Bridge Damage Assessment through Fuzzy Petri Net Based Expert System

The reliability of the techniques adopted for damage assessment is important for bridge management systems. It is widely recognized that the use of expert systems for bridge damage assessment is a promising direction toward bridge management systems. However, several important issues need to be addressed, such as the management of uncertainty and imprecision, the efficiency of fuzzy rule based reasoning, and the need of an explanation facility to increase confidence about the assessment results. To address the issues arising from using expert systems, this paper is aimed at developing an expert system for assessing bridges based on an expert system shell, which is called the fuzzy Petri net based expert system (FPNES). Major features of FPNES include the ability to reason using uncertain and imprecise information, knowledge representation through the use of hierarchical fuzzy Petri nets, a reasoning mechanism based on fuzzy Petri nets, and an explanation of the reasoning process through the use of hierarchical fuzzy Petri nets. Therefore, this expert system for assessing bridges does not impose any restriction on the inference mechanism. Furthermore, this approach offers more informative results than other systems. An application to the damage assessment of the Da-Shi bridge in Taiwan is used as an illustrative example of FPNES.     

Application of a case-based expert system to orthodontic diagnosis and treatment planning: a review of the literature

Computer expert systems are being utilised increasingly in medical fields to assist diagnosis and treatment planning. Traditional rule-based expert systems have some limitations when applied to orthodontic diagnosis and treatment planning. These limitations may be avoided by using a case-based system which is a particular type of expert system that uses a stored data bank of previously-treated cases to provide the knowledge for solving new treatment problems. This article reviews the use of expert systems for orthodontic diagnosis and treatment planning, outlines the rationale, processes and advantages of case-based systems, and gives examples of the application of this technology in medical fields.     

Case-Based Reasoning for Construction Hazard Identification: Case Representation and Retrieval

This paper proposes a case-based reasoning (CBR) approach to construction hazard identification that facilitates systematic feedback of past knowledge in the form of incident cases and hazard identification. This paper focuses on two of the key components of the CBR approach: (1) a detailed knowledge representation scheme, developed based on the modified loss causation model, to codify incident cases and past hazard identification and (2) an intelligent retrieval mechanism that can automatically retrieve relevant past cases. The detailed knowledge representation scheme presented herein is designed to model both incident cases and hazard identification so that both types of knowledge repository can be retrieved simultaneously and adapted for use. The scheme also includes a linguistic structure used to facilitate indexing of cases. The retrieval mechanism is based on the concept of similarity scoring. In this paper, a novel scoring technique based on semantic networks is presented. A case study is presented to demonstrate and validate the proposed approach.     

The two stage case-based reasoning technology research on the basis of the expert decision

In some domain expert’s decision-making is intelligent output by comprehensive analysis and reasoning for the numerous decision-making factors,constraints and the goal in their domain,it is the concentrated expression of domain knowledge.Be aimed at field knowledge induction;sum up the exploration with systematization method,the two stages case-based reasoning(CBR) technology on the basis of the expert decision cased is present.The main idea,according to the characteristic of problem,analysis and decision process of field expert is carried out case-reasoning by two steps,and the field knowledge can be classified,induction and accumulated from different angles by multi case libraries.This technology includes two aspects,first,through two state case-based reasoning mechanisms to achieve area of decision-making process simulation;second,the CBR service in two more cases of library design process.First,two stage case-based reasoning mechanism,the first stage case-based reasoning to analyze the main level,clear nature of the problem,type,degree and character as the main content;the second stage case reasoning takes first order case reasoning result as basis then,the case reasoning,gives to making policy holding out being that purpose is in progress to out a decision-making suggestion once again.Secondly,in many cases the design of the library,to serve two-stage case-based reasoning process,the design of the four case libraries:First, property type case library,is a static case base can be modified,the effect is to performance differences between actual case and plan case of the qualitative distinction,indicate difference type attribute;Second, hierarchic quantization case library,is a static case base can be modified,the effect is to performance differences between actual case and plan case the quantitative distinction between the scope,indicate degree, size that the type issues in some difference etc.Three are that decision-making supports case library,is a static case base can be modified,the effect is similar enumeration all possible decision-makings content, indicate the decision-making that field expert possibility carries out;Four are decision match case library,is a self-study and dynamic case library,the effect is the library recording history decision-making case,is used to gain the decision-making scheme specifically for current reality achievement case is similar,to provide the support making policy,to learn composing in reply the case accumulating the new decision-making at the same time.The method is based on real business needs for research and development obtained after the conclusion,the paper shows through examples of the technology industry has a good adaptability,practicality and effectiveness.     

