Cooperative-Competitive Healthcare Service Negotiation

Service negotiation is a complex activity, especially in complex domains such as healthcare. The provision of healthcare services typically involves the coordination of several professionals with different skills and locations. There is usually negotiation between healthcare service providers as different services have specific constraints, variables, and features (scheduling, waiting lists, availability of resources, etc.), which may conflict with each other. While automating the negotiation processes by using software can improve the effciency and quality of healthcare services, most of the existing negotiation automations are positional bargaining in nature, and are not suitable for complex scenarios in healthcare services. This paper proposes a cooperative-competitive negotiation model that enables negotiating parties to share their knowledge and work toward optimal solutions. In this model, patients and healthcare providers work together to develop a patient-centered treatment plan. We further automate the new negotiation model with software agents.     

Logic Programs with Ordered Disjunction

Logic programs with ordered disjunction (LPODs) contain a new connective which allows representing alternative, ranked options for problem solutions in the heads of rules: A × B intuitively means that if possible A , but if A is not possible, then at least B . The semantics of logic programs with ordered disjunction is based on a preference relation on answer sets. We show how LPODs can be implemented using answer set solvers for normal programs. The implementation is based on a generator, which produces candidate answer sets and a tester which checks whether a given candidate is maximally preferred and produces a better candidate if it is not. We also discuss the complexity of reasoning tasks based on LPODs and possible applications.     

A Real-Time Negotiation Model and A Multi-Agent Sensor Network Implementation

This paper describes a negotiation model that incorporates real-time issues for autonomous agents. This model consists of two important ideas: a real-time logical negotiation protocol and a case-based negotiation model. The protocol integrates a real-time Belief-Desire-Intention (BDI) model, a temporal logic model, and communicative acts for negotiation. This protocol explicitly defines the logical and temporal relationships of different knowledge states, facilitating real-time designs such as multi-threaded processing, state profiling and updating, and a set of real-time enabling functional predicates in our implementation. To further support the protocol, we use a case-based reasoning model for negotiation strategy selection. An agent learns from its past experience by deriving a negotiation strategy from the most similar and useful case to its current situation. Guided by the strategy, the agent negotiates with its partners using an argumentation-based negotiation protocol. The model is time and situation aware such that each agent changes its negotiation behavior according to the progress and status of the ongoing negotiation and its current agent profile. We apply the negotiation model to a resource allocation problem and obtain promising results.     

一种具有回溯机制的智能代理协商模型

近年来,智能代理技术被引入帮助人们处理互联网上的海量商务数据。然而在网上商务自动协商时,智能代理技术仍存在着一些问题。网上商务对话需要智能商务代理具备一套完善的交互模型以应付各种特殊情况。论文通过改进已有的交互模型使其具备协商回溯功能,以提高商务协商的成功率。此外,论文还将讨论如何提高该模型的效率。     

Automated Negotiation from Declarative Contract Descriptions

Our approach for automating the negotiation of business contracts proceeds in three broad steps. First, determine the structure of the negotiation process by applying general knowledge about auctions and domain–specific knowledge about the contract subject along with preferences from potential buyers and sellers. Second, translate the determined negotiation structure into an operational specification for an auction platform. Third, after the negotiation has completed, map the negotiation results to a final contract. We have implemented a prototype which supports these steps by employing a declarative specification (in courteous logic programs) of (1) high–level knowledge about alternative negotiation structures, (2) general–case rules about auction parameters, (3) rules to map the auction parameters to a specific auction platform, and (4) special–case rules for subject domains. We demonstrate the flexibility of this approach by automatically generating several alternative negotiation structures for the domain of travel shopping in a trading agent competition.     

多Agent多问题协商模型

在多agent环境中,协商是多agent系统能够成功运转的关键.根据参与协商agent的数目和协商问题的数目,多agent环境中的协商可以分为双边-单问题协商、双边-多问题协商、多边-单问题协商、多边-多问题协商.前3种协商是多边-多问题协商在不同维上的简化.利用协商-协商过程-协商线程的概念建立了一个多边-多问题协商模型MMN(multi-agent multi-issue negotiation).该模型通过提供一个灵活的协商协议支持多agent环境中的不同协商形式,并且支持agent在协商过程中的学习.     

