
         
         
         








                                     ESIE


                       The Expert System Inference Engine


                                    History

















         Lightwave Consultants                            August 1985
         P.O. Box 290539
         Tampa, FL  33617







                      Copyright 1985, All Rights Reserved.

         The ESIE distribution diskette, of which this history is one 
         file, may be freely copied and distributed.  Printed copies 
         of this history, or this history without the rest of the 
         files on the distribution diskette, may not be copied or 
         reproduced in any form. 


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                               Table of Contents 


         Introduction  . . . . . . . . . . . . . . . . . . . . . .  3 
         
         Before the 20th Century . . . . . . . . . . . . . . . . .  4 
         
         1900 to 1940  . . . . . . . . . . . . . . . . . . . . . .  6 
         
         The 40s . . . . . . . . . . . . . . . . . . . . . . . . .  7 

         The 50s . . . . . . . . . . . . . . . . . . . . . . . . .  8 

         The 60s . . . . . . . . . . . . . . . . . . . . . . . . .  9 

         The 70s . . . . . . . . . . . . . . . . . . . . . . . . . 10 

         The 80s . . . . . . . . . . . . . . . . . . . . . . . . . 12 
         
         Bibliography  . . . . . . . . . . . . . . . . . . . . . . 15 
































                                                                Page 3


                                  Introduction 


         ESIE (pronounced "easy") is the acronym for Expert System 
         Inference Engine.  ESIE is, according to many people working 
         in Artificial Intelligence (AI), an "expert system shell." 

         ESIE is a fast, powerful, inexpensive tool for work in 
         Artificial Intelligence.  If you would like to know more 
         about ESIE, please print and read the file MANUAL. 
         
         This history is designed to give you a brief "run down" on 
         the past of Artificial Intelligence.  While the history of AI 
         is not exactly as exciting as Napolean at Waterloo, I hope 
         you will find it interesting. 
         
         Those of you who are interested in the socioeconomic impacts 
         of AI may well be excited, perhaps worried, about the 
         direction and potential of AI.  For example, a few science 
         fiction authors have claimed that man's purpose on earth is 
         to BUILD a better race that eventually will become the 
         dominant one. 
         
         I take a different outlook.  Man was meant to be and do great 
         things and we need to build great tools to help us do it.  
         After all, if we never invented the spear we would still be 
         wearing animal pelts.  I'm sure when the spear was first 
         invented, other members of the tribe had serious misgivings 
         about it and warned the young ones to do things the old way. 
         
         I certainly am not claiming that the road to the successful 
         use of Artificial Intelligence will be an easy one, (there 
         must have been more than one caveman who stabbed himself in 
         the foot with his new spear), but it can be one that provides 
         numerous benefits.  The coming of any new technology has 
         always brought on some problems; the successful ones cure far 
         more than they hurt. 
         
         Hopefully, you will get more and more interested in AI, and 
         meet as many knowledge engineers (KEs) as you can.  One of 
         the nicest, and most consistent things, about KEs is our 
         nearly universal desire to talk and think about the future.  
         A conversation with a KE at KE social hour can be 
         invigorating. 
         
         It is my belief that Artificial Intelligence has real promise 
         to be an important tool in the ascent of man. 
         




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                            Before the 20th Century 
                                
         
         The astute reader may well be wondering what this chapter is 
         doing here.  Logically, didn't AI start with advent of the 
         computer?  Well, the answer is sort of. 
         
         The IDEA of objects having human qualities has been around 
         probably as long as man has.  When Mr. Ug first missed his 
         prey with his new found weapon, say the bow and arrow, he 
         might have thought, "it would be nice if the arrow could find 
         it's own way to the food."  There is evidence that certain 
         groups of ancient man thought their weapons had souls and 
         should be appeased before the hunt.  While not quite fitting 
         in with a modern definition of AI these were definite 
         feelings toward Artificial Intelligence. 
         
         Real work towards defining the mathematics and symbolics 
         behind AI can be thought of as beginning with Charles Babbage 
         in the 19th century.  Babbage was fascinated with the idea of 
         building machines to do human tasks, and the mathematics that 
         would be required to do such tasks.  Babbage was, of course, 
         a mathematician. 
         
