Название: Fuzzy Petri Nets for Knowledge Representation, Acquisition and ReasoningАвтор: Hua Shi, Hu-Chen Liu
Издательство: Springer
Год: 2023
Страниц: 476
Язык: английский
Формат: pdf (true)
Размер: 12.2 MB
With the development of Artificial Intelligence, developing expert systems to simulate human thinking has become a hot research topic nowadays. Expert system is an intellectual programming system that uses the knowledge captured from experts to solve specific problems reaching the level of experts. The crucial issues in developing an expert system are the representation of the obtained expert knowledge, the acquisition of experts’ professional knowledge, and the reasoning process of knowledge rules. So far, many knowledge representation methods have been introduced in the literature. Among them, the fuzzy Petri nets (FPNs) are a promising modelling tool for expert systems and have a couple of attractive advantages.
Combining fuzzy sets and Petri nets, the FPNs are a graphical and mathematical model tool for representing imprecise information and supporting fuzzy reasoning in expert systems. An FPN is a marked graphical system containing places and transitions, where graphically circles represent places, bars depict transitions, and directed arcs denote the relationships between places and transitions. The main features of an FPN are that it supports visualized representation of information and provides a unified form to deal with imprecise and uncertain knowledge information. Due to these characteristics, the FPN method has been applied to many industrial fields for knowledge representation and reasoning in expert systems.
Although the FPN model is a useful mathematical technique for knowledge representation and reasoning, it is plagued by a number of shortcomings when applied in real-life situations. Therefore, how to enhance the performance of FPNs has attracted considerable attention from both academics and practitioners. In this book, we provide an in-depth and systematic introduction to different types of new FPN models to enhance the performance of traditional methods and implement the rule-based reasoning intelligently. In addition, various engineering problems in different industries are provided to demonstrate the applicability and effectiveness of the developed FPN models. The strengths and practicality of the proposed models are further validated by using comparative analysis with existing methods.
This book is very interesting for practitioners and academics working in the fields of knowledge management, Artificial Intelligence (AI), industrial and production engineering, and management science and engineering, etc.
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