基于代理的电子商务洽谈模式的研究和设计

商务洽谈是电子商务活动中十分关键的行为。该文从软件生命周期的角度阐述了电子商务洽谈的生命周期，并给出了相应的商务洽谈过程模型，最后给出了一个基于软件代理的商务洽谈的应用框架。     

基于协作度的分布式自动协商联盟形成机制

现有联盟形成的研究中大都没有考虑到不同Agent的协作资源和协作态度不同的异质性,而是假定所有Agent具有相同的协作资源和协作态度.为此提出一种基于协作度的分布式自动协商联盟形成机制(collaborative degree-based distributed automatic negotiation coalition formation mechanism, CDBDN),通过对处在网络拓扑结构中Agent的协作资源和协作态度的描述建立Agent协作度的概念.以分布式的应用环境为背景,建立分布式协商协议(distributed negotiation protocal, DNP)来实现分布式自动协商方式构建联盟.该机制建立了分布式协商协议和引入了Agent协作度,提出基于Agent协作度的协商策略.实验结果表明,该机制在联盟形成的效率、Agent协商效率和个体效用方面都表现出有较好的性能.     

一种基于Agent技术的安全能力自动协商机制

适应多安全域环境的安全能力自动协商机制包括了一种基于Agent的安全能力协商模型、适用于该模型的协商Agent和协商流程。该机制中,通过定义安全能力协商描述语言(SanDL)文档,用户可以方便准确地将协商要求和协商策略告知协商Agent,由协商Agent自动完成协商过程。在协商Agent中通过定义通信适配器和一套基本协商原语,使协商Agent的通信接口更加安全、灵活、易于扩展。     

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。     

Consistency Property of Finite FC-Normal Logic Programs

Marek＇s forward-chaining construction is one of the important techniques for investigating the non-monotonic reasoning. By introduction of consistency property over a logic program, they proposed a class of logic programs, FC-normal programs, each of which has at least one stable model. However, it is not clear how to choose one appropriate consistency property for deciding whether or not a logic program is FC-normal. In this paper, we firstly discover that, for any finite logic programⅡ, there exists the least consistency property LCon（Ⅱ） overⅡ, which just depends onⅡitself, such that, Ⅱ is FC-normal if and only ifⅡ is FC-normal with respect to （w.r.t.） LCon（Ⅱ）. Actually, in order to determine the FC-normality of a logic program, it is sufficient to check the monotonic closed sets in LCon（Ⅱ） for all non-monotonic rules, that is LFC（Ⅱ）. Secondly, we present an algorithm for computing LFC（Ⅱ）. Finally, we reveal that the brave reasoning task and cautious reasoning task for FC-normal logic programs are of the same difficulty as that of normal logic programs.     

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。     

一个基于多阶段的多Agent多问题协商框架

多问题协商是电子交易中的关键问题.多Agent技术的不断成熟为这个问题的解决提供了有效的途径.提出了一个以理性Agent为基础的基于多阶段的多问题协商框架,该框架在时间约束下适用于信息不完全的场景,它描述了多问题的价格协商.为了降低多问题协商的复杂性,它将多问题协商分解为多阶段协商,每个阶段的大小(问题数)相同.阶段数和顺序在协商前确定,每个阶段中的问题顺序在协商中确定.在阶段大小相同的情况下,对给定协商问题的分解,框架能给出优化协商议程(agenda).尤其是框架能为参与协商的Agent建立学习系统(LS),以增强Agent的学习能力.最后基于这个框架实现了一个原型系统,原型系统证明这个框架是有效的.     