         Theory that was developed during his period is still used and 
         debated today.  The Tower of Babel is standard fare in 
         beginning computer science courses.  In the Tower of Babel 
         you have three stakes in the ground and around one stake you 
         have donut-shaped pieces.  The pieces get consecutively 
         larger in size: 
         
         
                    |                     |                 |
                   x|x                    |                 |
                  yy|yy                   |                 |
                 zzz|zzz                  |                 |
                aaaa|aaaa                 |                 |
               bbbbb|bbbbb                |                 |
              cccccc|cccccc               |                 |
             ddddddd|ddddddd              |                 |
            eeeeeeee|eeeeeeee             |                 |
         
         The object of the puzzle was to move all of the pieces from 
         the starting stake to any other stake with these rules:  you 
         may move only one piece at a time, and no piece may have a 
         larger piece on top of it.  In a child's toybox is often 
         found the "stake" with the pieces around it, and a couple of 
         wine bottles will suffice for the empty stakes, it you want 
         to try to solve the puzzle.  It's not as easy as it sounds. 
         
         Puzzles such as these, and the human intelligence applied to 
         solve them, were fascinating to Babbage.  He theorized that 
                                                                Page 5
         you could apply the rigors of mathematics to the mental 
         process so that one common language could be used to transfer 
         that process from man to machine. 
         


















































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                                  1900 to 1940 
                     
         
         This period saw the development of a formalism for computers 
         and Artificial Intelligence.  In the early history of 
         computers the two were almost always talked about together - 
         they were inseparable.  The goal was to create machines that 
         acted like humans or performed human functions so that humans 
         would no longer have to perform them. 
         
         The early pioneers in the U.S. were George Stibitz, Howard 
         Aiken, Presper Eckert, John Mauchley, John Von Neuman, Herman 
         Goldstine,  and Julian Bigelow. 
         
         In Britain, Alan Turing contributed substantially to AI and 
         computer science.  Nearly every computer in existence today 
         is based on the Turing model. 
         
         If you've had some coursework in computers, one or more of 
         the above names should sound familiar.  They are the fathers 
         of computers, and in a way, the fathers of Artificial 
         Intelligence.  For them, the two were one and the same. 
         





























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                                    The 40s 
                 
         
         Computers during the Forties left a lot to be desired.  They 
         were used to do real work for the first time, during World 
         War II, to help artillery batteries better aim their 
         projectiles.  After the war, the concentration changed: since 
         computers could handle numbers well, shouldn't they handle 
         symbols well?  During the Forties, much effort was expended 
         to get the computer to work with symbols the same way it 
         worked with numbers. 
         
         Many attempts were failures, but some successes drove the 
         fire towards building machines that could work with symbols 
         and therefore be one more step closer to thinking. 

         For an interesting book you might want to pick up and read 
         "Cybernetics - Control and Communication in the Animal and 
         the Machine", by Norbert Weiner.  The book was published in 
         1948. 
         































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                                    The 50s 
                 
         
         The Fifties saw work begin in earnest on the thinking machine 
         - a computer that would reason as a human reasoned.  Four of 
         the major institutions involved during the 50s were:  
         Stanford, RAND, Carnegie-Mellon, and MIT. 
         
         In 1956 John McCarthy held a conference on Artificial 
         Intelligence at Dartmouth.  At this conference were, among 
         others, Herbert Simon, Marvin Minsky, Alan Newell, Claude 
         Shannon, and Arthur Samuel.  All of these people are 
         considered among the fathers of AI. 

         DARPA was also very interested in human reasoning in the 
         Fifties.  Often it was claimed that building expert systems 
         would show the true value of computers to man.  An expert KB 
         feasibility study was conducted by DARPA in the late Fifties. 
         The study was labelled MODAPS.  MODAPS was eventually built 
         into a usable system for the U.S. Army called A-VIS.  A-VIS' 
         main goal was for maintenance of hardware and software on 
         computers of the day.  Much funding for AI work came out of 
         DARPA. 
         
