Search results

The importance of fuzzy logic (FL) in approximate reasoning, and that of default logic (DL) in reasoning with incomplete information, is well established. Also, the need for a commonsense reasoning framework that handles both these aspects has been widely anticipated. The purpose of this paper is to show that fuzzyfied default logic (FDL) is an attempt at creating such a framework.

The basic syntax, semantics, unique characteristics and examples of its complex reasoning abilities have been presented in this paper.

Interestingly, FDL turns out to be a generalization of traditional DL, with even better support for non‐monotonic reasoning.

The paper presents a generalized tool for commonsense reasoning which can be used for inference under incomplete information.

In the framework of his theory of relative information, derives formulae to convert probability into possibility and conversely, and shows how they can be utilized to reconsider many questions related to fuzzy sets and approximate reasoning on a meaningful information theoretic standpoint. The key of this approach is that the membership of a fuzzy set is now thought of as equivalent to the pair (probability, fuzziness coefficient). Therefore a sound new concept of informational membership entropy, which is fully consistent with Shannon theory, is evolved. In this new paradigm, the fuzzy object is simultaneously defined by the set itself and its complement. Thus obtains a new modelling for the union and the intersection of fuzzy sets, and new approaches to quantitatively modelling “If A then B”.

The purpose of this paper is to ascertain overall system health and maintenance needs with degree of certainty using condition‐monitoring data with hierarchical fuzzy inference system.

In process plants, equipment condition is ascertained using condition‐monitoring data for each condition indicator. For large systems with multiple condition indicators, estimating the overall system health becomes cumbersome. The decision of selecting the equipment for an overhaul is mostly determined by generic guidelines, and seldom backed up by condition‐monitoring data. The proposed approach uses a hierarchical system health assessment using fuzzy inference on condition‐monitoring data collected over a period. Each subsystem health is ascertained with degree of certainty using degree of match operation performed on fuzzy sets of condition‐monitoring data and expert opinion. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge.

The proposed approach has been applied to a large electric motor (> 500kW), which is treated as four subsystems i.e. power transmission system, electromagnetic system, ventilation system and support system. Fuzzy set of condition‐monitoring data of each condition indicator on each subsystem is used to ascertain the degree of match with the expert opinion fuzzy set, thus inferring the need for periodical overhaul. Subjective expert opinion and quantitative condition‐monitoring data have been evaluated using hierarchical fuzzy inference system with a rule base. It is found that the certainty of each subsystem's health is not the same at the end of 600 days of monitoring and can be classified as “very good”, “good”, “marginal” and “sick”. Degree of certainty has helped in taking a managerial decision to avoid “over‐maintenance” and to ensure reliability. Large volumes of condition‐monitoring data not only helped in assessing motor overhaul health, but also guide the maintenance engineer to suitably review maintenance/monitoring strategy on similar systems to achieve desired reliability goals.

Condition‐monitoring data collected for long periods can be utilized to understand the degree of certainty of degradation pattern in the longer time frame with reference to domain knowledge to improve effectiveness of predictive maintenance towards reliability.

The paper gives an opportunity to evaluate quantitative condition‐monitoring data and subjective/qualitative domain expertise using fuzzy sets. The predictive maintenance cycle “Monitor‐analyse‐plan‐repair‐restore‐operate” is scientifically regulated with a degree of certainty. Approach is generic and can be applied to a variety of process equipment to ensure reliability through effective predictive maintenance.

A problem of handling fuzzy quantities in a process of knowledge acquisition and deriving an inference mechanism by means of fuzzy relation equations is studied in extensive way. It is clearly pointed out that both of them are closely related and correspond to various types of fuzzy relation equations that are considered. Their relevance to the form of knowledge collected is also indicated. A problem of dimension reduction of a knowledge base is considered as well. Two modes of the use of the knowledge base (goal‐, and data‐driven) are also studied.

