method reflects the problems of decision making when both objective (represented using non-fuzzy expressions) and subjective (represented using linguistic expressions) criteria, are involved. The proposed method enables to obtain a solution without having to conduct negotiations between decision makers. This is of advantage when there is a risk that some experts will be dominated by others. The method not only helps define a single preferred solution but also create the preference relation within a group. By applying this method, it is possible to reproduce the actual preference relations of individual decision makers. Presenting them to decision makers may induce them to change their evaluation of the weights of criteria or how they score variants.     

Quantum solution to a class of two-party private summation problems

In this paper, we define a class of special two-party private summation (S2PPS) problems and present a common quantum solution to S2PPS problems. Compared to related classical solutions, our solution has advantages of higher security and lower communication complexity, and especially it can ensure the fairness of two parties without the help of a third party. Furthermore, we investigate the practical applications of our proposed S2PPS protocol in many privacy-preserving settings with big data sets, including private similarity decision, anonymous authentication, social networks, secure trade negotiation, secure data mining.     

Trust-/spl Xscr/;: a peer-to-peer framework for trust establishment

We present Trust-/spl Xscr/;, a comprehensive XML-based framework for trust negotiations, specifically conceived for a peer-to-peer environment. Trust negotiation is a promising approach for establishing trust in open systems like the Internet, where sensitive interactions may often occur between entities at first contact, with no prior knowledge of each other. The framework we propose takes into account all aspects related to negotiations, from the specification of the profiles and policies of the involved parties to the selection of the best strategy to succeed in the negotiation. Trust-/spl Xscr/; presents a number of innovative features, such as the support for protection of sensitive policies, the use of trust tickets to speed up the negotiation, and the support of different strategies to carry on a negotiation. In this paper, besides presenting the language to encode security information, we present the system architecture and algorithms according to which negotiations take place.