Beyond belief bias: reasoning from conceptual structures by mental models manipulation

Mental models constitute an alternative to the rule-based systems in the explanation of human reasoning (Johnson-Laird, 1983). In this paper, we claim that the concept of believability generally used to categorize content and context effects is of little use within a semantic theory. Thus, we propose the use of categories that are directly extracted from subjective relations among concepts within the reasoning problem. We demonstrate that manipulations based on this kind of categorization produce predictable patterns of responses in reasoning problems. We present two experiments to test our predictions, using conditional and syllogistic reasoning problems, and in both cases, we demonstrate the influence of conceptual knowledge not only in natural contexts, but also in experimentally created artificial contexts.     

Rule-based consultation systems]

This paper overviews the architecture of rule-based consultation systems and illustrates how such systems work by an Antibiotic Advisor. Knowledge representation and the inference engine implemented in the program are briefly described along with a sample consultation with the system. The paper is concluded with an analysis of the advantages and limitations of rule-based reasoning in clinical decision support.     

Design of Highway Bridges: Natural Place for CBR

The paper addresses a model, a framework, and an implemented system for supporting design activities where the use of case-based reasoning may reveal particular appropriateness. In the proposed environment, special attention is given to the synthesis of solutions by means of adaptation. A pragmatic combination of a number of artificial intelligence (AI) techniques, considering case-based reasoning (CBR) as the framing concept, enables the implementation of a system that conveniently supports most designers’ cognitive needs. The design of highway bridges was the chosen domain of discourse, for it represents an excellent example for demonstrating the potential of analogy in design. Thus, a large base of real cases is built. The induction of new knowledge is performed by extraction, association, and regression processes. Finally, a real context is used to illustrate the use of the model and to demonstrate its utility and capabilities in supporting designers’ decisions, particularly on the synthesis—i.e., adaptation—of solutions.     

Automatic and controlled components of judgment and decision making.

The categorization of inductive reasoning into largely automatic processes (heuristic reasoning) and controlled analytical processes (rule-based reasoning) put forward by dual-process approaches of judgment under uncertainty (e.g., K. E. Stanovich & R. F. West, 2000) has been primarily a matter of assumption with a scarcity of direct empirical findings supporting it. The present authors use the process dissociation procedure (L. L. Jacoby, 1991) to provide convergent evidence validating a dual-process perspective to judgment under uncertainty based on the independent contributions of heuristic and rule-based reasoning. Process dissociations based on experimental manipulation of variables were derived from the most relevant theoretical properties typically used to contrast the two forms of reasoning. These include processing goals (Experiment 1), cognitive resources (Experiment 2), priming (Experiment 3), and formal training (Experiment 4); the results consistently support the author's perspective. They conclude that judgment under uncertainty is neither an automatic nor a controlled process but that it reflects both processes, with each making independent contributions. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

知识图谱的最新进展、关键技术和挑战

围绕知识图谱的全生命周期技术，从知识抽取、知识融合、知识推理、知识应用几个层面展开综述，重点介绍了知识融合技术和知识推理技术。通过知识抽取技术，可从已有的结构化、半结构化、非结构化样本源以及一些开源的百科类网站抽取实体、关系、属性等知识要素。通过知识融合，可消除实体、关系、属性等指称项与实体对象之间的歧义，得到一系列基本的事实表达。通过本体抽取、知识推理和质量评估形成最终的知识图谱库。按照知识抽取、知识融合、知识推理3个步骤对知识图谱迭代更新，实现碎片化的互联网知识的自动抽取、自动关联和融合、自动加工，从而拥有词条自动化链接、词条编辑辅助功能，最终达成全流程自动化知识获取的目标。最后，讨论知识图谱未来的发展方向与可能存在的挑战。      

Evolutionary Fuzzy Neural Inference System for Decision Making in Geotechnical Engineering

