基于偏好的双边多议题协商优化

提出一种优化的自动协商模型。Agent在信知不完全的情况下通过学习交互历史和在线协商信息获取对手的偏好,结合贝叶斯方法和支持向量机学习对手偏好,基于保留值和权重提出一种决策模型。通过实验比较和分析,该模型能有效降低协商次数,提高协商双方的联合效用。在信息保密和先验知识未知的环境下,该模型仍然表现出了较高的效用和效率。     

Bargaining with incomplete information

This paper analyses the process and outcomes of competitive bilateral negotiation for a model based on negotiation decision functions. Each agent has time constraints in the form of a deadline and a discounting factor. The importance of information possessed by participants is highlighted by exploring all possible incomplete information scenarios – both symmetric and asymmetric. In particular, we examine a range of negotiation scenarios in which the amount of information that agents have about their opponent’s parameters is systematically varied. For each scenario, we determine the equilibrium solution and study its properties. The main results of our study are as follows. Firstly, in some scenarios agreement takes place at the earlier deadline, while in others it takes place near the beginning of negotiation. Secondly, in some scenarios the price surplus is split equally between the agents while in others the entire price surplus goes to a single agent. Thirdly, for each possible scenario, the equilibrium outcome possesses the properties of uniqueness and symmetry – although it is not always Pareto optimal. Finally, we also show the relative impacts of the opponent’s parameters on the bargaining outcome.     

A win–win method for multi‐party negotiation support

In this paper, we introduce an interactive multi‐party negotiation support method for decision problems that involve multiple, conflicting linear criteria and linear constraints. Most previous methods for this type of problem have relied on decision alternatives located on the Pareto frontier; in other words, during the negotiation process the parties are presented with new Pareto optimal solutions, requiring the parties to sacrifice the achievement of some criteria in order to secure improvements with respect to other criteria. Such a process may be vulnerable to stalemate situations where none of the parties is willing to move to a potentially better solution, e.g., because they perceive – rightly or wrongly ? that they have to give up more than their fair share. Our method relies on “win–win” scenarios in which each party will be presented with “better” solutions at each stage of the negotiations. Each party starts the negotiation process at some inferior initial solution, for instance the best starting point that can be achieved without negotiation with the other parties, such as BATNA (best alternative to a negotiated agreement). In subsequent iterations, the process gravitates closer to the Pareto frontier by suggesting an improved solution to each party, based on the preference information (e.g., aspiration levels) provided by all parties at the previous iteration. The preference information that each party needs to provide is limited to aspiration levels for the objectives, and a party's revealed preference information is not shared with the opposing parties. Therefore, our method may represent a more natural negotiation environment than previous methods that rely on tradeoffs and sacrifice, and provides a positive decision support framework in which each party may be more comfortable with, and more readily accept, the proposed compromise solution. The current paper focuses on the concept, the algorithmic development, and uses an example to illustrate the nature and capabilities of our method. In a subsequent paper, we will use experiments with real users to explore issues such as whether our proposed “win–win” method tends to result in better decisions or just better negotiations, or both; and how users will react in practice to using an inferior starting point in the negotiations.     

Multi-Dimensional,MultiStep Negotiation

We present a multi-dimensional, multi-step negotiation mechanism for task allocation among cooperative agents based on distributed search. This mechanism uses marginal utility gain and marginal utility cost to structure this search process, so as to find a solution that maximizes the agents’ combined utility. These two utility values together with temporal constraints summarize the agents’ local information and reduce the communication load. This mechanism is anytime in character: by investing more time, the agents increase the likelihood of getting a better solution. We also introduce a multiple attribute utility function into negotiations. This allows agents to negotiate over the multiple attributes of the commitment, which produces more options, making it more likely for agents to find a solution that increases the global utility. A set of protocols are constructed and the experimental result shows a phase transition phenomenon as the complexity of negotiation situation changes. A measure of negotiation complexity is developed that can be used by an agent to choose an appropriate protocol, allowing the agents to explicitly balance the gain from the negotiation and the resource usage of the negotiation.This revised version was published online in August 2005 with a corrected cover date.     