         Three universities in the U.S. took on the leading roles in 
         AI research: Carnegie-Mellon, MIT, and Stanford.  Four 
         universities in Britain took on the leading role of AI there: 
         Edinburgh, Sussex, Essex, and Imperial College.  Donald 
         Michie, H. C. Longuet-Higgines, R. A. Brooker, and R. 
         Kowalski are all important people in British AI. 
         
         Stimulated by the impressive gains these people made towards 
         intelligent machines, the press, and the people, overreacted.  
         Science fiction stories exploded on the scene about the 
         power, and danger, of intelligent machines.  Everyone, it 
         seemed, was concerned about the impact of thinking machines 
         on their lives.  Surprisingly, the overwhelming attitude was 
         positive - people were pro machine.  This was due to the 
         promise of less labor and more free time, along with greater 
         prosperity. 
         











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                                    The 60s 
                 
         
         The Sixties can be classified in Artificial Intelligence by 
         the lack of it.  During the Fifties, and somewhat during the 
         postwar period, fantastic and glamourous claims were made for 
         thinking computers.  Computers, it was said, would soon solve 
         all our problems by thinking and reasoning and performing 
         like humans.  We would use them to find all the tough answers 
         and build machines that would do all our dirty work for us. 
         
         The let down from these claims produced the dismal lack of AI 
         in the Sixties.  Research was left to a few universities: 
         MIT, Carnegie-Mellon ,and Stanford.  
         
         The work at MIT centered on building machines to play the 
         perfect game of chess.  Researchers reasoned that if they 
         could build a machine that played perfect chess, then they 
         could use the same techniques to build a machine to mimic any 
         human behavior.  Toward the end of the Sixties they realized 
         that building a computer to play perfect chess gave you a 
         computer that played perfect chess, and that's all. 
         
         They had trouble using the same techniques for chess playing 
         in other fields, athough concepts were gained that have been 
         applied successfully in many AI applications.  Also, no chess 
         playing computer has ever been capable of consistently 
         beating the masters of the game. 
         























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                                    The 70s 
                 
         
         In the Seventies came the push to "try something practical" 
         in Artificial Intelligence.  The goal then became to define 
         very limited domains that AI could be applied to TODAY to 
         solve real world problems.  This decision changed the course 
         of AI and is the reason you hear so much about AI today.  The 
         two types of AI focused on were expert systems and natural 
         language processors. 

         In 1970 there were only 65,000 computers in the United 
         States, (In 1984 there were over 5 million), and the rapid 
         "computerization" of America has helped AI. 
         
         In 1972, SHRDLU made headlines (at least among the AI 
         researchers) by using semantic networks for natural language 
         processing.  SHRDLU was roughly diviable into three parts: 
         the first part analyzed the text to get at the intent of the 
         user's input, a semantic processor to get at the meaning of 
         words, and a logic segment to implement the user's requests. 
         
         SHRDLU functioned with a fairly limited domain: the blocks 
         world.  In SHRDLU's world there were only blocks, and the 
         only thing SHRDLU could do was move these blocks around on a 
         screen.  The method behind this movement was what was unique.  
         The first part of SHRDLU, now called an augmented transition 
         network (ATN), where SHRDLU tried to solve for the intent of 
         user's request, was unique. 
         
         In 1975 MARGIE was created by Roger Schank, with the model of 
         conceptual dependency (CD) in mind.  In CD, the researcher is 
         intent on using work done by linguists and psychologists to 
         build human language understanding into machines.  In MARGIE 
         an input would be analyzed into the most minimal components, 
         where it could be operated on.  MARGIE had two main operating 
         modes, in one it would paraphrase your input, for example: 
         
              Bob asked Mary out. 
         
         might become: 
         
              Bob requested that Mary go on a date with him. 
         
         And MARGIE's other mode, where it would make inferences 
         concerning the input.  Inferencing became one of the most 
         important aspects of MARGIE, even though it was intended for 
         natural language processing. 
         
         In 1977 another breakthrough occurred in natural language 
         processing:  GUS.  As natural language processors became 
         larger and took on additional capabilities, the size of the 
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         semantic network, the network that models human language, 
         became extraordinarily large.  In order to handle such large 
         amounts of data a system would have to break the information 
         up into digestable chunks.  GUS demonstrated that you could 
         break this data up and still be effective. 
         
