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.     

基于闭环DNA的指派问题算法

给出了闭环DNA计算模型及其生化实验。用闭环DNA计算模型设计出了指派问题的DNA算法。首先对决策变量进行二维DNA编码来存放决策变量和效益值,然后通过有目的的终止技术和删除实验得到指派问题的全部可行解,最后通过电泳实验和检测实验获得最优指派问题的最优解。举例说明了算法的可行性。最后,为减少DNA编码数量和缩短DNA编码的码长,讨论了算法的两种改进方法。     

VB环境下的模拟退火算法求指派问题

模拟退火算法是一种随机搜索算法,可应用于许多前提信息很少的问题,能渐进地收敛于全局最优解。指派问题是组合优化问题中的一种,可用模拟退火算法来解此问题。模拟退火算法解决指派问题时,需要考虑实现此算法的技术问题,例如解的形式,初始温度的计算,邻域的生成方式,解的接受和舍弃,内外循环的中止条件等。在VB编程环境下,实现了该算法的求解过程。实例仿真表明了该方法能够以一定的概率跳出局部最优而实现全局寻优。     

一种基于信念修正思想的SVR 增量学习算法

针对实际应用中数据的批量到达,以及系统的存储压力和学习效率低等问题,提出一种基于信念修正思想的SVR增量学习算法。首先从历史样本信息中提取信念集,根据信念集和新增数据的特点选择相应的信念集建立支持向量回归模型并进行预测;然后对信念集进行修正,调整当前认知状态,使该算法对在线和批处理增量学习都有很好的适应性。在标准数据集上的测试验证了算法的良好性能;在某机场噪声实测数据上的对比实验也表明,该算法的性能明显优于传统学习算法和一般增量学习算法。     

非方阵指派问题的求解

本文将2 类方阵指派问题——极大极小和总体极小指派问题——的矩阵作业解法推广到非方阵情形, 即求解任务与人员数目不等的指派问题,且维持矩阵作业法的效率．假定m > n,则按本文行优先选取算法求解 m￡n 非方阵指派问题的最大逻辑运算量为O(mn2),其效率通常与执行一轮覆盖的矩阵作业法相当．     

基于主Agent信念修正的推测计算及其资源协商

基于假设推理(abduction-based)的推测计算(speculative computation)是在资源信息不能及时到达时,利用缺省假设进行计算的过程.在计算过程中,如果应答和信念不一致,则主Agent将修正它的信念.为了实现目标,在有限时间内使推测计算的结果更精确,主Agent要通过协商获得尽可能多的实际信息,协商是降低决策风险的主要途径.在介绍假设推理和推测计算的基本原理的基础上,提出了基于时间约束的推测计算扩展框架、基于时间约束的进一步协商框架和基于信念修正的协商算法,并将进一步协商框架和协商算法嵌入到推测计算的过程中,在协商过程中赋予主Agent更强的信念修正能力.最后,在货物运输领域的实验中,证实了基于信念修正的推测计算的有效性.     

工作流资源指派技术的研究与实现

资源指派是工作流管理系统的一项重要功能,它在运行期将资源模型和过程模型联系起来。对资源指派规则的表达机制进行了分析,并在此基础上研究开发了一个基于指派规则和资源管理服务的资源指派系统,它具有良好的可扩充性,并支持资源模型的共享。     

求解指派问题的一次性分配算法

匈牙利算法是求解指派问题的全局最优求解算法,但是经典的匈牙利算法存在着实现难、处理速度慢等不足。提出了一种改进匈牙利算法,对匈牙利算法寻找独立零的次序进行了改进,从而避免了匈牙利算法通常需要进行多次试分配的不足。针对改进前后两种算法的复杂度、运算时间、精确度等进行了对比分析,结果表明,改进的算法是一种高精度的近似最优求解算法;与匈牙利算法相比,改进的算法易于编程实现,且时间花费较低,是一种适用于工程实时应用的有效求解算法。     

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.     

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.     

多Agent信念修正研究

Belief Revision is a theory that studies how to integrate new information into original belief set.Classical BR theory uses AGM frame,but it only resolves problems in single agent BR system.Multi-agent BR faces problems such as the collision of many information sources and how to maximize the logic consistence of multi-agent system.On the basis of game theory model,we form profit matrix under different BR strategies in muhi-agent system and try to get the best strategy that satisfies logic consistence of the system through negotiation.     

Iterated Belief Change

Most existing formalizations treat belief change as a single-step process, and ignore several problems that become important when a theory, or belief state, is revised over several steps. This paper identifies these problems, and argues for the need to retain all of the multiple possible outcomes of a belief change step, and for a framework in which the effects of a belief change step persist as long as is consistently possible. To demonstrate that such a formalization is indeed possible, we develop a framework, which uses the language of PJ-default logic (Delgrande and Jackson 1991) to represent a belief state, and which enables the effects of a belief change step to persist by propagating belief constraints . Belief change in this framework maps one belief state to another, where each belief state is a collection of theories given by the set of extensions of the PJ-default theory representing that belief state. Belief constraints do not need to be separately recorded; they are encoded as clearly identifiable components of a PJ-default theory. The framework meets the requirements for iterated belief change that we identify and satisfies most of the AGM postulates (Alchourrón, Gärdenfors, and Makinson 1985) as well.     

一种基于遗忘理论的信念收缩方法

针对在经典信念理论框架下,信念收缩后可能出现信息损失的问题,本文提出了一种利用遗忘理论来构建收缩算子的信念收缩方法。本文先通过理论证明来说明该收缩算子能够满足AGM理论中信念收缩的假定,然后用实例说明,与命题逻辑表示的信念遗忘收缩相比,一阶谓词逻辑表示的信念遗忘收缩保留了更多的原有信息,避免了不必要的信息损失,遵循最小修改原则。     

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.     

有限信念集上修正的一种方法

讨论了信念集是有限子句集时的信念修正方法.首先给出了一阶逻辑上求所有极小不协调子集的一个过程,证明了该过程的正确性;然后讨论了由有极小不协调的子集来实现信念修正的方法,介绍所开发的信念修正的原型系统;最后与相关工作进行了比较.