Algorithm selection in bilateral negotiation

Despite the abundance of strategies in the multi-agent systems literature on repeated negotiation under incomplete information, there is no single negotiation strategy that is optimal for all possible domains. Thus, agent designers face an “algorithm selection” problem—which negotiation strategy to choose when facing a new domain and unknown opponent. Our approach to this problem is to design a “meta-agent” that predicts the performance of different negotiation strategies at run-time. We study two types of the algorithm selection problem in negotiation: In the off-line variant, an agent needs to select a negotiation strategy for a given domain but cannot switch to a different strategy once the negotiation has begun. For this case, we use supervised learning to select a negotiation strategy for a new domain that is based on predicting its performance using structural features of the domain. In the on-line variant, an agent is allowed to adapt its negotiation strategy over time. For this case, we used multi-armed bandit techniques that balance the exploration–exploitation tradeoff of different negotiation strategies. Our approach was evaluated using the GENIUS negotiation test-bed that is used for the annual international Automated Negotiation Agent Competition which represents the chief venue for evaluating the state-of-the-art multi-agent negotiation strategies. We ran extensive simulations using the test bed with all of the top-contenders from both off-line and on-line negotiation tracks of the competition. The results show that the meta-agent was able to outperform all of the finalists that were submitted to the most recent competition, and to choose the best possible agent (in retrospect) for more settings than any of the other finalists. This result was consistent for both off-line and on-line variants of the algorithm selection problem. This work has important insights for multi-agent systems designers, demonstrating that “a little learning goes a long way”, despite the inherent uncertainty associated with negotiation under incomplete information.     

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.     

STRATUM: A METHODOLOGY FOR DESIGNING HEURISTIC AGENT NEGOTIATION STRATEGIES

Automated negotiation is a powerful (and sometimes essential) means for allocating resources among self-interested autonomous software agents. A key problem in building negotiating agents is the design of the negotiation strategy, which is used by an agent to decide its negotiation behavior. In complex domains, there is no single, obvious optimal strategy. This has led to much work on designing heuristic strategies, where agent designers usually rely on intuition and experience. In this article, we introduce STRATUM, a methodology for designing strategies for negotiating agents. The methodology provides a disciplined approach to analyzing the negotiation environment and designing strategies in light of agent capabilities and acts as a bridge between theoretical studies of automated negotiation and the software engineering of negotiation applications. We illustrate the application of the methodology by characterizing some strategies for the Trading Agent Competition and for argumentation-based negotiation.     

一种自适应的信任协商协议*

在分析现有的信任管理技术和自动信任协商技术的基础上,分别提出了一种支持信任管理的协商策略和一种最优化的信任协商策略,在此基础上设计了一种自适应信任协商协议。该协议可以实现多协议协商,同时融合了信任管理和自动信任协商系统的优点,能够实现安全域内和跨安全域的信任协商功能,具有良好的可扩展性、灵活性和隐私保护。     

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.     

基于博弈分析的电子商务自动协商系统

为提高基于拍卖机制的电子商务自动协商系统效率，该文以非合作博弈论为基础提出了基于博弈分析的自动协商Agent模型及基于拍卖机制和博弈分析的自动投标协商算法，实现了一个基于拍卖机制和博弈分析的电子商务自动协商原型系统，并应用在一个企业敏捷供应链管理系统中实现自动协商交易。     

Ontologies for supporting negotiation in e-commerce

In this paper we present our experience in applying Semantic Web technology to automated negotiation. This result is a novel approach to automated negotiation, that is particularly suitable to open environments such as the Internet. In this approach, agents can negotiate in any type of marketplace regardless of the negotiation mechanism in use. In order to support a wide variety of negotiation mechanisms, protocols are not hard-coded in the agents participating to negotiations, but are expressed in terms of a shared ontology, thus making this approach particularly suitable for applications such as electronic commerce. The paper describes a novel approach to negotiation, where the negotiation protocol does not need to be hard-coded in agents, but it is represented by an ontology: an explicit and declarative representation of the negotiation protocol. In this approach, agents need very little prior knowledge of the protocol, and acquire this knowledge directly from the marketplace. The ontology is also used to tune agents’ strategies to the specific protocol used. The paper presents this novel approach and describes the experience gained in implementing the ontology and the learning mechanism to tune the strategy.     

