多Agent技术研究

本文就近年来有关多Ａｇｅｎｔ系统的研究作一个介绍。包括：Ａｇｅｎｔ系统,多Ａｇｅｎｔ的主要研究内容及应用等。     

一种劝说式多Agent多议题协商方法

多Agent系统中的协商问题往往由许多议题组成,导致问题空间十分庞大.传统的协商方法通过对问题空间进行穷尽搜索来找到最优解,并不适合多议题协商.而且,传统的方法不考虑协商偏好变化的情况,使得Agent在不完全及不正确环境下找到的最优解并不合理.提出一种劝说式多Agent多议题协商方法.借助信念修正这一有效的推理工具,协商Agent能够在协商过程中接受协商对手的劝说,考虑对手对协商议题的偏好,并根据一种基于辩论的信念修正方法调整自身的偏好.这样就能够使协商Agent对变化的协商环境具备适应性,从而提高协商的效率及正确率,快速准确地达成协议.     

多Agent技术研究与应用

1 Introduction In resent years, with the development of computer net- work and communication technology, research on Agent and Multi- Agent technology has been a hotspot in distributed AI area. Multi- Agent technology is mainly about a group of autonomous agents completing complex tasks with the intel- ligent actions such as communication, cooperation, compe- tition and negotiation under distributed, open and dynamic environments. As Agent has intelligence and can adapt to the open and dyna…     

多Agent系统的研究

自agent术语引起国内学者重视以来,人们一直试图寻找一个贴切的汉语名词,“代理人”“智能体洲结点”等概念应运而生。但这些概念都不能令人满意,因为无论是“智能体”、“代理人”,还是“结点”,都仅部分地反映了agent的特征,而未完全地反映其本质属性,故本文仍使用agent的英文形式。     

多Agent系统与分布式专家系统研究

1 引言专家系统是人工智能中最活跃的一个分支,已在众多领域取得了巨大的社会效益和经济效益。但是,传统专家系统方法还存在着许多局限性,尤其是随着计算机网络和计算机应用的发展,专家系统又面临着两个严重问题。一是如何改变过去的单机模式,更好地适应网络环境;二是如何打破以往的封闭模式,更好地实现大型复杂问题的分布式处理。     

多Agent系统的理论、技术及其应用

一、引言分布式人工智能(DAD主要研究在逻辑上或物理上分散的智能系统如何并行地,相互协作地进行问题求解。DAI是在传统AI的基础上发展起来的,分为分布式问题求解(DPS)和多Agent系统(MAS)两个方向。多Agent系统被看成是人工智能的实验平台,成为AI的研究热点之一。     

一个多Agent系统模型

一个Ａｇｅｎｔ的知识和问题求解能力是有限的,为更好的服务于用户,必须与其它Ａｇｅｎｔ全作,Ａｇｅｔｎｔ寻找合作伙伴是问题求解的关键。本文结构Ｎ．Ｒ．Ｊｅｎｎｉｎｇｓ等人的工作给出了一种系统模型,任务减少了通开锁,自适应单元增强了Ａｅｇｎｔ对外界变化的自适应能力,Ａｇｅｎｔ个性倾向性使模型更合理,排除了冲突因素,提高了２求解的效率。     

多Agent系统体系结构

1.引言基于Agent的系统是目前计算机科学领域中一个非常重要和活跃的研究方向,代表了一种新的方式和途径,可用于概念化、分析、描述和实现复杂而庞大的系统,因而体现了一种新的软件开发范型。这种范型在开发诸如internet这样的分布、开放环境中的软件系统时尤具吸引力。     

多Agent系统中实时消息传递机制

1 引言 Agent是具有信念、能力、决策和承诺等精神状态的实体,它的提出是以计算的社会学模型为背景,通过多个简单Agent协同工作,可以完成比较复杂的任务。分布式人工智能(DAI)的主要目标就是解决如何组织多Agent系统中的Agent,使它们能够协同工作以完成复杂问题求解。多Agent系统的关键问题是Agent间的协同,而根据计算社会学模型的观点,消息     

Belief Revision: A Critique

We examine carefully the rationale underlying the approaches to belief change taken in the literature, and highlight what we view as methodological problems. We argue that to study belief change carefully, we must be quite explicit about the “ontology” or scenario underlying the belief change process. This is something that has been missing in previous work, with its focus on postulates. Our analysis shows that we must pay particular attention to two issues that have often been taken for granted: the first is how we model the agent's epistemic state. (Do we use a set of beliefs, or a richer structure, such as an ordering on worlds? And if we use a set of beliefs, in what language are these beliefs are expressed?) We show that even postulates that have been called “beyond controversy” are unreasonable when the agent's beliefs include beliefs about her own epistemic state as well as the external world. The second is the status of observations. (Are observations known to be true, or just believed? In the latter case, how firm is the belief?) Issues regarding the status of observations arise particularly when we consider iterated belief revision, and we must confront the possibility of revising by φ and then by ¬ φ.     

