Artificial Intelligence and Expert Systems for Engineers

C.S. Krishnamoorthy, S. Rajeev

• Publisher: CRC Press
• Year: 2018
• eBook ISBN: 9781351465588
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

This book provides a comprehensive presentation of artificial intelligence (AI) methodologies and tools valuable for solving a wide spectrum of engineering problems. What's more, it offers these AI tools on an accompanying disk with easy-to-use software.
Artificial Intelligence and Expert Systems for Engineers details the AI-based methodologies known as: Knowledge-Based Expert Systems (KBES); Design Synthesis; Design Critiquing; and Case-Based Reasoning. KBES are the most popular AI-based tools and have been successfully applied to planning, diagnosis, classification, monitoring, and design problems. Case studies are provided with problems in engineering design for better understanding of the problem-solving models using the four methodologies in an integrated software environment.
Throughout the book, examples are given so that students and engineers can acquire skills in the use of AI-based methodologies for application to practical problems ranging from diagnosis to planning, design, and construction and manufacturing in various disciplines of engineering.
Artificial Intelligence and Expert Systems for Engineers is a must-have reference for students, teachers, research scholars, and professionals working in the area of civil engineering design in particular and engineering design in general.

Chapter 1

INTRODUCTION

1.1 GENERAL

Engineer utilise principles of science and mathematics to develop certain technologies. These technologies are then used to create engineered artifacts such as products, structures, machines, processes or entire systems.

However, this is too abstract a definition for the engineer’s sphere of operation. It must be analysed in greater detail for an understanding of how engineers create the artifacts that improve the quality of life. When an engineer creates an artifact in any area of application, he has to employ a host of related activities like planning, conceptual design, analysis, detailing, drafting, construction, manufacturing and maintenance. Depending on the type of problem that is being addressed and the domain, different combinations and different sequences of these activities are undertaken. Right from the days of ENIAC, the first digital computer, computers have been extensively used by the engineering community to expedite or automate some of the numerous tasks. The history of the use of computers in engineering problems parallels the developments in computer hardware and software technology. Such developments have advanced at such an unbelievable pace in the past fifteen years that today’s desktop computers are far more capable than the mainframe computers of the last decade. Developments are not constrained to faster CPUs alone. The emergence of improved paradigms such as parallel and distributed computing, backed up by appropriate software environments, has virtually transformed the direction of research in computer usage in engineering. From the development of faster and faster algorithms, we have moved to developments for evolving improved methods of assistance. This has resulted in the transformation of computers from large numerical computing machines to aids to engineers at every stage of problem solving.

Numerical computing-intensive tasks were the early applications attempted to be solved with the aid of computers in the early days of computer usage by the engineering community. Research in the areas of computer graphics, database management systems and Artificial Intelligence (AI) along with the development of faster and more powerful hardware platforms accelerated and widened the use of computers for engineering problem solving. Computer graphics tools improved the visualisation capabilities, thereby making it possible for complete graphical simulation of many engineering processes. DataBase Management Systems (DBMS) provided engineers with necessary tools for handling and manipulating the large amount of data generated during processing in a systematic and efficient manner. Integration of spatial information handling and graphical presentation with DBMS provided a very powerful tool, viz., the Geographical Information System (GIS), which has really revolutionised computer-assisted execution of many tasks in many disciplines of engineering. Still, all these developments helped only numerical computing-intensive, data-intensive and visualisation-based problems. One of the major tasks in many of the activities mentioned earlier is decision making, which is required in different stages of execution of each of the tasks. Decision making requires processing of symbolic information in contrast to the conventional data processing, handling of facts and inference using domain knowledge. Inference is nothing but search through the knowledge base using the facts. The intensive research carried out in the area of AI in the last four decades resulted in the emergence of a number of useful techniques which can be used for solving many complex problems.

1.2 DEVELOPMENTS IN ARTIFICIAL INTELLIGENCE

In the early 1950s Herbert Simon, Allen Newell and Cliff Shaw conducted experiments in writing programs to imitate human thought processes. The experiments resulted in a program called Logic Theorist, which consisted of rules of already proved axioms. When a new logical expression was given to it, it would search through all possible operations to discover a proof of the new expression, using heuristics. This was a major step in the development of AI. The Logic Theorist was capable of quickly solving thirty-eight out of fifty-two problems with proofs that Whitehead and Russel had devised [1]. At the same time, Shanon came out with a paper on the possibility of computers playing chess [2].

Though the works of Simon et al and Shanon demonstrated the concept of intelligent computer programs, the year 1956 is considered to be the start of the topic Artificial Intelligence. This is because the first AI conference, organised by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shanon at Dartmouth College in New Hampshire, was in 1956. This conference was the first organised effort in the field of machine intelligence. It was at that conference that John McCarthy, the developer of LISP programming language, proposed the term Artificial Intelligence. The Dartmouth conference paved the way for examining the use of computers to process symbols, the need for new languages and the role of computers for theorem proving instead of focusing on hardware that simulated intelligence.

Newell, Shaw and Simon developed a program called General Problem Solver (GPS) in 1959, that could solve many types of problems. It was capable of proving theorems, playing chess and solving complex puzzles. GPS introduced the concept of means-end analysis, involving the matching of present state and goal state. The difference between the two states was used to find out new search directions. GPS also introduced the concept of backtracking and subgoal states that improved the efficiency of problem solving [3]. Backtracking is used when the search drifts away from the goal state from a previous nearer state, to reach that state. The concept of subgoals introduced a goal-driven search through the knowledge. The major criticism of GPS was that it could not learn from previously solved problems. In the same year, John McCarthy developed LISP programming language, which became the most widely used AI programming language [4].