A Negotiation Enabling Agent Based Infrastructure: Composition and Behavior

Software agent-based negotiation is a major method to automate the interactions in electronic marketplaces and Internet enabled communities. The traditional approach is to let the agents to interact directly. In this paper it has been investigated how a mediator agent can improve the chances to reach the agreement via bargaining. Although the ideal mathematical model was proposed in the seventies, this was never implemented as a working mechanism, due to the fact that the mediator needed information that was difficult to gather and the usual environment was not repetitive enough to consolidate this information for a fair mediation. The agent-based infrastructure proposed collects continuously data about the negotiating parties and the mediator agents use this data to reduce the exaggeration of the parties. The paper includes a mediation example and the major conclusion is that negotiation is improved by a mediator which has historical data about the negotiating parties.     

一种基于相似度方法的并发协商策略

多Agent协商是目前人工智能、电子商务等领域研究的热点问题。在电子商务活动中为了达到更好的效果,获得更多的利益,买方希望与多个卖方进行并发协商(与多个卖方同时进行协商),从中选择最佳的交易解决方案,这种需求广泛存在。本文利用相似度方法(similarity criteria)发展和评价了一种双边多议题多Agent并发协商策略,一方面使用相似度函数来计算对手的偏好类型,从而选择相应的协商策略;另一方面协商一方采用该策略后能够使得双方达成一致的可能性最大并且收益最大,从而使得一个Agent能够在半竞争、信息不完全和不确定以及存在最大协商时间的情况下,更为有效地完成多Agent并发协商。     

基于神经网络的Agent电子商务协商模型

提出一个在电子商务中能辅助用户网上采购的智能Agent协商模型,并对模型进行仿真实验,以验证模型的有效性。模型的构造基于BP神经网络,通过对用户的购物偏好训练学习,使Agent获得一组包含用户偏好特征的规则信息并作为协商过程中推理的依据;每次协商的结果都作为学习样本,以提高Agent对市场变化的适应能力。     

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.     

A formal framework for connective stability of highly decentralized cooperative negotiations

Multiagent cooperative negotiation is a promising technique for modeling and controlling complex systems. Effective and flexible cooperative negotiations are especially useful for open complex systems characterized by high decentralization (which implies a low amount of exchanged information) and by dynamic connection and disconnection of agents. Applications include ad hoc network management, vehicle formation, and physiological model combination. To obtain an effective control action, the stability of the negotiation, namely the guarantee that an agreement will be eventually reached, is of paramount importance. However, the techniques usually employed for assessing the stability of a negotiation can be hardly applied in open scenarios. In this paper, whose nature is mainly theoretical, we make a first attempt towards engineering stable cooperative negotiations proposing a framework for their analysis and design. Specifically, we present a formal protocol for cooperative negotiations between a number of agents and we propose a criterion for negotiation stability based on the concept of connective stability. This is a form of stability that accounts for the effects of structural changes on the composition of a system and that appears very suitable for multiagent cooperative negotiations. To show its possible uses, we apply our framework for connective stability to some negotiations taken from literature.     

A Market–Driven Model for Designing Negotiation Agents

Although there are many extant agent–based systems for negotiation in e–commerce, the negotiation strategies of agents in these systems are mostly static. This article presents a model for designing negotiation agents that make adjustable rates of concession by reacting to changing market situations. To determine the amount of concession for each trading cycle, these market–driven agents are guided by four mathematical functions of eagerness, trading time, trading opportunity , and competition . Trading opportunity is determined by considering: (i) number of trading partners, (ii) spreads —differences in utilities between an agent and its trading partners, and (iii) probability of completing a deal. Competition is determined by the probability that an agent is not considered the most preferred trader by other negotiating parties. Motivated by factors such as corporate policies and resource needs, eagerness represents an agent's desire to complete a deal. Agents with different time sensitivity to deadlines employ different trading strategies by making different rates of concession at different stages of negotiation. In this article, three classes of strategies with respect to remaining trading time are discussed. Theoretical analyses show that market–driven agents are designed to make prudent and appropriate amounts of concession for a given market situation.     

基于TD3算法的自动协商策略

协商是人们就某些议题进行交流寻求一致协议的过程.而自动协商旨在通过协商智能体的使用降低协商成本、提高协商效率并且优化协商结果.近年来深度强化学习技术开始被运用于自动协商领域并取得了良好的效果,然而依然存在智能体训练时间较长、特定协商领域依赖、协商信息利用不充分等问题.为此,本文提出了一种基于TD3深度强化学习算法的协商策略,通过预训练降低训练过程的探索成本,通过优化状态和动作定义提高协商策略的鲁棒性从而适应不同的协商场景,通过多头语义神经网络和对手偏好预测模块充分利用协商的交互信息.实验结果表明,该策略在不同协商环境下都可以很好地完成协商任务.     

