The degree of belief in a fuzzy event

Epistemic probabilities are better described by belief functions. Their definition is extended in order to apply them to fuzzy events. The relations between belief functions, possibilities, and admissibilities are explained, and these last notions are extended.     

Temporal Reasoning and Bayesian Networks

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporal (or dynamic) probabilities to represent facts, events, and the effects of events. The architecture of a belief network may change with time to indicate a different causal context. Probability variations with time capture temporal properties such as persistence and causation. They also capture event interaction, and when the interaction between events follows known models such as the competing risks model, the additive model, or the dominating event model, the net effect of many interacting events on the temporal probabilities can be calculated efficiently. This representation of reasoning also exploits the notion of temporal degeneration of relevance due to information obsolescence to improve the efficiency.     

WSN中事件驱动与信任分配加权的层次数据融合

针对WSN监测突发事件及节点能量受限问题,提出了一种基于事件驱动与信任度分配加权的层次数据融合算法(EDBA).通过设置监测阈值,仅在事件发生时,相关部分节点才进入高频次数据采集和传输的兴奋状态,其他情况节点处于低频次采集(或传输)的抑制(或活动)状态,采用证据理论及信任分配函数对网络监测数据进行多层次融合,以减少监测数据传输量.仿真表明,在通常情况下,EDBA算法能耗分别是EBPDF、LEACH的50%和21%,有效地降低了网络能耗.     

基于价值累加理论的网络热点事件舆情研判及系统实现

基于价值累加理论分析网络热点事件的演变过程,确定触发因素的出现、共同信念的形成、行动动员的完成是事件演变的三个关键环节,设计监测模型,对事件舆情中的敏感因素、情感的形成及扩散进行分析判断。在此基础上,构造网络舆情热点信息智能监测平台系统(NPOIMS),以我国西部地区的x市为实例对象,架构舆情监测系统,监测与x市有关的各类舆情信息,提炼热点词语,进行舆情研判,提供分析报告,为相关部门提供舆情引导和事件应对的信息参考和决策支持。     

基于深度信念网络的维吾尔语事件伴随关系识别

维吾尔语事件伴随关系是维吾尔语语言中常见且重要的关系之一。结合对维吾尔语语言特点的研究,该文提出一种基于深度信念网络的维吾尔语事件伴随关系识别方法,根据维吾尔语语言特性和事件伴随关系的特点,抽取12项基于事件结构信息的特征;同时充分利用事件对所对应的两个触发词之间的语义信息,引入Word Embedding计算两个触发词之间的语义相似度。而后融合两类特征作为DBN模型的输入进行训练,最后将训练结果作为softmax分类器的输入实现维吾尔语事件伴随关系的识别。该方法用于维吾尔语事件伴随关系的识别准确率P为81.89%、召回率R为84.32%、F₁值为82.48%。实验结果表明,与支持向量机方法相比,基于DBN模型的方法取得更好的识别效果。     

Evaluation method based on fuzzy relations between Dempster–Shafer belief structure

Many relations in the real world can be described by mathematical language. Fuzzy set theory can transform human language into mathematical language and use membership degree function to describe relations between events. Dempster–Shafer evidence theory provides basic probability assignment (BPA), which can describe the occurrence rate of attributes in basic events. Based on the known membership degree function and BPA distribution, a new evaluation method is proposed in this paper to analyze decision making. Given the relations among relevant events, which are expressed by BPA distribution and membership degree function, the relations among basic events and top event can be obtained. The Dempster's combination rule and pignistic probability transformation are used to transform BPA distribution into probability distribution. The belief measure is applied to deal with these fuzzy relations. Some numerical examples are given in this paper to illustrate the proposed evaluation methodology.     