Kenneth Colby at Stanford University and Joseph Weizenbaum at MIT wrote separate programs in 1960, which simulated human reasoning. Weizenbaum’s program ELIZA used a pattern-matching technique to sustain very realistic two-way conversations [5]. ELIZA had rules associated with keywords like ‘I’, ‘you’, ‘like’ etc., which were executed when one of these words was found. In the same year, Minsky’s group at MIT wrote a program that could perform visual analogies [6]. Two figures that had some relationship with each other were described to the program, which was then asked to find another set of figures from a set that matched a similar relationship.

The other two major contributions to the development of AI were a linguistic problem solver STUDENT [7] and a learning program SHRDLU [8]. The program STUDENT considered every sentence in a problem description to be an equation and processed the sentences in a more intelligent manner. Two significant features of SHRDLU were the ability to make assumptions and the ability to learn from already solved problems.

Parallel to these developments, John Holland at the University of Michigan conducted experiments in the early 1960s to evolve adaptive systems, which combined Darwin’s theory of survival-of-the-fittest and natural genetics to form a powerful search mechanism [9]. These systems with their implicit learning capability gave rise to a new class of problem-solving paradigms called genetic algorithms. Prototype systems of applications involving search, optimisation, synthesis and learning were developed using this technique, which was found to be very promising in many engineering domains [10].

Extensive research and development work has been carried out by many to simulate learning in the human brain using computers. Such works led to the emergence of the Artificial Neural Network (ANN) [11,12] as a paradigm for solving a wide variety of problems in different domains in engineering. Different configurations of ANNs are proposed to solve different classes of problems. The network is first trained with an available set of inputs and outputs. After training, the network can solve different problems of the same class and generate output. The error level of the solution will depend on the nature and number of problem sets used for training the network. The more the number and the wider the variety of data sets used for training, the lesser will be the error level in the solutions generated. In fact, this technique became very popular among the engineering research community, compared to other techniques such as genetic algorithms, due to simplicity in its application and reliability in the results it produced.

All these developments that took place in the field of AI and related topics can be classified into eight specialised branches:

1. Problem Solving and Planning: This deals with systematic refinement of goal hierarchy, plan revision mechanisms and a focused search of important goals [13].

2. Expert Systems: This deals with knowledge processing and complex decision-making problems [14,15, 16].

3. Natural Language Processing: Areas such as automatic text generation, text processing, machine translation, speech synthesis and analysis, grammar and style analysis of text etc. come under this category [17].

4. Robotics: This deals with the controlling of robots to manipulate or grasp objects and using information from sensors to guide actions etc. [18].

5. Computer Vision: This topic deals with intelligent visualisation, scene analysis, image understanding and processing and motion derivation [6].

6. Learning: This topic deals with research and development in different forms of machine learning [19].

7. Genetic Algorithms: These are adaptive algorithms which have inherent learning capability. They are used in search, machine learning and optimisation [9, 10].

8. Neural Networks: This topic deals with simulation of learning in the human brain by combining pattern recognition tasks, deductive reasoning and numerical computations [11].

Out of these eight topics, expert systems provided the much needed capability to automate decision making in engineering problem solving.

1.3 DEVELOPMENTS IN EXPERT SYSTEMS

Although ANN and Genetic Algorithms (GA) provided many useful techniques for improving the effectiveness and efficiency of problem solving, expert systems and developments in related topics made it possible to address many down-to-earth problems. Expert system technology is the first truly commercial application of the research and development work carried out in the AI field. The first successful expert system DENDRAL, developed by Fiegenbaum, demonstrated a focused problem-solving technique which was not characterised in AI research and development [20]. The program simulated an expert chemist’s analysis and decision-making capability. A number of expert systems in different domains, such as geological exploration, medical diagnosis etc., were developed using the concepts presented by Fiegenbaum in DENDRAL. There was apprehension among the AI community to accept expert systems as AI programs, since they used specific knowledge of a domain to solve narrow problems. Development of practical applications using the techniques of expert systems accelerated with the introduction of two new concepts, viz., scripts and frames. Roger Schank in 1972 introduced the concept of ‘script’ that represents a set of familiar events that can be expected from an often-encountered setting [21]. Minsky in 1975 proposed the concept of ‘frame’, which helps in a structured representation of scenarios and objects [6]. A combination of heuristics with scripts or frames considerably improved the capability of knowledge representation and inference strategies in expert systems. Many knowledge-based expert systems were developed in engineering and non-engineering domains. Stand-alone expert systems did not appeal much to the engineering community due to their limited applicability to narrow problem domains. Expert systems were found to be ideal for integrating different programs in a domain resulting in the development of decision support systems. Decision support systems integrate heuristic knowledge-based inference, description of scenarios and situations using a network of frames, objects or scripts, conventional programs and databases.

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Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• PREFACE
• Chapter 1 INTRODUCTION
• Chapter 2 SEARCH TECHNIQUES
• Chapter 3 KNOWLEDGE-BASED EXPERT SYSTEM
• Chapter 4 ENGINEERING DESIGN SYNTHESIS
• Chapter 5 CRITICISM AND EVALUATION
• Chapter 6 CASE-BASED REASONING
• Chapter 7 PROCESS MODELS AND KNOWLEDGE-BASED SYSTEMS
• Appendix I
• Appendix II
• Appendix III
• Index