Formalizing Default Reasoning

Fuzzy set systems can be used to solve the problem with uncertain knowledge,and default logic can be used to solve the problem with incomplete knowledge,in some sense.In this paper,based on interval-valued fuzzy sets we introduce a method of inference which combines approximate reasoning an default ogic,and give the procedure of transforming monotonic reasoning into default reasoning.     

模糊描述逻辑L-ALCN

为了使描述逻辑能够处理更一般化的模糊信息,Straccia给出了基于完备格的L-ALC描述逻辑系统．在该方法的基础上,提出了带数量约束算子的L-ALCN系统,给出了望ALCN的语法,并详细给出了概念（≥n R）和（≤n R）的语义．经典的描述逻辑系统中引入了数量约束算子后,角色尺就出现了多个后继．当系统的真子集扩充到完备格时,角色R的后继和断言的真值同时出现了多个．为了保证推理算法的合理性且得到可行的计算复杂度,引入了一个特殊的集合DL（c）,并且利用集合DL（c）扩展了完备格上的两条运算性质．在这些工作的基础上,深入研究了系统的推理算法,并证明了算法的终止性、可靠性与完全性．相对于L-ALC,系统L-ALCN具有更强的表达能力,并且L-ALCN的计算复杂度是Pspace完全的．     

A framework for reasoning with soft information

In order to provide for the representation and manipulation of human sourced soft information we turn to the fuzzy set based theory of approximate reasoning. We describe how approximate reasoning provides a framework for representing and manipulating a wide body linguistically expressed information. We then suggest a number of extensions of the theory to enhance its representational capacity. One such extension focuses on the ability to model imprecise variables as well as imprecise values for the variable. We consider the representation of possible qualified propositions. We look at the issue of deduction in the face of conflict in our knowledge base and suggest an approach compatible with human behavior.     

基于DFL的缺省假设推理研究

日常生活中人们可以在信息不完全的情况下进行推理并得出较好的推理结论,而且在推理过程中,很多对象都是具有动态模糊性(DF Character)。因此文中针对研究对象以及它们之间的动态模糊性,提出了基于动态模糊逻辑(DFL)的缺省假设推理,并给出了缺省假设推理的框架描述、动态模糊(DF)知识的表示以及推理算法等。     

Approaches for the production and evaluation of fuzzy land cover classifications from remotely-sensed data

Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional ‘hard’ classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for ‘hard’ classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the ‘hard’ classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.     

基于Multi-head Attention和Bi-LSTM的实体关系分类

关系分类是自然语言处理领域的一项重要任务,能够为知识图谱的构建、问答系统和信息检索等提供技术支持.与传统关系分类方法相比较,基于神经网络和注意力机制的关系分类模型在各种关系分类任务中都获得了更出色的表现.以往的模型大多采用单层注意力机制,特征表达相对单一.因此本文在已有研究基础上,引入多头注意力机制（Multi-head attention）,旨在让模型从不同表示空间上获取关于句子更多层面的信息,提高模型的特征表达能力.同时在现有的词向量和位置向量作为网络输入的基础上,进一步引入依存句法特征和相对核心谓词依赖特征,其中依存句法特征包括当前词的依存关系值和所依赖的父节点位置,从而使模型进一步获取更多的文本句法信息.在SemEval-2010任务8数据集上的实验结果证明,该方法相较之前的深度学习模型,性能有进一步提高.     

Neural networks that learn from fuzzy if-then rules

An architecture for neural networks that can handle fuzzy input vectors is proposed, and learning algorithms that utilize fuzzy if-then rules as well as numerical data in neural network learning for classification problems and for fuzzy control problems are derived. The learning algorithms can be viewed as an extension of the backpropagation algorithm to the case of fuzzy input vectors and fuzzy target outputs. Using the proposed methods, linguistic knowledge from human experts represented by fuzzy if-then rules and numerical data from measuring instruments can be integrated into a single information processing system (classification system or fuzzy control system). It is shown that the scheme works well for simple examples     