Problems in geotechnical engineering are full of uncertain, vague, and incomplete information. In most instances, successfully solving such problems depends on experts’ knowledge and experience. The primary object of this research was to develop an evolutionary fuzzy neural inference system (EFNIS) to imitate the decision-making processes in the human brain in order to facilitate geotechnical expert decision making. First, an evolutionary fuzzy neural inference model (EFNIM) was constructed by combining the genetic algorithm (GA), fuzzy logic (FL), and neural network (NN). In the proposed model, GA is primarily concerned with optimizing parameters required in the fuzzy neural network; FL with imprecision and approximate reasoning; and NN with learning and curve fitting. This research then integrates the EFNIM with an object-oriented computer technique to develop an EFNIS. Finally, the potential to apply the proposed system to practical geotechnical decision making is validated using two real problems, namely estimating slurry wall duration and selecting retaining wall construction methods.     

Evaluation of Solid Waste Management System Using Fuzzy Composition

Evaluating the best alternative for a solid waste management system often requires decision makers to consider conflicting, vague, and uncertain information. Fuzzy set theory offers a possible means of managing these kinds of data or information. In this study, we propose a systematic approach to evaluating a solid waste management system in a fuzzy environment. The approach employs three main concepts: linguistic variables, fuzzy numbers, and an analytic hierarchy process. The linguistic variables are used to represent the degree of appropriateness of decision criteria, which are vague or uncertain. These linguistic variables are then translated into fuzzy numbers to reflect their uncertainties and aggregated into the final fuzzy decision value using a hierarchical structure. Through a case study, the approach is applied to the evaluation of a solid waste management system consisting of four different treatment alternatives. The results demonstrate that the developed approach can be a useful tool for evaluating a solid waste management system, where criteria are vague or imprecise.     

Computer-assisted diagnosis of postmenopausal osteoporosis using a fuzzy expert system shell

This paper shows how the AMDIS (Automated Medical Diagnosis with Intelligent Systems) integrated system can be employed to build a fuzzy medical expert system in the domain of postmenopausal osteoporosis. The fundamental aims of the expert system are to standardize knowledge and support physicians in the early detection of postmenopausal osteoporosis. A wide range of diagnostic situations has been considered for both categories of the disease, with judgments that range from disease is excluded to disease is definite. The salient aspects of the approach are the use of fuzzy logic as an analytic language for the representation and manipulation of knowledge and strategies and the integration of structured interview techniques and learning-by-example to address the knowledge acquisition task.     

高线轧机齿轮箱故障诊断案例库的设计与应用

以高速线材轧机齿轮箱故障诊断系统为应用背景,探讨了基于案例的推理方法,研究了构建齿轮箱故障诊断系统的关键技术,最终制订出相应的设计方案。依据故障的特征属性和内容将齿轮箱故障案例按照不同轧机和架次分类,从而将整个案例库划分成了多个代表不同齿轮箱的案例集。在案例检索中,通过加权平均算法找到相应案例库中与目标案例最相似的案例,实现了对高速线材轧机齿轮箱故障的智能诊断。     

The empirical case for two systems of reasoning.

Distinctions have been proposed between systems of reasoning for centuries. This article distills properties shared by many of these distinctions and characterizes the resulting systems in light of recent findings and theoretical developments. One system is associative because its computations reflect similarity structure and relations of temporal contiguity. The other is "rule based" because it operates on symbolic structures that have logical content and variables and because its computations have the properties that are normally assigned to rules. The systems serve complementary functions and can simultaneously generate different solutions to a reasoning problem. The rule-based system can suppress the associative system but not completely inhibit it. The article reviews evidence in favor of the distinction and its characterization. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

CB-Contract: Case-Based Reasoning Approach to Construction Contract Strategy Formulation

The formulation of an appropriate contract strategy is a fundamental yet critical aspect of contractual arrangement for a project. This paper presents a decision support system (DSS) prototype for contract strategy formulation using the case-based reasoning (CBR) approach. The prototype is called CB-Contract. An overview on the scope and formulation process of contract strategy is provided. Contract strategy is regarded as comprising four main substrategies: (1) work packaging; (2) functional grouping; (3) contract type; and (4) award method. The significance of CBR in contract strategy formulation is examined. The overall architecture of CB-Contract is discussed, with particular emphasis on its knowledge component. A case structure for case representation has been suggested taking into account the important factors that need to be considered while formulating a contract strategy, in particular project characteristics, client’s objectives, and client’s comparative advantages. Issues pertaining to case indexing and similarity calculation for efficient retrieval of similar cases are explored. An illustrative example to obtain similar cases for a given set of project information is presented. Case adaptation that considers the robustness of each substrategy, the compatibility of the substrategies, and the expected effectiveness of the contract strategy is also included in the illustrative example.     

