"Team negotiation: An examination of integrative and distributive bargaining": Correction.

Reports an error in "Team negotiation: An examination of integrative and distributive bargaining" by Leigh Thompson, Erika Peterson and Susan E. Brodt (Journal of Personality and Social Psychology, 1996[Jan], Vol 70[1], 66-78). Susan E. Brodt's department affiliation was listed incorrectly on p. 66. Her correct affiliation is The Fuqua School of Business, Duke University. (The following abstract of the original article appeared in record 1996-01707-006.) Two experiments compared the effectiveness of team and solo negotiators in integrative and distributive bargaining. When at least 1 party to a negotiation was a team, joint profit increased. Teams, more than solos, developed mutually beneficial trade-offs among issues and discovered compatible interests. The presence of at least 1 team increased information exchange and accuracy in judgments about the other party's interests in comparison with solo negotiations. The belief by both teams and solos that teams have a relative advantage over solo opponents was not supported by actual outcomes. Unexpectedly, neither private meetings nor friendships among team members improved the team's advantage. Teams of friends made less accurate judgments and reached fewer integrative agreements compared to teams of nonfriends. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Development of negotiation expert system for subtransmission system planning

Recently, the task of planning in power systems is becoming a very complicated process for utility planners. This kind of planning has many objectives to accomplish. This paper employs the negotiation algorithm, which was proposed by the Chen-Ching Liu group for subtransmission power system planning to create a plan having many objectives. First, the goal-decision network (GDN) is constructed to model this planning problem, and the negotiation algorithm is applied to utilize both subtransmission system planning GDN, which attempts to capture its knowledge, and negotiation operators, which search for the most feasible and promising decisions in this planning GDN. Finally, the negotiation expert system is demonstrated for the subtransmission system planning using a real system.     

Applying Dialectic Agents to Argumentation in E-Commerce

Reaching an agreement or understanding through argumentation is an important aspect of decision making in a virtual society as well as in our real society. In this paper, we consider compromise (Aufheben) and concession (weaker Aufheben) as a simple form of Hegelian dialectical reasoning, which we think are desiderata to deliberate or cognitive agents. We then propose an argument-based agent system that allows for the issue modification among agents concerned during argumentation and reaching an agreement or understanding through argumentation with the dialectical reasoning capability. We illustrate its potential usefulness by showing applications to seller and buyer agents and traveling salesman agents in e-commerce.     

Trends in distributed artificial intelligence

Distributed artificial intelligence (DAI) is a subfield of artificial intelligence that deals with interactions of intelligent agents. Precisely, DAI attempts to construct intelligent agents that make decisions that allow them to achieve their goals in a world populated by other intelligent agents with their own goals. This paper discusses major concepts used in DAI today. To do this, a taxonomy of DAI is presented, based on the social abilities of an individual agent, the organization of agents, and the dynamics of this organization through time. Social abilities are characterized by the reasoning about other agents and the assessment of a distributed situation. Organization depends on the degree of cooperation and on the paradigm of communication. Finally, the dynamics of organization is characterized by the global coherence of the group and the coordination between agents. A reasonably representative review of recent work done in DAI field is also supplied in order to provide a better appreciation of this vibrant AI field. The paper concludes with important issues in which further research in DAI is needed.     

多人冲突决策研究综述

对多人冲突决策的研究现况进行了综述，给出了冲突决策的一些基本概念，并分析了国内外的研究进展和发展趋势。     

基于多议题协商的贝叶斯学习

随着Internet的日益完善和电子商务的普及,如何快速、高效地进行agent协商学习是必须面对和解决的一个重要问题。文中从买方agent的观点出发,在协商过程中采用贝叶斯学习机制进行在线更新对方agent的信念,从而缩短了协商时间,提高了协商效率,并实验说明了其可行性。     

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.     

基于信任度的协同商务链自学习协商研究

在传统合同网协议的基础上，构建了协同商务链自学习协商框架．借鉴群体智能技术中的响应阀值概念，提出了基于信任度的自学习协商模型，同时给出实现该模型的算法和步骤．根据协商过程中投标主体完成任务的情况，实时更新其信任度值．基于此动态值，管理者可以有效的选择合适的投标主体，提高了协商效率，并减少协商时间和成本，从而达到减少通信量和保证协同商务链上各主体利益的目的．最后，通过仿真实验，从通信量、时间和任务完成数3个指标来进行比较．结果表明：该模型可以有效缓解协商过程中的管理器瓶颈和信息拥塞问题，相对于单任务双方协商模型，其在解决多主体、任务不断变动的动态环境下的协商问题有明显的优势．     

基于时态逻辑的自动信任协商模型*

通过分析自动信任协商系统中的行为抽象,形式化定义了协商模式.基于扩展的一阶时态逻辑给出了自动信任协商系统的抽象模型,分析了模型的语法和语义,为实现自动信任协商系统提供了有效的工具.     

基于两阶段优化模型的电动汽车充电站放电策略

随着电动汽车的普及,合理制定其充放电策略,实现主动配电网和充电站的双赢成为电动汽车负荷并网的研究重点,为此提出一种充电站充放电计划的两阶段优化模型。该模型综合考虑了主动配电网和充电站双方的利益,并计及了电动汽车实际负荷与预测负荷不符的情况。在日前优化阶段,以成本分析为基础,采用Nash谈判法求解配电网和充电站的多目标优化,利用Karush-Kuhn-Tucker条件表示Pareto前沿。在日内优化阶段,在不改变日前优化确定的充放电计划基础上,根据实时负荷,采用Q-learning算法,进行充电站充电服务优化。最后构建配电网系统算例,对提出的优化模型进行仿真,验证了本文方法的有效性。