一种广义信念修正的计算方法

AGM理论中新知识为单个语句,而实际上新知识通常是以多语句甚至无穷语句的形式出现．广义信念修正从理论上扩充了AGM系统,但在具体实现上还缺乏有效的算法．通过构造优序划分的方法解决信念间的关系问题,采用新知识的信度与冲突知识相关的方法解决新知识的信度问题．给出了一种基于链表的计算方法,从而解决了广义信念修正理论的信念库维护操作问题．     

信念修正的完全和可操作的方法

给出了命题逻辑上信念修正的两种可操作的完全方法.首先对R-演算的规则进行了修改,使得对任何一个极大协调的子集都通过这组规则得到.然后,给出了求得所有的极小不协调子集的一组规则.最后,给出一个过程,该过程能求得所有的极大协调子集.因为这两种方法都能求得所有的极大协调子集,所以把它们称为完全的.     

一种基于可信度的迭代信念修正方法

信念修正主要解决在接收到新信息时,如何对原有知识库进行操作的问题.经典的迭代信念修正主要关注信念修正的一致性,并未考虑多agent系统中信息具有不可靠性,以及信念修正过程对修正结果的影响.基于可信度的迭代信念修正方法,通过证据理论以及信度函数方法估计信息的可信度,并由此确定最优的最大协调子集作为信念修正的结果.基于可信度的迭代信念修正算子具有历史依赖性,即修正结果不仅与当前的信念集和接收到的新信息有关,也与信念集中曾经接收到的信息相关.     

Distributed Belief Revision

In this paper, a distributed approach to belief revision is presented. It is conceived as a collective activity of a group of interacting agents, in which each component contributes with its own local beliefs. The integration of the different opinions is performed not by an external supervisor, but by the entire group through an election mechanism. Each agent exchanges information with the other components and uses a local belief revision mechanism to maintain its cognitive state consistent. We propose a model for local belief revision/integration based on what we called: Principle of Recoverability. Computationally, our way to belief revision consists of three steps acting on the symbolic part of the information, so as to deal with consistency and derivation, and two other steps working with the numerical weight of the information, so as to deal with uncertainty. In order to evaluate and compare the characteristics and performance of the centralized and of the distributed approaches, we made five different experiments simulating a simple society in which each agent is characterized by a degree of competence, communicates with some others, and revise its cognitive state. The results of these experiments are presented in the paper.     

Conciliation through Iterated Belief Merging

Two families of conciliation processes for intelligent agentsbased on an iterated merge-then-revise change function for beliefprofiles are introduced and studied. The processes from thefirst family are sceptical in the sense that at any revisionstep, each agent considers that her current beliefs are moreimportant than the current beliefs of the group, while the processesfrom the other family are credulous. Some key features of suchconciliation processes are pointed out for several merging operators;especially, the stationarity issue, the existence of consensusand the properties of the induced iterated merging operatorsare investigated.     

Chemical Discovery as Belief Revision

In this paper we describe STAHLp, a system that constructs componential models of chemical substances. STAHLp is a descendant of Zytkow and Simon's (1986) STAHL system, and both use chemical reactions and known componential models in order to construct new chemical models. However, STAHLp employs a more unified and effective strategy for recovering from erroneous inferences, based partly on de Kleer's (1984) assumption-based method of belief revision. This involves recording the underlying source beliefs or premises which lead to each inferred reaction or model. Where Zytkow and Simon's system required multiple methods for detecting errors and recovering from them, STAHLp uses a more powerful representation and additional rules which allow a unified method for error detection and recovery. When given the same initial data, the new system constructs the same historically correct models as STAHL, but it has other capabilities as well. In particular, STAHLp can modify data it has been given if this is necessary to achieve consistent models, and then proceed to construct new models based on the revised data.     

有缺指派下的信念修正逻辑

经典的AGM信念修正理论和以D-P假设为代表的迭代信念修正理论都是以完全指派为可能世界而进行的理论研究.把这些研究推广到有缺指派的领域中与完全指派为每个原子命题符号都指派真假值不同,有缺指派是一个三值指派,它可以为每个原子命题符号指派真、假和不确定三值之一.以有缺指派为可能世界,对D-P系统进行了推广,证明了相应的表示定理.