         GUS used a coding scheme called frames.  Frames are used to 
         group nodes in the semantic network into groups that are 
         similar.  GUS was also one of the first natural language 
         systems to work against a data base; GUS was used as an 
         advisor to passengers flying in California.  The data base 
         was a part of the Official Airline Guide and GUS answered 
         questions against this data base.  Most natural language 
         processors sold commercially today are designed specifically 
         to answer questions from an existing data base. 







































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                                    The 80s 
         
         
         The Eighties have brought an explosion into the computer 
         field and a corresponding explosion in Artificial 
         Intelligence.  This has occurred for three reasons: 1)  there 
         finally is enough computer power, and advanced software, for 
         AI to be useful in real time, 2) there are plenty of 
         computers and computer professionals to spend time and money 
         accomplishing more than the simple computer tasks, and 3) 
         industry has taken notice of AI and moved it from the 
         laboratory into the field, along with additional financial 
         arrangements. 
         
         In 1983 another step up in natural language processing 
         occured with IPP.  With IPP the frame used in other natural 
         language processors became a dynamic scheme.  Frames could be 
         moved, deleted, changed, updated, and added with relative 
         ease.  This made creation and maintenance of the semantic 
         network easier and quicker.  IPP could build new structures 
         if it encountered information that was new to it, and these 
         new structures were fully compatible with, and enhanced, the 
         old structures. 
         
         Many other countries are involved in AI besides the United 
         States.  Much press has been devoted to the Japanese 5th 
         Generation Computer, which is AI of a high form.  However, 
         many other countries, most of them western, are also involved 
         in AI. 
         
         Canada, for example, has a very impressive AI program, 
         although most of its work is done in the University 
         laboratory.  I expect that Canadian work will soon leave the 
         lab and advance into the marketplace and attract significant 
         financing with it. 
         
         In 1983 several interesting developments came out of Canadian 
         laboratories concerning machine vision.  Mackworth and Havens 
         are working towards several schema for scene interpretation 
         (putting into words what the camera sees), map understanding, 
         and remote sensing. 
         
         Other major work in Canada involves natural language 
         processing, knowledge representation, and expert systems. 
         
         The United Kingdom has been active in AI almost certainly 
         from its inception. 
         
         One fact that may be suprising is that Japan is the largest 
         user of industrial robots in the world.  Not of robots per 
         person or per corporation, but Japan has more robots in 
         employment than anywhere else in the world, including the 
                                                               Page 13
         United States.  Japan uses well over 60,000 industrial 
         robots, and some estimates place the tiny Asian country as 
         having as many robots in use as North America and Western 
         Europe combined. 
         
         In Japan the concentration, as far as robotics are concerned, 
         is on sensing and control: they have successfully made a 
         robot that can shake your hand firmly but gently.  
         
         Britain, France, Germany, Italy, The Netherlands, Belgium, 
         Sweden, and Spain all have active AI laboratories. 
         
         The Artificial Intelligence Laboratory at Linkoping 
         University, Sweden is concentrating on knowledge 
         representation, problem solving, and natural language 
         communication. 
         
         The Kaiserslautern University, Germany, is working on the 
         theory behind expert systems, and how to build them. 
         
         Prolog, which the Japanese have taken as their language of 
         choice for the fifth generation computer, was originally 
         built in France.  Prolog is a logic programming language, and 
         was built by A. Colmerauer.  Later, Prolog was enhanced by R. 
         Kowalski of Britain. 
         
         At the Louvain La Neuve, Belgium, techniques for knowledge 
         base pruning have been developed.  Since knowledge bases can 
         become very large as information is added to them, several 
         algorithms have been designed over the years to eliminate 
         large sections of the knowledge base as the consultation 
         proceeds.  The problem with pruning is that you might miss 
         some knowledge you need.  In Belgium, they are working 
         towards the best of both worlds. 
         
         At the Research Institute of Applied Computer Science, 
         Budapest, a computer language called Lobo has been developed.  
         This language offers many of the advantages of Prolog, while 
         keeping the advantages of a standard computer language, such 
         as speed. 
         