A knowledge-based framework for complex, proactive and service-oriented e-negotiation systems

Advances in information technology and knowledge management change the way that e-negotiations, which constitute an important aspect of worldwide e-trading, can be structured and represented. In this paper, a novel approach that focuses on knowledge modeling, formalization, representation and management in the domain of e-negotiation is described. The proposed approach exploits Ontologies, Service Oriented Architectures, Semantic Web Services, software agent platforms, and Knowledge-Bases to construct a framework that favors dynamically adapted negotiation protocols, negotiation process visualization and management, modeling and preference elicitation of the negotiated object and automatic deployment of negotiation interfaces. Negotiation process, protocol and strategy are examined, and a hybrid approach that integrates rules and workflow diagrams to describe and represent them is introduced.     

基于移动Agent的自动协商应用

主要研究电子商务中的协商问题,为实现自动协商提供了一个可行的方向。通过分析目前电子商务的协商现状及所存在的问题,提出将移动Agent技术引入电子商务,为B2C电子商务中一对多的协商问题自动化指出了一个实现方向,并得出基于移动Agent的自动协商系统的建设目标及特点。这将对于实现电子商务协商的自动化,提供有力的理论支持及发展方向。     

基于学习的多议题协商

随着在线交易越来越普遍,如何有效地将先进的agent技术运用于电子商务协商领域,已经成为经济学家和计算机学者共同研讨的一个主要方向。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预测和更新对方agent的信念,使得每个agent通过学习来协调自身的行为,从而缩短了协商时间,提高了协商效率,更能有效地完成协商目的。并实验说明了其可行性。     

一种基于属性的自动信任协商方法

提出一种基于属性的信任协商方法。协商的双方首先交换包含多个加密属性的信任证书,然后双方根据自己的访问控制策略多次交换密钥逐步向对方显示出自己的属性。在这种协商方法中,双方可以控制自己的信任书中属性值的出示,且该协商方法使用椭圆曲线密钥交换算法产生会话密钥,计算量比较小。     

多Agent系统中基于信任度评估模型的自动信任协商

在动态开放的系统中．由于Agent间交互存在着不确定性,安全成为一个重要问题。在现有的自动信任协商的基础上．考虑主观信任的作用．提出基于信任度评估模型的自动信任协商框架．详细介绍框架中的主要成分及其功能．着重讨论基于信任度评估模型的访问控制,以及在信任度评估模型基础上的两种协商对策：基于信任度评估模型的积极对策和基于信任度评估模型的谨慎对策。分别详细介绍采取上述两种对策的协商过程．并结合一应用实例说明基于信任度评估模型的积极对策的协商过程。     

A reinforcement learning approach to improve the argument selection effectiveness in argumentation-based negotiation

Deciding what argument to utter during a negotiation is a key part of the strategy to reach an expected agreement. An agent, which is arguing during a negotiation, must decide what arguments are the best to persuade the opponent. In fact, in each negotiation step, the agent must select an argument from a set of candidate arguments by applying some selection policy. By following this policy, the agent observes some factors of the negotiation context (for instance, trust in the opponent and expected utility of the negotiated agreement). Usually, argument selection policies are defined statically. However, as the negotiation context varies from a negotiation to another, defining a static selection policy is not useful. Therefore, the agent should modify its selection policy in order to adapt it to the different negotiation contexts as the agent gains experience. In this paper, we present a reinforcement learning approach that allows the agent to improve the argument selection effectiveness by updating the argument selection policy. To carry out this goal, the argument selection mechanism is represented as a reinforcement learning model. We tested this approach in a multiagent system, in a stationary as well as in a dynamic environment. We obtained promising results in both.     

