Agent口碑辩论模型研究

基于辩论的Agent谈判能提高Agent在动态环境下的谈判效率,保证谈判效果。口碑则能强化辩论效果。为保证模型的整体性,将对Agent口碑辩论模型的阐述分为描述和评价两个部分;对口碑进行分类,结合形式化理论,提出Agent口碑辩论模型的描述部分;提出口碑辩论力度的概念,并对其进行量化,提出Agent口碑辩论模型的评价部分;最后通过算例对模型进行验证。     

基于辩论的多Agent商务谈判认知模型

针对在基于辩论的谈判中如何表示Agent的认知结构和决策机制的问题,提出一个应用于商务谈判的Agent的认知决策模型。该模型由卖方和买方Agent两部分构成,其中,Agent的认知结构用自我目标认知以及对对方的目标认知等集合来表示,Agent的决策用多目标优化模型来辅助进行。基于辩论的谈判实例验证了该模型的实用性。     

基于辩论的社会性Agent间的谈判模型

通过对Agent间存在的有关社会性的关键元素进行分析,建立了基于辩论的社会性Agent间的谈判模型,并建立了相关的决策函数;然后通过一个存在于多Agent社会中而具有社会性的Agent间的谈判实例,在假设了相关数据的基础上对模型和决策函数进行了举例计算,分析验证了其有效性,并在对相关的谈判原语作出定义的基础上对此谈判实例的辩论对话过程进行了模拟。     

基于辩论的自动谈判模型

自动谈判是商务活动中最富智能性和创造性的环节,是智能商务模式中亟需解决的关键技术.主要讨论了基于辩论的自动谈判问题,提出了相应的谈判模型和谈判Agent的体系结构模型,并详述了辩论式谈判机制,最后设计了一个辩论式谈判实例.     

基于案例匹配度的Agent劝说机制

自动谈判中的Agent劝说能充分提高其在动态环境下的谈判效率,保证谈判效果,同时极大节约谈判成本。案例推理技术能使Agent将谈判中遇到的新案例与以往成功案例进行比对,根据结果做出较优的选择,从而促使Agent之间取得更佳的合作效果。对Agent劝说中的案例匹配度的概念进行了界定;结合形式逻辑理论,建立了基于案例匹配度的Agent劝说模型,并通过举例对模型进行了阐述;提出了模型中关键指标的计算方法,并通过算例进一步给出了相应的评价体系和交互机制;最后进行了验证,实现了基于案例匹配度的Agent劝说机制的构建。实验结果表明,该机制能有效减少系统开销。     

基于辩论的Agent谈判中威胁及其评价的形式化模型

针对电子商务中Agent间谈判使用最为重要且普遍的威胁的辩论方式进行了研究,提出了一种有关威胁的形式化模型,并结合现实生活中的交易实例进行了说明,以验证此模型的有效性。在此基础上,提出了一种有关威胁的辩论力度的计算的形式化模型,并在做出相关假定和给出相关的假设数据的基础上,以一个算例对模型进行计算和分析,以验证此模型的正确性。最后,得出相关结论,认为其在交易伙伴的选择方面具有传统的谈判方式所无法比拟的优势,并对进一步的研究方向进行了探讨。     

基于Agent正面情绪变化的劝说及评价模型

Agent劝说在提高电子商务谈判自动化程度的同时也能相应提高其理性程度,因此受到广泛关注。Agent情绪变化则能影响劝说的进程和效果。对Agent劝说中的情绪变化进行分类;结合形式化理论,提出了基于Agent正面情绪变化的劝说模型,并对模型进行举例说明;为使该劝说模型有效,提出了Agent正面情绪变化度的概念;在此基础上,构建了相应的评价模型;最后通过算例对模型的有效性进行了验证。     

Agent劝说中的积极舆论模型及其评价

Agent劝说中的舆论能发挥其人工智能优势,同时节约谈判成本和提高谈判效率。为此,给出Agent劝说中舆论的定义,并将Agent劝说中的舆论分为积极舆论和消极舆论,在定义和分类的基础上,结合形式逻辑理论,提出Agent劝说中的积极舆论模型,并通过相应的举例对模型进行解释。针对该模型,提出相应的评价体系,并通过相应的算例对该体系进行验证。结果表明,该模型和评价体系能促使Agent劝说进一步快速高效完成。     

基于Agent的辩论谈判系统的协议研究

协议是谈判支持系统实现的基本条件之一。依据经典的BDI（信念、愿望、意图）理论和已有的协议,将人类辩论思想融入,给出了协议的基本语言和逻辑框架,提出了相应的状态转换图,对状态转换图中的推理规则进行了详细阐述,从而构建了更加完整的基于Agent的辩论谈判系统协议。最后结合已有的策略和模型,以系统实现的方式对协议的合理性和有效性进行了验证,并指出了下一步研究方向。     