电子商务中的自动协商

描述电子商务中基于智能体的自动协商，给出基于智能体的自动协商模型和协商智能体的体系结构，描述了一组构成协商协议的通信原语。通过约束满足机制、协商策略和提议评估算法实现自动协商。并实现了一个供应链管理中买卖自动协商的原型系统。     

Optimizing agent-based meeting scheduling through preference estimation

Meeting scheduling is a routine task that needs to be performed quite regularly and frequently within any organization. Unfortunately, this task can be quite tedious and time-consuming, potentially requiring a several rounds of negotiations among many people on the meeting date, time and place before a meeting can finally be confirmed. The objective of our research is to create an agent-based environment within which meeting scheduling can be performed and optimized. For meeting scheduling, we define optimality as the solution that has the highest average preference level among all the possible choices. Our model tries to mimic real life in that an individual's preferences are not made public. Without complete information, traditional optimal algorithms, such as A^* will not work. In this paper, we present a novel “preference estimation” technique that allows us to find optimal solutions to negotiations problems without needing to know the exact preference models of all the meeting participants beforehand. Instead, their preferences are “estimated” and built on the fly based on observations of their responses during negotiation. Another unique contribution is the use of “preference rules” that allow preferences to change dynamical as scheduling decisions are made. This mimics changing preferences as schedule gets filled. This paper uses two negotiation algorithms to compare the effect of “preference estimation”—one that is based on negotiation through relaxation and the other that extends this with preference estimations. Simulations were then performed to compare these algorithms.     

基于时间约束的不完全信息的战略协商模型

在实证的一对一协商中，协商Agent不仅要面临自己的最后期限的压力，同时又要预测协商对手的最后期限和其类型，协商Agent的协商战略必须满足理性与均衡的要求。提出了通过形式化的方法建立轮流出价协商模型，给出了轮流出价协商战略均衡的条件定义，求出了基于时间限制的不完全信息环境下满足均衡组合的协商战略，建立了依据均衡战略的实用化协商算法，最后分析了该算法产生的实验数据，并在相同环境下与Zeus协商模型比较显示，依从本模型的均衡战略的协商Agent能根据对对手的不确定信息的信念动态地采取行动，以获得最大的期望收益。     

基于区间相似度的动态服务协商模型

针对服务协商中信息的不对称性、协商环境的动态性以及QoS属性的不确定性和模糊性,提出基于区间相似度的动态服务协商模型。利用区间相似度和区间估计预测对方的协商策略,以此制定己方的最优反策略。算例分析表明,在动态服务协商环境下,该模型比静态协商模型更贴近现实的协商行为,能有效提高协商效率。     

Time-bound negotiation framework for electronic commerce agents

For efficient and informative coordination of agents especially in electronic commerce environment, a time-bound agent negotiation framework is proposed utilizing a time-based commitment scheme. By attaching commitment duration to agent messages, the traditional contract net protocol is extended to a time-bound negotiation framework (TBNF). The proposed negotiation framework has a new message type which allows for parties to agree upon the extension of a commitment duration, and a novel commitment concept in the form of negative commitment. The semantics of the messages with the commitment duration are interpreted, and then the three typical negotiation protocols are formally defined and compared — nothing-guaranteed protocol, acceptance-guaranteed protocol, and finite-time guarantee protocol — which can be incorporated into TBNF. The TBNF should provide a background for efficient and effective electronic commerce negotiation while accommodating each agent's adaptive negotiation strategy.     

Managing commitments in multiple concurrent negotiations

Automated negotiation by software agents is a key enabling technology for agent mediated e-commerce. To this end, this paper considers an important class of such negotiations – namely those in which an agent engages in multiple concurrent bilateral negotiations for a good or service. In particular, we consider the situation in which a buyer agent is looking for a single service provider from a number of available ones in its environment. By bargaining simultaneously with these providers and interleaving partial agreements that it makes with them, a buyer can reach good deals in an efficient manner. However, a key problem in such encounters is managing commitments since an agent may want to make intermediate deals (so that it has a definite agreement) with other agents before it gets to finalize a deal at the end of the encounter. To do this effectively, however, the agents need to have a flexible model of commitments that they can reason about in order to determine when to commit and to decommit. This paper provides and evaluates such a commitment model and integrates it into a concurrent negotiation model.     