         At the Telecommunication Laboratory and Study Center, Turin, 
         Prolog programs have been written to analyze the concurrent 
         communications that occur in telephone operations. 

         If you look at every AI system in existence today you might 
         well exclaim that the humanoid robot of science fiction and 
         'Hal' of 2001 are just around the corner. 
         
         There are systems that can perform very delicate sensor-motor 
         tasks such as assembly of complex automobile structures - as 
         long as the parts are all laid out in their correct 
         positions, hold very impressive conversations, win nearly 
         every time in certain games, advise doctors better than the 
                                                               Page 14
         doctors themselves, identify and select objects from a bin 
         based on "looking" at them - using three dimensions, and a 
         host of other successful applications. 
         
         However, we are a long way from building 'Hal' or a humanoid 
         robot.  In not one single area of AI have we even come close 
         to approximating human behavior or capabilities.  In some, 
         heavily restricted domains, with well defined parameters, the 
         AI system CAN occasionally surpass the human in accomplishing 
         the same task.  This is primarily for two reasons: 1) humans 
         get bored.  We become lax in the attention needed to 
         accomplish a task, and AI systems never get bored.  2) AI 
         systems also never forget, once they are taught to do a task, 
         and if the environment in which that task is performed does 
         not change, they will continue to perform that task correctly 
         forever.  A human might, over a period of time, forget how to 
         do some part of a task. 
         
         Another reason we are years from building "science fiction 
         systems" is the problem of integration.  We can build a 
         system to "see" parts on a conveyor built, and a system to 
         build automobile assemblies, but to build a system that can 
         "see" to select parts then build an automobile assembly from 
         them is another thing. 
         
         What is needed is an influx of AI researchers and experts, 
         willing to spend the time needed to tackle complex problems, 
         and the creation of tools that are inexpensive yet fast and 
         powerful.  It is my hope that ESIE will make the road a 
         little easier. 
         























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                                  Bibliography 
                     
         
         1.  Dr. Herbert Simon, "AI - The Realty and the Promise"; in 
         his lecture at "Artificial Intelligence -- Opportunities and 
          Limitations in the 80's", Miami, Florida; November 7, 1984. 
         
         AI Intelligence Report; Sendero; Phoenix, Arizona; April, 
         1985. 

         AI Magazine; American Association for Artificial 
         Intelligence; Menlo Park, CA; Fall 1985. 
         
         AI Magazine; American Association for Artificial 
         Intelligence; Menlo Park, CA; Winter 1985. 
         
         Applied Artificial Intelligence Reporter; University of 
         Miami; Miami, Florida; October 1984. 
         
         Artificial Intelligence in Canada: A Review; by Gordon 
         McCalla and Nick Cercone; AI Magazine; American Association 
         of Artificial Intelligence; Menlo Park, CA; Winter 1985. 
         
         Executive Briefing Artificial Intelligence; Longman Crown; 
         Reston, Virginia; 1984. 

         The First Conference on Artificial Intelligence Applications; 
         Sponsered by the IEEE Computer Society; Denver; Decemeber 
         5-7, 1984. 
         
         Intelligence, Artificial and Otherwise; by William M. Chance; 
         Campus Report; Stanford University; Stanford, CA; April 27, 
         1984. 
         
         Physical Object Representation and Generalization:  A Survey 
         of Programs for Semantic-Based Natural Language Processing;  
         by Kenneth Wasserman; AI Magazine; American Association for 
         Artificial Intelligence; Menlo Park, CA; Winter 1985. 
         
         Proceedings:  AI - Opportunities and Limitations in the 80's; 
         ICS Research Institute; University of Miami; Miami, Florida; 
         November 7, 1984. 
         
         Proceedings:  IEEE Workshop on Principles of Knowledge-based 
         Systems; Sponsored by the IEEE Computer Society; Denver; 
         December 3-4, 1984. 
         
         Worldwide Artificial Intelligence and Computer Science; by 
         Dr. Jacob F. Blackburn; Proceedings: AI - Opportunities and 
         Limitations in the 80's; ICS Research Institute; University 
         of Miami; Miami, Florida; November 7, 1984. 

