CHAPTER 2 THEORETICAL FOUNDATION

Transcription

1 CHAPTER 2 THEORETICAL FOUNDATION 2.1. Artificial Intelligence Artificial Intelligence (AI) history begins thousands of years ago when people were drawn to the idea of creating intelligence outside the human body. Charles Babbage ( ) in 1823 made a plan of creating calculating machine called Difference Engine. The attempt continues toward the modern era when Claude Shannon in 1950 first suggested that computers would be able to play chess. The present age can be described as the age of entrepreneurs, or the commercialization of AI (Turban, Efraim, 1992, p4-8) Definition According to Marvin Minsky, Artificial Intelligence is the science of making machines do things that would require intelligence if done by men (Turban, Efraim, 1992, p35). Most experts, however, agree that AI is concerned with two basic ideas. First, it involves studying the thought processes of humans to understand what intelligence is; second, it deals with representing those processes via machines computers, robots, etc. (Turban, Efraim, 1992, p4) Major Fields of Artificial Intelligence AI has been growing into some fields, which requires special attention to conquer. Expert System is the most well developed field of AI that many practical products are available in the market today. Other fields of AI are Natural Language Processing, Speech (Voice) Understanding, Robotics and Sensory Systems, Computer Vision and Scene Recognition, Intelligent Computer-aided Instruction, Automatic Programming, Intelligent Workstations, Summarizing News, and Translation from One Language to Another (Turban, Efraim, 1992, p21-27) Expert System Expert System (ES) is one of Artificial Intelligence fields. The purpose of expert systems is to assist a person s thinking process, not merely to provide information for a person to think about. Properly used, they can help a person become a little wiser, not just better informed. AI and expert systems are not the same. AI encompasses many aspect of human behavior (speech, language, movement, etc), whereas expert systems focus on the task of problem solving (Awad, Elias M., 1996, p15).

2 Definition The name expert systems was derived from the term knowledge-based expert systems. An expert system is a system that employs human knowledge captured in a computer to solve problems that ordinarily require human expertise (Turban, Efraim, 1993, p466). According to Awad, Expert System is an artificial intelligence program that represents captured human knowledge and the rules of thumb (reasoning) used by one or more human experts to solve a specific problem (Awad, Elias M., 1996, p64). A standard definition of expert system is difficult to articulate A survey of more that 80 books and 200 published papers revealed about as many definitions as definitions as there were publications. A definition representing the bulk of the work is that an expert system consists of computer software programs that emulate or clone the reasoning of a human expert in a problem domain (Awad, Elias M., 1996, p51) Major Components of Expert Systems Components of Expert Systems may vary from on expert system to another; however, all expert systems have four major components, which is knowledge base, inference engine, justifier/scheduler, and user interface (Awad, Elias M., 1996, p58). The knowledge base is the repository of the rules, facts, and knowledge acquired from the human expert (Awad, Elias M., 1996, p58). Inference engine is the brain of an expert system; a cluster of computer programs that coordinate the reasoning and inferencing based on the rules of the knowledge base to come up with a solution (Awad, Elias M., 1996, p60). Justifier (Explanation Subsystem) is a component of an expert system that can explain the system s reasoning and justify its conclusions (Turban, Efraim, 1998, p858). Scheduler is the port of the inference engine that coordinates and controls the sequence of the rules (Awad, Elias M., 1996, p64). User interface of expert system is a language processor for friendly, problem-oriented communication between the user and the computer (Turban, Efraim, 1998, p448) Expert System Development Life Cycle (ESDLC) An expert system is basically computer software, so its development follows a software development process. The goal of such a process is to maximize the probability of developing viable, sustainable software within cost limitations and on schedule. The main functions of a model of this process are to determine the order of the steps (or tasks) involved in the software development and to establish the transition criteria for progressing from one stage to another (Turban, Efraim, 1998, p604) Steps in Developing Expert System Steps of Expert System Development Life Cycle have yet standardized, therefore there are several approaches have been suggested for building expert system. The methodology is mainly an improvement from conventional system development life cycle. Although

3 the conventional system development and the expert system development differ in some important ways, the two approaches are fundamentally similar. While the conventional approach may still be used in developing expert systems, iterative design, prototyping, early testing, and other variations for building conventional system are replacing the waterfall steps. As a summary Elias M. Awad introduced an Expert System Development Life Cycle. (Awad, Elias M., 1996, p99). Problem Identification Preliminary requirements analysis and knowledge acquisition Selection of expert system tools Representation Knowledge acquisition Prototype development Verification & validation Implementation Operation & maintenance Figure 2.1. Expert System Development Life Cycle (Awad, Elias M., 1996, p97). The expert system development life cycle (ESDLC) as displayed in figure 2.1. begins with a problem. A knowledge engineer works with an organization s user to identify a problem domain and then builds an expert system accordingly. The important initial phase is acquiring knowledge from an expert, someone with proven, expertise in the

4 problem domain. Once the needed knowledge is acquired, it is represented as knowledge representation, which then will become the system s knowledge base. The knowledge base is tested before it is made available to the user. This test is the last step in the life cycle (Awad, Elias M., 1996, p94) Differences with System Development Life Cycle (SDLC) The conventional system life cycle is process-driven and documentation-oriented, with emphasis on the flow of the data and the resulting system. It fosters the specify then build approach. The ESDLC is result-oriented. The emphasis is on a start slow and grow incremental process. Conventional system development does not support rapid prototyping or advanced languages because if follows a set sequence of steps. The ESDLC utilized rapid prototyping, incorporating changes on the spot, which augments and refines the system until it is ready for use (Awad, Elias M., 1996, p97). The prototyping, knowledge acquisition, and knowledge presentation steps are unique in ESDLC. Prototyping is a process that enables the developer to create a model of the software that must be built (Pressman, Roger S., 1992, p27). By nature, prototyping involves a number of iterations, through which the prototype is gradually altered and expanded to meet organizational needs (Zahedi, Fatemeh, 1992, p227). Knowledge acquisition involves elicitation, analysis, and interpretation of the knowledge that a human expert uses to solve a particular problem (Awad, Elias M., 1996, p112). Knowledge representation is formalism for representing facts or rules in the computer about a subject or a specialty (Turban, Efraim, 1998, p862) Ellipse Ellipse is a totally integrated system, modular in design, and covering four primary subsystems of management information, with two other sub-systems of support functions. The main areas of management information are: Materials management (Supply) Operations and Maintenance management Financial management Human Resources management Each of these major sub-systems is further segmented into 81 functional modules and more than 1700 screens and more than 2900 programs. In support of these information management areas, there are: Performance Engineering Services

5 Personal Productivity Tools In short, Ellipse is a very complex system so that so far there is no one who is able to comprehend the whole system solely. In every implementation project, there are expertises from major sub-systems work together, enabling successful go live of implementation.