A Representation for Conceptual Design of Buildings

The building representation for storing design solutions adopted in SEED-Config consists of two levels of abstraction models defined on top of the object-oriented data model. The first level is an information model, called the building entity and technology model, which stores design data as they are generated during conceptual design, supports case-based reasoning, and shares data among all design participants. This model represents each building entity as a generic container that encompasses its properties, taxonomy, geometry, composition, relationships, and design knowledge applied in generating it. The second level is a conceptual model that defines the types of objects, relationships, and data needed to fully represent the information in a given design domain. The conceptual model specifies the semantics of the design information for the domain using the syntax defined in the information model. The proposed representation also includes a faceted classification scheme to define the controlled vocabulary from which indexes are obtained. The representation has the following advantages: It integrates multiple views, supports design evolution, supports design exploration, and is extensible.     

Facilitating children's proportional reasoning: A model of reasoning processes and effects of intervention on strategy change.

In this study, 140 fourth graders were asked to solve proportion problems about juice-mixing situations both before and after an intervention that used a manipulative model or other materials in 3 experiments. Using a manipulative model based on children's prior knowledge about crowdedness and equal distribution was effective in letting children discover a unit strategy, which was useful for solving proportion problems. The model was more effective for those who had an appropriate representation but could not correctly compare juice concentrations than it was for those who didn't have the representation. On the basis of this study, different approaches appear to be necessary to facilitate children's proportional reasoning, depending on the reasoning process (representation or comparison) with which children are having difficulty. Interventions on the basis of the process model and learning that builds on intuitive knowledge are discussed. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Can any ostrich fly?: some new data on belief bias in syllogistic reasoning

According to one version of the mental models theory (Oakhill, J.V., Johnson-Laird, P.N., Garnham, A., 1989. Believability and syllogistic reasoning. Cognition 31, 117-140) beliefs exert their influence on reasoning in three ways. First they can affect the interpretation of the premises, for example by conversion. Second, they can curtail the search for alternative models of the premises, if an initial model supports a believable conclusion. Third, they can act as a filter on any conclusion that is eventually generated. This last influence is important in explaining the effects of belief bias in one-model syllogisms with no convertible premises, since such syllogisms, by definition, have no alternative models. However, the most natural interpretation of such a filter is that it filters out conclusions and leads to the response 'no valid conclusion'. The present study, which was conducted with groups of both British and Italian subjects, looked at the effect of prior knowledge on syllogistic reasoning, and showed that: (1) invalid conclusions for such one model syllogisms, either thematic or abstract, are typically not of the type 'no valid conclusion', but state invalid relations between the end terms; (2) belief-bias is completely suppressed when previous knowledge is incompatible with the premises, and therefore the premises themselves are always considered. The results are compatible with a version of the mental models theory in which a representation of prior knowledge precedes modelling of the premises, which are then incorporated into the representation of this knowledge. The relation between this theory and other accounts of belief bias in syllogistic reasoning, and the implications of these findings for reasoning more generally, are considered.     

Application of Fuzzy Logic to Simulation for Construction Operations

This paper describes the application of fuzzy logic to discrete event simulation in dealing with uncertainties of construction operations. The uncertainties in the quantity of resources required to activate an activity are modeled with fuzzy sets in linguistic terms. The fuzzy logic if-then rule is built to control the activation of activities. The duration of the activity that varies with the quantities of resources involved is determined through the fuzzy logic rule-based model. The fuzzy logic control of activities is incorporated with the activity scanning simulation strategy to implement the fuzzy simulation system for construction operations. In addition, the fuzzy activity element is adopted in the graphical modeling process. Examples are given that illustrate uses of the fuzzy simulation system and the impact of flexible demand of resources on productivity.