Interventions and belief change in possibilistic graphical models

Causality and belief change play an important role in many applications. This paper focuses on the main issues of causality and interventions in possibilistic graphical models. We show that interventions, which are very useful for representing causal relations between events, can be naturally viewed as a belief change process. In particular, interventions can be handled using a possibilistic counterpart of Jeffrey's rule of conditioning under uncertain inputs. This paper also addresses new issues that are arisen in the revision of graphical models when handling interventions. We first argue that the order in which observations and interventions are introduced is very important. Then we show that in order to correctly handle sequences of observations and interventions, one needs to change the structure of possibilistic networks. Lastly, an efficient procedure for revising possibilistic causal trees is provided.     

A SIMPLE DECISION RULE ON FUZZY EVENTS

Decision making in real problems is done in a fuzzy environment. Thus, Fuzzy-Bayes decision rules have been proposed to cope with a fuzzy state of nature. These decision rules are based on the probability of fuzzy events, or the possibility measure of fuzzy events. Furthermore, a decision rule based on fuzzy utility functions and the possibility distribution of fuzzy events are constructed. However, in these decision rules the fuzziness of the fuzzy expected utility is very big, because these decision rules are based on the extension principle for calculation of the fuzzy expected utility. In this article, avoiding the large fuzziness of the expected utility, we proposed a simple decision rule based on the representation interval of the possibility distributions of fuzzy events and the representation value of the fuzzy utility function. Further, we discuss the application of this simple decision rule to the decision problems, in which the decision maker obtains the one-peak symmetric possibility distribution of a state of nature and the one-peak symmetric membership functions of fuzzy events on a state of nature, by his or her knowledge and his or her belief.     

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．     

Representing partial ignorance

This paper advocates the use of nonpurely probabilistic approaches to higher-order uncertainty. One of the major arguments of Bayesian probability proponents is that representing uncertainty is always decision-driven and as a consequence, uncertainty should be represented by probability. Here we argue that representing partial ignorance is not always decision-driven. Other reasoning tasks such as belief revision for instance are more naturally carried out at the purely cognitive level. Conceiving knowledge representation and decision-making as separate concerns opens the way to nonpurely probabilistic representations of incomplete knowledge. It is pointed out that within a numerical framework, two numbers are needed to account for partial ignorance about events, because on top of truth and falsity, the state of total ignorance must be encoded independently of the number of underlying alternatives. The paper also points out that it is consistent to accept a Bayesian view of decision-making and a non-Bayesian view of knowledge representation because it is possible to map nonprobabilistic degrees of belief to betting probabilities when needed. Conditioning rules in non-Bayesian settings are reviewed, and the difference between focusing on a reference class and revising due to the arrival of new information is pointed out. A comparison of Bayesian and non-Bayesian revision modes is discussed on a classical example     

Corruptible social agents

Agent‐based virtual simulations of social systems susceptible to corruption (e.g., police agencies) require agents capable of exhibiting corruptible behaviors to achieve realistic simulations and enable the analysis of corruption as a social problem. This paper proposes a formal belief‐desire‐intention framework supported by the functional event calculus and fuzzy logic for modeling corruption based on the integrity level of social agents and the influence of corrupters on them. Corruptible social agents are endowed with beliefs, desires, intentions, and corrupt‐prone plans to achieve their desires. This paper also proposes a fuzzy logic system to define the level of impact of corruption‐related events on the degree of belief in the truth of anti‐corruption factors (e.g., the integrity of the leader of an organization). Moreover, an agent‐based model of corruption supported by the proposed belief‐desire‐intention framework and the fuzzy logic system was devised and implemented. Results obtained from agent‐based simulations are consistent with actual macro‐level patterns of corruption reported in the literature. The simulation results show that (i) the bribery rate increases as more external entities attempt to bribe agents and (ii) the more anti‐corruption factors agents believe to be true, the less prone to perpetrate acts of corruption. Copyright © 2014 John Wiley & Sons, Ltd.     