带函数析取逻辑程序的无基集及其应用

基于回答集（也称稳定模型）语义的带函数析取逻辑程序是一种重要的知识表示和推理方法。由于判定一个析取逻辑程序是否有回答集是困难的（Σ2完全的）,目前还没有有效的方法来计算带函数析取逻辑程序的回答集,主要原因之一是检查一个集合是否是回答集是coNP完全的。提出了带函数析取逻辑程序无基集（unfounded sets）的概念,发现了空无基集（unfounded-free sets）与稳定模型之间的一一对应关系,在此基础上,证明了一个逻辑程序的模型是该程序的稳定模型当且仅当它们对应的一个命题公式是不可满足的,从而在理论上为计算带函数析取逻辑程序的回答集提供了一种有效的途径。     

结合协商机制的Web服务属性访问控制模型

针对实现有效Web服务访问控制的问题,提出一种结合协商机制的Web服务属性访问控制模型。该模型基于安全断言标记语言和可扩展访问控制标识语言,利用主体属性和上下文属性的组合限制条件,提供细粒度的访问控制。通过加入协商机制,服务请求者可以与服务提供者相互沟通,在访问请求中动态地调整参数信息以获得访问授权。     

一种多Agent多议题的协商模型

协商是多Agent系统实现协作、协调和冲突消解的关键技术。本文分析了协商问题的实质和协商过程,提出了一种支持多轮协商的多Agent多议题协商模型。模型中引入了Agent类型的概念,在信息不完全的条件下,协商Agent通过推测协商对手的类型来指导自身的提议策略和协商战术,使提议更具针对性,避免了盲目性,从而节约了协商时间,提高了协
商质量。     

A Negotiation Scheme for Access Rights Establishment in Autonomic Communication

Autonomic computing and communication has become a new paradigm for dynamic service integration and resource sharing in today's ambient networks. Devices and systems need to dynamically collaborate and federate with little known or even unknown parties in order to perform everyday tasks. Those devices and systems act as independent nodes that autonomously manage and enforce their own security policies. Thus in autonomic pervasive communications clients may not know a priori what access rights they need in order to execute a service nor service providers know a priori what credentials and privacy requirements clients have so that they can take appropriate access decisions. To solve this problem we propose a negotiation scheme that protects security and privacy interests with respect to information disclosure while still providing effective access control to services. The scheme proposes a negotiation protocol that allows entities in a network to mutually establish sufficient access rights needed to grant a service.

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层次式无线传感器网络中的协商机制研究

无线传感器网络由具有计算、通信和感知能力的小型自治实体组成,层次式体系结构作为一种有效的组织方式能够降低网络能量消耗,延长网络的生存周期。从多Agent系统的角度研究构成这种层次式无线传感器网络的相关协议和算法的意图协商过程,建立其协商模型,为层次式无线传感器网络路由协议或分簇算法提供一种评价和分析方法。基于该模型的实际网络协议分析表明,具有相同协商机制而意图信息量不同的层次式无线传感器网络在性能方面存在一定的差异,良好的意图和基于良好意图的完善协商机制可以有效提高网络的性能。     

基于不妥协度的Bayes学习协商机制

如何高效地使用Agent学习机制进行在线协商,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。,为了当协商进入僵持状态时参与协商的买卖双方能确定是否进行妥协,从而使协商继续进行下去,本文在限时条件下的多议题协商中和贝叶斯学习的基础上提出了基于不妥协度的协商策略。实验表明在协商过程中进行学习可以提高对方私有信息的预测精确度,缩短了协商时间,提高了协商效率。     

A Complex Networked Method of Sorting Negotiation Demand Based on Answer Set Programs

With the development of big data science, handling intensive knowledge in the complex network becomes more and more important. Knowledge representation of multi-agent negotiation in the complex network plays an important role in big data science. As a modern approach to declarative programming, answer set programming is widely applied in representing the multi-agent negotiation knowledge in recent years. But almost all the relevant negotiation models are based on complete rational agents, which make the negotiation process complex and low efficient. Sorting negotiation demands is the most key step in creating an efficient negotiation model to improve the negotiation ability of agents. Traditional sorting method is not suitable for the negotiation in the complex network. In this paper, we propose a complex networked negotiation, which can show the relationships among demands, and then a sorting method of negotiation demands is proposed based on demand relationships. What’s more, we use the betweenness of literals and the boundary co-efficient of rules to evaluate the importance of demands and rules.