A context-aware approach to automated negotiation using reinforcement learning

Agents negotiate depending on individual perceptions of facts, events, trends and special circumstances that define the negotiation context. The negotiation context affects in different ways each agent’s preferences, bargaining strategies and resulting benefits, given the possible negotiation outcomes. Despite the relevance of the context, the existing literature on automated negotiation is scarce about how to account for it in learning and adapting negotiation strategies. In this paper, a novel contextual representation of the negotiation setting is proposed, where an agent resorts to private and public data to negotiate using an individual perception of its necessity and risk. A context-aware negotiation agent that learns through Self-Play and Reinforcement Learning (RL) how to use key contextual information to gain a competitive edge over its opponents is discussed in two levels of temporal abstraction. Learning to negotiate in an Eco-Industrial Park (EIP) is presented as a case study. In the Peer-to-Peer (P2P) market of an EIP, two instances of context-aware agents, in the roles of a buyer and a seller, are set to bilaterally negotiate exchanges of electrical energy surpluses over a discrete timeline to demonstrate that they can profit from learning to choose a negotiation strategy while selfishly accounting for contextual information under different circumstances in a data-driven way. Furthermore, several negotiation episodes are conducted in the proposed EIP between a context-aware agent and other types of agents proposed in the existing literature. Results obtained highlight that context-aware agents do not only reap selfishly higher benefits, but also promote social welfare as they resort to contextual information while learning to negotiate.     

Agent negotiation based ontology refinement process and mechanisms for service applications

Nowadays organisations are willing to outsource their business processes as services and make them accessible via the Web. In doing so, they can dynamically combine individual services to their service applications. However, unless the data on the Web can be meaningfully shared and is interpretable, this objective cannot be realised. In this paper, a new agent-based approach for managing ontology evolution in a Web services environment is exploited. The proposed approach has several key characteristics such as flexibility and extensibility that differentiate this research from others. The refinement mechanisms which cope with an evolving ontology are carefully examined. The novelty of our work is that inter-processes between different ontologies are studied from the agent’s perspective. Based on this perspective, an agent negotiation model is applied to reach an agreement regarding ontology discrepancy in an application. The efficiency and effectiveness of reaching an agreement over an ontology dispute is leveraged by the private negotiation strategy applied in the argumentation approach. An extended negotiation strategy is discussed to enable sufficient information in decision making at each negotiation round. A case study is presented to demonstrate ontology refinement in a Web services environment.     

The impact of available information on negotiation results

Negotiation is the most famous tool for reaching an agreement between parties. Usually, the different parties can be modeled as a buyer and a seller, who negotiate about the price of a given item. In most cases, the parties have incomplete information about one another, but they can invest money and efforts in order to acquire information about each other. This leads to the question of how much each party will be willing to invest on information about its opponent, prior to the negotiation process. In this paper, we consider the profitability of automated negotiators acquiring information on their opponents. In our model, a buyer and a seller negotiate on the price of a given item. Time is costly, and incomplete information exists about the reservation price of both parties. The reservation price of the buyer is the maximum price it is willing to pay for an item or service, and the reservation price of the seller is the minimum price it is willing to receive in order to sell the item or service. Our research is based on Cramton’s symmetrical protocol of negotiation that provides the agents with stable and symmetric strategies, and involves a delay in proposing an offer for signaling. The parties in Cramton’s model delay their offers in order to signal their strength, and then an agreement is reached after one or two offers. We determine the Nash equilibrium for agents that prefer to purchase information. Then, in addition to the theoretical background, we used simulations to check which type of equilibrium will actually be obtained. We found that in most of the cases, each agent will prefer to purchase information only if its opponent does. The reason for these results lies in the fact that an agent that prefers to purchase information according to a one-side method, signals its weakness and thereby reduces its position in the negotiation. Our results demonstrate the efficiency of joint information acquisition by both agents, but they also show that one-sided information purchasing may be inefficient, if the acquisition activity is revealed by the opponent, which causes it to infer that the informed agent is relatively weak.