A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classification

In multi-label classification problems, every instance is associated with multiple labels at the same time. Binary classification, multi-class classification and ordinal regression problems can be seen as unique cases of multi-label classification where each instance is assigned only one label. Text classification is the main application area of multi-label classification techniques. However, relevant works are found in areas like bioinformatics, medical diagnosis, scene classification and music categorization. There are two approaches to do multi-label classification: The first is an algorithm-independent approach or problem transformation in which multi-label problem is dealt by transforming the original problem into a set of single-label problems, and the second approach is algorithm adaptation, where specific algorithms have been proposed to solve multi-label classification problem. Through our work, we not only investigate various research works that have been conducted under algorithm adaptation for multi-label classification but also perform comparative study of two proposed algorithms. The first proposed algorithm is named as fuzzy PSO-based ML-RBF, which is the hybridization of fuzzy PSO and ML-RBF. The second proposed algorithm is named as FSVD-MLRBF that hybridizes fuzzy c-means clustering along with singular value decomposition. Both the proposed algorithms are applied to real-world datasets, i.e., yeast and scene dataset. The experimental results show that both the proposed algorithms meet or beat ML-RBF and ML-KNN when applied on the test datasets.

     

A Frame Based Architecture for Information Integration in CIMS

This paper foumulates and architecture for information integration in computer integrated manufacturing systems(CIMS).The architecture takes the frame structure as single link among applications and between applications and physical storage.All the advantages in form features based intgrated systems can be found in the frame-based architecture as the frame structrue here takes from features as its primitives.But other advantage,e.g.,default knowledge and dynamic domain knowledge can be attached to frames and the frame structure is easy to be changed and extended,which cannot be found ing form reatures based systems,can also be showed in frame based architectures as the frame structure is a typical knowledge representation scheme in artificial intelligence and many researches and interests have put on it.     

Induction of descriptive fuzzy classifiers with the Logitboost algorithm

Recently, Adaboost has been compared to greedy backfitting of extended additive models in logistic regression problems, or “Logitboost". The Adaboost algorithm has been applied to learn fuzzy rules in classification problems, and other backfitting algorithms to learn fuzzy rules in modeling problems but, up to our knowledge, there are not previous works that extend the Logitboost algorithm to learn fuzzy rules in classification problems.In this work, Logitboost is applied to learn fuzzy rules in classification problems, and its results are compared with that of Adaboost and other fuzzy rule learning algorithms. Contradicting the expected results, it is shown that the basic extension of the backfitting algorithm to learn classification rules may produce worse results than Adaboost does. We suggest that this is caused by the stricter requirements that Logitboost demands to the weak learners, which are not fulfilled by fuzzy rules. Finally, it is proposed a prefitting based modification of the Logitboost algorithm that avoids this problem     

Tuning certainty factor and local weight of fuzzy production rulesby using fuzzy neural network

Approximate reasoning in a fuzzy system is concerned with inferring an approximate conclusion from fuzzy and vague inputs. There are many ways in which different forms of conclusions can be drawn. Fuzzy sets are usually represented by fuzzy membership functions. These membership functions are assumed to have a clearly defined base. For other fuzzy sets such as intelligent, smart, or beautiful, etc., it would be difficult to define clearly its base because its base may consist of several other fuzzy sets or unclear nonfuzzy bases. A method to handle this kind of fuzzy set is proposed. A fuzzy neural network (FNN) is also proposed to tune knowledge representation parameters (KRPs). The contributions are that we are able to handle a broader range of fuzzy sets and build more powerful fuzzy systems so that the conclusions drawn are more meaningful, reliable, and accurate. An experiment is presented to demonstrate how our method works.     

Handling uncertainty with possibility theory and fuzzy sets in asatellite fault diagnosis application

The fault mode effects and criticality analyses (FMECA) describe the impact of identified faults. They form an important category of knowledge gathered during the design phase of a satellite and are used also for diagnosis activities. This paper proposes their extension, allowing a finer representation of the available knowledge, at approximately the same cost, through the introduction of an appropriate representation of uncertainty and incompleteness based on Zadeh's possibility theory and fuzzy sets. The main benefit of the approach is to provide a qualitative treatment of uncertainty where we can for instance distinguish manifestations which are more or less certainly present (or absent) and manifestations which are more or less possibly present (or absent) when a given fault is present. In a second step, the proposed approach is extended to handle fault impacts expressed as event chronologies. Efficient, real-time compatible discrimination techniques exploiting uncertain observations are introduced, and an example of satellite fault diagnosis illustrates the method. A brief rationale for the choice of possibility theory and fuzzy sets is provided     

