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。     

不一致信念的定量非修正方法满足AGM公设的讨论

常识推理的核心问题为信念修正问题,研究人员就此已经提出了许多方法,其中AGM理论是最有影响的一个。本文讨论并逐条证明了定量非修正方法满足AGM公设的情况,表明该方法是一种性质良好的信念修正方法。     

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.     

修正指数遗忘RLS算法及其在故障诊断上的应用

汽车电子系统在线故障诊断的算法执行效率、动态跟踪速度和稳态估计精度是检测突变故障参数档估计故障诊断中常用的递推最小二乘RLS算法存在的典型问题,提出了一种在线修正遗忘因子的方法.理论分析和仿真结果均表明,修正后的方法能有效解决一般递推算法的"数据饱和"问题,与通常的遗忘算法和滑动数据窗法相比,表现出了明显的优越性.为进一步的实车故障诊断提供了更加有效的理论根据.     

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.     

弱化矛盾信息的一种修正方法

信念修正中一个关键的问题就是对不一致信息的处理。文中提出一种新的策略，在对不一致信息的处理过程中，通过弱化矛盾信息而不是完全丢弃来维护知识库的一致性。这种方法遵循了最小改变原则，使信息丢失尽可能小从而保留了更多的有用信息。并讨论了其性质及满足AGM公设的情况。     

弱化矛盾信息的一种修正方法

信念修正中一个关键的问题就是对不一致信息的处理.文中提出一种新的策略,在对不一致信息的处理过程中,通过弱化矛盾信息而不是完全丢弃来维护知识库的一致性.这种方法遵循了最小改变原则,使信息丢失尽可能小从而保留了更多的有用信息.并讨论了其性质及满足AGM公设的情况.     

全收缩及其与广义收缩之间的关系

首先给出全信念收缩运算的一个反例，证明全收缩运算之附加假设与基本假设不协调，从而说明全收缩假设系统的不协调性，其次通过地全收缩运算假设系统的修改，消除了导致原系统不协调的直接因素，证明若一个全收缩函数满足修改后的各条假设，则该函数一定为最大选择收缩函数，并证明了在非平衡的情形下其假设系统仍不协调；最后给出了满足基本假设的广义收缩函数与全收缩函数之间的互可定义性，说明可以用广义收缩函数部分代替全收缩     

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.     

基于粗糙集理论的规则修正方法

基于粗糙集理论分别给出了确定性决策规则和可能性决策规则的获取与修正的理论和方法，给出一种利用粗糙集理论解决在增加样本数量情况下的动态规则获取方法，滚动轴承的故障诊断实例证明了该方法的有效性。     

Dynamic belief revision operators

The AGM approach to belief change is not geared to provide a decent account of iterated belief change. Darwiche and Pearl have sought to extend the AGM proposal in an interesting way to deal with this problem. We show that the original Darwiche-Pearl approach is, on the one hand excessively strong and, on the other rather limited in scope. The later Darwiche-Pearl approach, we argue, although it addresses the first problem, still remains rather permissive. We address both these issues by (1) assuming a dynamic revision operator that changes to a new revision operator after each instance of belief change, and (2) strengthening the Darwiche-Pearl proposal. Moreover, we provide constructions of this dynamic revision operator via entrenchment kinematics as well as a simple form of lexicographic revision, and prove representation results connecting these accounts.     

Operational and complete approaches to belief revision

Two operational approaches to belief revision are presented in this paper.The rules of R-calculus are modified in order to deduce all the maximal consistent subsets.Another set of given in order to deduce all the minimal inconsistent subsets.Then a procedure,which can generate all the maximal consistent subsets,is presented.They are complete approaches,since all the maximal consistent subsets can be deduced or generated.In this paper,only the case of propositional logic is considered.     

Iterated belief revision, revised

The AGM postulates for belief revision, augmented by the DP postulates for iterated belief revision, provide widely accepted criteria for the design of operators by which intelligent agents adapt their beliefs incrementally to new information. These postulates alone, however, are too permissive: They support operators by which all newly acquired information is canceled as soon as an agent learns a fact that contradicts some of its current beliefs. In this paper, we present a formal analysis of the deficiency of the standard postulates alone, and we show how to solve the problem by an additional postulate of independence. We give a representation theorem for this postulate and prove that it is compatible with AGM and DP.     

Abductive logics in a belief revision framework

Abduction was first introduced in the epistemological context of scientific discovery. It was more recently analyzed in artificial intelligence, especially with respect to diagnosis analysis or ordinary reasoning. These two fields share a common view of abduction as a general process of hypotheses formation. More precisely, abduction is conceived as a kind of reverse explanation where a hypothesis H can be abduced from events E if H is a good explanation of E. The paper surveys four known schemes for abduction that can be used in both fields. Its first contribution is a taxonomy of these schemes according to a common semantic framework based on belief revision. Its second contribution is to produce, for each non-trivial scheme, a representation theorem linking its semantic framework to a set of postulates. Its third contribution is to present semantic and axiomatic arguments in favor of one of these schemes, ordered abduction, which has never been vindicated in the literature.     

A Note on Partial Meet Package Contraction

It was shown that finite P-recovery holds for partial meet package contraction in Furhmann and Hansson (1994). However, it is not known if recovery holds for partial meet package contraction in the infinite case. In this paper, I show that recovery does not hold for partial meet package contraction in the infinite case.     

Default reasoning and belief revision: A syntax-independent approach

As an important variant of Reiter‘s default logic.Poole(1988) developed a nonmonotonic reasoning framework in the classical first-order language,Brewka and Nebel extended Poole‘s approach in order to enable a representation of priorities between defaults.In this paper a general framework for default reasoning is presented,which can be viewed as a generalization of the three approaches above.It is proved that the syntax-independent default reasoning in this framework is identical to the general belief revision operation introduced by Zhang et al.(1997).This esult provides a solution to the problem whether there is a correspondence between belief revision and default logic for the infinite case .As a by-product,an answer to the the question,raised by Mankinson and Gaerdenfors(1991),is also given about whether there is a counterpart contraciton in nonmonotonic logic.     

智能体行动推理中的信念修正

信念修正中的加强修正算法在保留低秩非矛盾信念时存在不足,流演算由于其表示公式的局限,无法较好的与信念修正结合来进行行动推理。针对以上不足,提出了依赖信念修正算法和Strategy-Axiom-Reasoning模型。该算法满足Ind假设并且较好的保留了条件信念和低秩非矛盾信念,SAR模型继承并改进了流演算的公理系统,用公式集表示信念集,较好的实现了与信念修正的结合。最后将依赖信念修正算法运用到SAR模型中,用实验证明了其可行性。     

A survey of multiple contractions

The AGM theory of belief contraction is extended tomultiple contraction, i.e. to contraction by a set of sentences rather than by a single sentence. There are two major variants: Inpackage contraction all the sentences must be removed from the belief set, whereas inchoice contraction it is sufficient that at least one of them is removed. Constructions of both types of multiple contraction are offered and axiomatically characterized. Neither package nor choice contraction can in general be reduced to contractions by single sentences; in the finite case choice contraction allows for reduction.