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ESIE The Expert System Inference Engine Novice Guide Lightwave July 1986 P.O. Box 16858 Tampa, FL 33617 Copyright 1986 All Rights Reserved. This manual may only be distributed as one file on the ESIE distribution diskette. Such duplication and distribution is authorized without compensation as long as the diskette is a duplicate of the ESIE distribution diskette. This manual may also be distributed in printed form as long as a copy of the distribution diskette is attached. All other distribution is strictly prohibited. Page 1 Table of Contents Introduction . . . . . . . . . . . . . . . . . . . . . . 2 What is Artificial Intelligence? . . . . . . . . . . . . 3 Types of Artificial Intelligence . . . . . . . . . . . . 4 Some Miscellaneous Terms . . . . . . . . . . . . . . . . 6 Where to Turn For More Information . . . . . . . . . . . 7 Page 2 Introduction This manual is designed for the person who has absolutely no experience in the field of Artificial Intelligence (AI). This manual is intended to give you a working knowledge of Artificial Intelligence. When you have completed this manual you should print and read the file MANUAL, which gives all the details concerning ESIE. We assume that you have no experience in AI whatsoever, but we also assume that you are familiar with your computer. Neither this manual, nor any file distributed with ESIE, will try to educate you concerning your equipment. However, operation of ESIE is very straightforward and you need only know the rudiments. Page 3 What is Artificial Intelligence? Now there is a good question. Whenever a bunch of knowledge engineers (KEs) get together for social hour this subject has a way of rubbing us the wrong way. In short, not even we can decide on a good definition of AI. Here is the currently "accepted" view: Artificial Intelligence (AI) is any action performed by a machine that is normally performed by a human. We at Lightwave have a little trouble with this definition. First, an argument can be struck (and has been - believe us), that ALL computer software fits this definition as any task the computer does would probably require intelligence if a person were doing the same thing. Second, if you define intelligence to mean the ability to remember facts than all computers have a 200+ IQ. Third, while we may consider things like machine vision to be wonderful, they do not necessarily mean intelligence. Our definition of AI is a little bit different. Although our definition is not the universally accepted one, we think we have heard more than one (at least two) KEs voice their approval: Artificial Intelligence occurs when a man-made system gathers data and makes a recommendation. You notice that we allow other systems besides computers to be considered. The recommendation part is key. A system that simply catalogues or computes is not AI under our definition. Also, an AI system does not have to make a recommendation to a human, it can be to another system. Well, enough of that. For a working definition of AI, at least until the tide changes, consider it as any activity that a computer does that is human-like. Page 4 Types of Artificial Intelligence If you could isolate every type of activity a human does, then you probably have found all the different types of AI. These are some of the most active ones in existence today: Natural Language (NL) is the field where researchers are trying to get machines to understand natural English, or some other human language. It is not as easy as you might think. For a widely known example, the sentence "the spirit is willing but the flesh is weak", was translated into "the wine is agreeable but the meat is spoiled" by an NL program. Machine Vision (MV) is the area where researchers are trying to get machines to see. Some of this work can be the most fascinating research in the world. We recommend it if you are in the mood for a career change. Past research has been concentrating on understanding how humans see and trying to duplicate that process in machines. Now, we believe, the trend is to use the special capabilities of the machine, such as infrared detection and laser range finding in order to give the machine vision. Expert system (ES) research is the area that ESIE fits in. It is involved with making the machine reason and think like a human. Although no current ES has creativity, we're sure it will be classified as an ES when discovered. Nearly all, if not all, ESs in existence today run on the knowledge base principle. In this principle, the actual knowledge, or expertise, is stored in some file called the knowledge base. An Expert System Shell (ESS) is used to interpret that expertise and become an instant consultant. Expert systems are also receiving the most recognition from industry as it has advanced enough to be useful. Machine Listening (ML) works with the area of synthetic speech, speech recognition and speech synthesis. This is fascinating work, and some real breakthroughs are expected very soon. Robotic research is primarily in the area of motor control, tactile processing, and location determination. Robotic companies are increasing their sphere, however, and we are sure they will soon be using all the capabilities of AI. There are two typical applications in existence today where expert systems are used. One is the area of decision tree applications and the other is in the area of data query. In decision tree systems a knowledge base is coded into a Page 5 file to provide human expertise where it might not be available or to aid an expert with a second opinion for better quality. A good example of decision tree systems are medical systems. In these, a typical first question might be: "Does the patient have a fever?". Based on the answer to that question an entire area of illnesses will be eliminated, and the next level of the tree will be selected. Many knowledge bases today are built to run in the same "inference engine." These inference engines can use the rules in a multitude of different KBs, so they are useful in many tasks. An inference engine is the computer program that captures the raw inferencing power of the human mind. It uses the knowlege in the KB to act with human reasoning capability. The other type of knowledge based system is the data query system. In these systems, there is typically a natural language front end. This front end allows the end user to query a data base in natural English commands. The system has to solve for what the end user really wants and then obtain and manipulate the appropriate data. An example of this type of system is where the question might be asked: "How many French speaking employess do I have?" A third form of expert, or knowledge based, system involves the process. This type of ES has been largely ignored by the AI community, but is starting to get large press in AI journals and papers. It is receiving notice in corporations due to its low cost and quick payback period. A process based system is trying to assist a human in completing some series of tasks. The goal in process based systems (PBS) is to automate that terribly lengthy, hard-to-read, impossible-to-use manual or intruction set. The interactive capability of PBSs are one of their nice features. ESIE can be used to build a PBS, but its main intent is for decision trees, or in the mainstay of AI thinking today. In fact, when one mentions Expert System, the usual mental picture is that of a decision tree system. Page 6 Some Miscellaneous Terms Expert System A computer system that has knowledge, coded as rules, separate from the programs that interpret those rules. These rules form some type of expertise on a subject, therefore the expert system. Inference Engine The raw brainpower of an Expert System. The inference engine is synonymous with the brain of a child or one with amnesia. The ability to think and reason is present, but the facts to think and reason with are not. Also called an Expert System Shell. Knowledge Base A computer file that contains the knowledge that an inference engine can work with. This knowledge often comes in the form of "if-then" constructs. Such constructs are called production rules, as they produce some type of result when used in an inference engine. Meta Knowledge Knowledge that takes novel or new thought to use and implement. This knowledge is often knowledge that a person would use that is not in the domain of a particular problem. For example, a mechanic's knowledge that a paper clip holds things together can be used to repair an automobile. Declarative Knowledge Knowledge that can be easily expressed as production rules. Expert Systems are experts at using this type of knowledge. Page 7 Where to Turn For More Information Your first place to turn to for help should be our technical staff here at Lightwave. There is no question too tough for us to answer. If we do not have the answer right here right now then we will get it for you. However, our technical support is not free, and you will have to pay for such support beforehand. Below are a few other places you can turn to for information and help: In publications: IEEE Expert IEEE Computer Society 10662 Los Vaqueros Circle Los Alamitos, CA 90720 (714)821-8380 AI Magazine American Association for Artificial Intelligence 445 Burgess Drive Menlo Park, CA 94025 (415)328-3123 Additional public domain software or shareware: PC Softwave Interest Group 1030 E. Duane Suite J Sunnyvale, CA 94086 (408)730-9291 PC-SIG charges just enough to cover their operating costs, which is more than reasonable.