多Agent谈判：一种基于模糊逻辑控制的方法

在分析各种Agent谈判模型不足的基础上，首先给出基于黑板模式的谈判Agent体系结构，然后设计了一个通用的谈判Agent建议的综合评价方法。此外，应用标准可加性模型原理设计了谈判Agent的策略模型，从而构建了应用范围广泛的自动化谈判Agent模型。     

Reward and Penalty Functions in Automated Negotiation

Automated negotiation is very important for organizing decentralized systems such as e‐business, p2p systems, cloud computing, and so on. During the course of a negotiation, reward and penalty can be used to increase the chance of reaching agreements between negotiating agents, but have not been applied into automated negotiation systems well, especially integrating both in a single negotiation system. Thus, in this work we make an effort to reveal how the reward increases the acceptability of an offer and how the penalty decreases the deniability of an offer. More specifically, our study shows that the degree, to which a reward and a penalty influence the outcome, depends on the greedy degree for the reward and the creditable degree on the penalty. Therefore, if we know an offeree's utilities of accepting and denying an offer, the greedy degree for reward and the creditable degree on penalty, we can calculate how much reward and penalty the offerer agent needs to change the offeree's mind (i.e., from denying to accepting).     

Argumentation-based negotiation planning for autonomous agents

When we negotiate, the arguments uttered to persuade the opponent are not the result of an isolated analysis, but of an integral view of the problem that we want to agree about. Before the negotiation starts, we have in mind what arguments we can utter, what opponent we can persuade, which negotiation can finish successfully and which cannot. Thus, we plan the negotiation, and in particular, the argumentation. This fact allows us to take decisions in advance and to start the negotiation more confidently. With this in mind, we claim that this planning can be exploited by an autonomous agent. Agents plan the actions that they should execute to achieve their goals. In these plans, some actions are under the agent's control, while some others are not. The latter must be negotiated with other agents. Negotiation is usually carried out during the plan execution. In our opinion, however, negotiation can be considered during the planning stage, as in real life. In this paper, we present a novel approach to integrate argumentation-based negotiation planning into the general planning process of an autonomous agent. This integration allows the agent to take key decisions in advance. We evaluated this proposal in a multiagent scenario by comparing the performance of agents that plan the argumentation and agents that do not. These evaluations demonstrated that performance improves when the argumentation is planned, specially, when the negotiation alternatives increase.     

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.     

Handling threats,rewards, and explanatory arguments in a unified setting

Current logic‐based handling of arguments has mainly focused on explanation or justification‐oriented purposes in presence of inconsistency. So only one type of argument has been considered, and several argumentation frameworks have then been proposed for generating and evaluating such arguments. However, recent works on argumentation‐based negotiation have emphasized different other types of arguments such as threats, rewards, and appeals. The purpose of this article is to provide a logical setting that encompasses the classical argumentation‐based framework and handles the new types of arguments. More precisely, we give the logical definitions of these arguments and their weighting systems. These definitions take into account that negotiation dialogues involve not only agents' beliefs (of various strengths), but also their goals (having maybe different priorities), as well as the beliefs on the goals of other agents. In other words, from the different beliefs and goals bases maintained by agents, all the possible threats, rewards, explanations, and appeals that are associated with them can be generated. It may also happen that an intended threat, or reward, is not perceived as such by the addressee and thus misses its target because the addresser misrepresents the addressee's goals. The proposed approach accounts for that phenomenon. Finally, we show how to evaluate conflicting arguments of different types. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1195–1218, 2005.     

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。     

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.     

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.     

Continuous-Time Negotiation Mechanism for Software Agents

While there are several existing mechanisms and systems addressing the crucial and difficult issues of automated one-to-many negotiation, this paper develops a flexible one-to-many negotiation mechanism for software agents. Unlike the existing general one-to-many negotiation mechanism, in which an agent should wait until it has received proposals from all its trading partners before generating counterproposals, in the flexible one-to-many negotiation mechanism, an agent can make a proposal in a flexible way during negotiation, i.e., negotiation is conducted in continuous time. To decide when to make a proposal, two strategies based on fixed waiting time and a fixed waiting ratio is proposed. Results from a series of experiments suggest that, guided by the two strategies for deciding when to make a proposal, the flexible negotiation mechanism achieved more favorable trading outcomes as compared with the general one-to-many negotiation mechanism. To determine the amount of concession, negotiation agents are guided by four mathematical functions based on factors such as time, trading partners' strategies, negotiation situations of other threads, and competition. Experimental results show that agents guided by the four functions react to changing market situations by making prudent and appropriate rates of concession and achieve generally favorable negotiation outcomes