Knowledge Based Supervised Fuzzy-Classification: An Application to Image Processing

In this paper, we take an interest in representation and treatment of imprecision and uncertainty in order to propose an original approach to approximate reasoning. This work has a practical application in supervised learning pattern recognition. Production rules whose conclusions are accompanied by belief degrees, are obtained by supervised learning from a training set. The proposed learning method is multi-featured, it allows to take into account the possible predictive power of a simultaneously considered feature conjunction. On the other hand, the feature space partition allows a fuzzy representation of the features and data imprecision integration. This uncertainty is managed in the learning phase as well as in the recognition one. To introduce more flexibility and to overcome the boundary problem due to the manipulations of membership functions of fuzzy sets, we propose to use a context-oriented approximate reasoning. For this purpose, we introduce an adequate distance to compare neighbouring facts. This distance, measuring imprecision, combined with the uncertainty of classification decisions represented by belief degrees, drives the approximate inference. The proposed method was implemented in a software called SUCRAGE and confronted with a real application in the field of image processing. The obtained results are very satisfactory. They validate our approach and allow us to consider other application fields.     

Building a Medical Decision Support System for Colon Polyp Screening by Using Fuzzy Classification Trees

To deal with highly uncertain and noisy data, for example, biochemical laboratory examinations, a classifier is required to be able to classify an instance into all possible classes and each class is associated with a degree which shows how possible an instance is in that class. According to these degrees, we can discriminate the more possible classes from the less possible classes. The classifier or an expert can pick the most possible one to be the instance class. However, if their discrimination is not distinguishable, it is better that the classifier should not make any prediction, especially when there is incomplete or inadequate data. A fuzzy classifier is proposed to classify the data with noise and uncertainties. Instead of determining a single class for a given instance, fuzzy classification predicts the degree of possibility for every class.Adenomatous polyps are widely accepted to be precancerous lesions and will degenerate into cancers ultimately. Therefore, it is important to generate a predictive method that can identify the patients who have obtained polyps and remove the lesions of them. Considering the uncertainties and noise in the biochemical laboratory examination data, fuzzy classification trees, which integrate decision tree techniques and fuzzy classifications, provide the efficient way to classify the data in order to generate the model for polyp screening.     

带传递关系和存在量词的描述逻辑MSC推理

分析了描述逻辑非标准推理的重要性,特别分析了描述逻辑MSC推理的研究现状和存在的问题.针对目前描述逻辑MSC推理不能同时处理传递关系和存在量词的不足,研究了带传递关系和存在量词的描述逻辑εL+的MSC推理问题.提出了一种新的εL+-述图,利用描述树和描述图给出了描述逻辑εL+的MSC近似推理算法,并利用εL+-描述树同态和εL+-描述树描述图同态证明了MSC近似推理算法的正确性.作为一个附带的结果,利用εL+-描述树描述图同态给出了εL+的实例推理算法,也证明了实例推理算法的正确性.     

An Application of Fuzzy Mathematical Morphology to Interval-Valued Knowledge Representation: A Remark

Mathematical morphology is known by its useful tools for processing binary (black-and-white) and gray-tone images. Due to the success of mathematical morphology in processing binary images, there have been many successful attempts to generalize its methods to more general, i.e. gray-tone images. One of these attempts—the most intuitive one is based on replacing sets by fuzzy sets, thus defining so called fuzzy morphological operations. In this paper we show that these operations can be used successfully in nonimage applications. We can use methods developed in fuzzy mathematical morphology to compute the membership functions of different "approximate" statements. Also, an application to interval-valued knowledge representation is given.     

Integrating discourse pragmatics and propositional knowledge for multilingual natural language processing

We present an integrated knowledge representation system for natural language processing (NLP) whose main distinguishing feature is its emphasis on encoding not only the usual propositional structure of the utterances in the input text, but also capturing an entire complex of nonpropositional — discourse, attitudinal, and other pragmatic — meanings that NL texts always carry. The need for discourse pragmatics, together with generic semantic information, is demonstrated in the context of anaphoric and definite noun phrase resolution for accurate machine translation. The major types of requisite pragmatic knowledge are presented, and an extension of a frame-based formalism developed in the context of the TRANSLATOR system is proposed as a first-pass codification of the integrated knowledge base.