Negotiating with bounded rational agents in environments with incomplete information using an automated agent

Many tasks in day-to-day life involve interactions among several people. Many of these interactions involve negotiating over a desired outcome. Negotiation in and of itself is not an easy task, and it becomes more complex under conditions of incomplete information. For example, the parties do not know in advance the exact tradeoff of their counterparts between different outcomes. Furthermore information regarding the preferences of counterparts might only be elicited during the negotiation process itself. In this paper we propose a model for an automated negotiation agent capable of negotiating with bounded rational agents under conditions of incomplete information. We test this agent against people in two distinct domains, in order to verify that its model is generic, and thus can be adapted to any domain as long as the negotiators' preferences can be expressed in additive utilities. Our results indicate that the automated agent reaches more agreements and plays more effectively than its human counterparts. Moreover, in most of the cases, the automated agent achieves significantly better agreements, in terms of individual utility, than the human counterparts playing the same role.     

基于多Agent系统的自动协商机制及通用协商框架

自动协商是多Agent系统实现协作的关键环节。目前,对协商模型的研究大部分是在特定的应用系统中针对具体协商背景进行研究,协商模型的通用性较差。本文分析了多Agent系统协商机制的三个方面:Agent通信语言、通信方式和交互协议。在此基础上,给出了基于多Agent的通用协商框架GNF,该协商框架包括抽象协商过程、协商规则分类和协商协议。最后,给出了一个基于通用协商框架GNF的原型系统——采用Jess规则引擎实现的多Agent的商品交易系统。     

基于协同进化遗传算法的多议题谈判

以协同进化遗传算法模拟自动谈判是目前智能计算和多Agent系统等领域研究的新课题。针对现有文献仅模拟单议题谈判的情况,该文提出基于协同进化遗传算法和适应度共享小生境技术的多议题谈判模拟算法,以轮流出价谈判协议为例进行仿真实验。实验结果表明,该算法能在策略种群中形成局部小生境,生成近似Pareto最优的策略集。     

一种基于动态规划的自动信任协商策略

动态规划是解决多阶段决策过程最优化的一种数学方法,可将其运用到自动信任协商中。针对目前有关协商策略的研究中没有区分信任凭证的敏感度和格式的问题,引入披露开销的概念,设计了一种新的协商策略。该策略采用动态规划的思想,基于与/或图建模,分解协商过程,自底向上求解最小开销的凭证披露序列。经证明,该策略是可采纳且高效的,能保障协商的安全性和提高协商的效率。     

Autonomous Adaptive Agents for Single Seller Sealed Bid Auctions

In developing open, heterogeneous and distributed multi-agent systems researchers often face a problem of facilitating negotiation and bargaining amongst agents. It is increasingly common to use auction mechanisms for negotiation in multi-agent systems. The choice of auction mechanism and the bidding strategy of an agent are of central importance to the success of the agent model. Our aim is to determine the best agent learning algorithm for bidding in a variety of single seller auction structures in both static environments where a known optimal strategy exists and in complex environments where the optimal strategy may be constantly changing. In this paper we present a model of single seller auctions and describe three adaptive agent algorithms to learn strategies through repeated competition. We experiment in a range of auction environments of increasing complexity to determine how well each agent performs, in relation to an optimal strategy in cases where one can be deduced, or in relation to each other in other cases. We find that, with a uniform value distribution, a purely reactive agent based on Cliff’s ZIP algorithm for continuous double auctions (CDA) performs well, although is outperformed in some cases by a memory based agent based on the Gjerstad Dickhaut agent for CDA.     

A fuzzy system approach to multilateral automated negotiation in B2C e-commerce

Software agents in e-commerce systems are assigned to the participants. Buyer and supplier agents into multi-agent system architecture of the e-commerce system negotiate with others through an automated negotiation mechanism. In this study, an automated negotiation to interact between buyer and supplier and attain agreement for both is presented. A fuzzy inference system was used to automate negotiation process and consider two effective factors in the negotiation process: requirements and preferences. Requirements are qualitative or quantitative values which the participants assign to the issues of negotiation. Preferences of the participants are priorities assigned by them to issues. These values express an importance measure of issues from a participant perspective. Proposed model applies different fuzzy inference system (FIS) schemes for qualitative and quantitative negotiation issues to enhance the satisfaction level of the buyer and supplier. The FISs infer based on the preferences and requirements of both parties. Additionally, analytic hierarchy process was used to get preferences of the issues. In this proposal, mediator uses issue trade-offs strategy in which multiple issues are traded-offs against one another. The model applies a fuzzy system approach to make trade-offs.