This paper explains the results of a doctoral thesis in production engineering and presents a tool for the qualitative evaluation of office buildings performance, using the Cosenza hierarchical analysis model (HAM). This embodies the principles of the complex thought of Edgar Morin, of fuzzy sets approach and of linguistic variables use. Starting from a group of regular attributes, HAM confronts, in one single operation, the competing forces of space and building facilities by different organizations with the supply of a group of buildings. The results of the simulation confirm the effectiveness of a tool that links manageability to a formal representation of a subjective decision‐making processes, based on environment‐behaviour aspects. Whilst confronting different organizational demands with the supply of a group of buildings, this model makes possible rapid decision making and the identification of a supply profile for the purpose of acquisition.

Discovering and recommending points of interest are drawing more attention to meet the increasing demand from personalized tours. This paper aims to propose and evaluate two fuzzy-based systems for decision of sightseeing spots considering different conditions.

In the system, the authors considered four input parameters as follows: ambient temperature (AT), air quality (AQ), noise level (NL) and the current number of people (CNP) to decide the sightseeing spots visit or not visit (VNV). The authors call the proposed system: fuzzy-based decision visiting systems (FBDVSs). The authors implemented two systems as follows: FBDVS1 (three input parameters) and FBDVS2 (four input parameters). The authors make a comparison study between FBDVS1 and FBDVS2. The authors evaluate the proposed systems by computer simulations.

From the simulations results, the authors conclude that when CNP is increased, the VNV is increased. However, when AQ and NL are increased, the VNV is decreased. Also, when the AT is around 18°C-26°C, the VNV is the best. Comparing the complexity, the FBDVS2 is more complex than FBDVS1. However, FBDVS2 considers also the AT, which makes the system more reliable.

In the future, the authors would like to make extensive simulations to evaluate the proposed systems and compare the performance of the proposed systems with other systems.

By simulation results, the authors have shown that the proposed system has a good performance and can choose good sightseeing spots.

Taking into account the importance of e‐commerce and the current applications of AI techniques in this area, this research aims to adequate the design of a multi‐agent system for the provisioning of e‐services in u‐commerce environments. This proposal is centred on the methods of evaluation in a u‐e‐commerce environment.

The multi‐agent systems (MAS) approach is based on an MAS model developed for AmI that has been redesigned to support u‐commerce. The use of a recommendation system, previously developed by the research group, is suggested for this MAS. The methodological proposal centres on the evaluation of this type of system.

The evaluation of this type of system is the principal problem of current research. Therefore, this is the main contribution of the paper.

The different evaluation methods that are proposed, whether qualitative or quantitative, offer the possibility of measuring the added value that the context can give to the use of e‐services in different domains of application. Qualitative evaluation should consider the customer as a central piece in the system. In addition, quantitative methods should objectively evaluate the contribution of context to the application.

At present, there is no single method for evaluating the benefits of different u‐commerce systems, so a new method needs to be found based on these techniques.

The research proposes an MAS designed for u‐commerce domains, analyzes the capacity of trust management techniques in this environment, and proposes several evaluation methods to show the benefits of context information in the use of e‐services. Several real developments are described to show the different applications of MAS in u‐commerce and how evaluation is carried out.

The purpose of this paper is to propose an alternative approach to approximate reasoning by DNA computing, thereby adding a new dimension to the existing approximate reasoning method by bringing it down to nanoscale computing. The logical aspect of approximate reasoning is replaced by DNA chemistry.

To achieve this goal, first the synthetic DNA sequence fuzzified by quantum dot, which is a recent advancement of nanotechnology. Thus with the help of fuzzy DNA, which holds the vague concept of human reasoning, the basic method of approximate reasoning on a DNA chip is realized. This approach avoids the tedious choice of a suitable implication operator (for a particular application) necessary for existing approximate reasoning based on fuzzy logic. The inferred consequences obtained from DNA computing‐based approximate reasoning is ultimately hybridized with appropriate complementary sequence probed on a DNA‐chip to confirm the result of inference.

The present approach is suitable for reasoning under vague and uncertain environment and does not require any subject choice of any individual expert, which is essential for existing approximate reasoning method.

This new tool for approximate reasoning based on DNA computing is applicable to several problems of science and engineering; namely pattern classification, control theory, weather forecasting, atmospheric science, etc.