基于证据推理和VIKOR的二维语言多准则群决策方法

针对专家给出二维语言评价信息的多准则群决策问题,提出基于证据推理和VIKOR的决策方法。首先, 从专家的心理认知和二维语言评价信息的语义出发,设定函数将二维语言信息映射为信度结构;接着基于所提出的广义信度结构,及证据的Pignistic概率距离,定义广义信度结构的距离;最后将专家给出的二维语言决策矩阵转化为信度决策矩阵,用证据推理算子集结为综合信度决策矩阵,并利用VIKOR方法对其求解,获取方案排序。实例分析表明了所提出方法的有效性和实用性。     

Entailment for measure based belief structures

We discuss the Dempster–Shafer belief structure on finite universes and note its use for modeling variables that have both probabilistic uncertainty as well as imprecision. We note for these structures the probability that the variable lies in a subset cannot be precisely known but only be known to an interval value. We discuss methods for deducing this uncertainty interval. We next discuss the issue of entailment of belief structures, inferring the validity of additional belief model of a variable from an already established belief model of the variable. We next discuss a more general belief structure were the underling uncertainty rather tha0n being based on a probability distribution is based on a general measure type of uncertainty. We then extend the concept of entailment to the case where the belief structures are these more general measure based belief structures. In order to accomplish this we must extend the idea of containment from classic Dempster–Shafer belief structures to measure based belief structures.     

基于可能回答集程序的多Agent信念协调

多Agent系统(Multi-Agent System,MAS)是人工智能领域的一个非常活跃的研究方向。在多Agent系统中,由于Agent之间信念的差异,会不可避免地造成行动冲突。Sakama等提出的严格协调方法只适用于各Agent之间有共同信念的情境,当不存在共同信念时,此协调方法无解。针对该问题,文中提出了一种基于可能回答集程序(Possibilistic Answer Set Programming,PASP)的信念协调方法。首先,针对各Agent的不同信念集,基于加权定量的方法计算PASP的回答集相对Agent信念的满足度,以此来弱化某些信念,并且引入缺省决策理论推理得到Agent信念协调的一致解。然后,根据一致解建立一致的协调程序,将其作为Agent共同认同的背景知识库。最后,以dlv求解器为基础实现了多Agent信念协调算法,使Agent之间可以自主完成信念协调。文中以旅游推荐系统为例,说明该算法能够打破严格协调方法的局限,有效解决各Agent之间无共同信念时的协调问题。     

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。     

Analyzing the monotonicity of belief interval based uncertainty measures in belief function theory

Measuring the uncertainty of pieces of evidence is an open issue in belief function theory. A rational uncertainty measure for belief functions should meet some desirable properties, where monotonicity is a very important property. Recently, measuring the total uncertainty of a belief function based on its associated belief intervals becomes a new research idea and has attracted increasing interest. Several belief interval based uncertainty measures have been proposed for belief functions. In this paper, we summarize the properties of these uncertainty measures and especially investigate whether the monotonicity is satisfied by the measures. This study provide a comprehensive comparison to these belief interval based uncertainty measures and is very useful for choosing the appropriate uncertainty measure in the practical applications.     

基于深度置信网络的4-CBA软测量建模

PTA工业生产过程中4-CBA的含量是评价其产品质量的重要依据。将深度置信网络和已有的浅层算法相结合,提出基于深度置信网络的4-CBA软测量模型。深度置信网络是一种典型的深度学习算法,该算法在特征学习方面优势显著。根据实验结果,基于深度置信网络的软测量模型能够很好地估计4-CBA含量,和单纯的BP神经网络模型相比,基于深度置信网络的模型预测精度更高。     

Iterated belief change in the situation calculus

John McCarthy's situation calculus has left an enduring mark on artificial intelligence research. This simple yet elegant formalism for modelling and reasoning about dynamic systems is still in common use more than forty years since it was first proposed. The ability to reason about action and change has long been considered a necessary component for any intelligent system. The situation calculus and its numerous extensions as well as the many competing proposals that it has inspired deal with this problem to some extent. In this paper, we offer a new approach to belief change associated with performing actions that addresses some of the shortcomings of these approaches. In particular, our approach is based on a well-developed theory of action in the situation calculus extended to deal with belief. Moreover, by augmenting this approach with a notion of plausibility over situations, our account handles nested belief, belief introspection, mistaken belief, and handles belief revision and belief update together with iterated belief change.