An undergraduate curriculum in expert systems design or knowledge engineering
By way of introduction, Chatham College is a small liberal arts college that has obtained a grant to implement a curriculum in artificial intelligence even with the minimal equipment and resources that we do possess.
We had been involved with preparing and teaching courses to familiarize undergraduate students with the field of artificial intelligence for several years. Last year we decided to develop a cluster of advanced courses that would turn out 'knowledge engineers'. There is a serious shortage of such skilled people in the Pittsburgh area. We planned for them to go immediately to Industry and begin working on expert systems, with a wide range of applications. These included but were not limited to diagnostic systems, financial modelling applications, and shop floor management. Under the terms of our funding we also encouraged students to become entrepreneurs by first developing and then marketing their own expert systems targeted for specialized markets.
Students selecting this upper level cluster have already completed our core computer science requirements and have a good math background. One of the key factors in this project has been that we worked closely with the business community seeking their input into the curriculum planning process. They also provided us with the internship opportunities we requested and gave the students exposure to a much wider range of equipment than we could afford to provide in house.
The program has been designed to run on IBM PC's with 512K. We purchased copies of Golden Common Lisp and Turbo Prolog as well as using an Altos with Xenix and C to get us started. We are currently negotiating for a grant for 2 Hewlett Packard artificial intelligence workstations with four terminals each. These cost about $50,000 each and are far beyond our price range. Two things are encouraging. Prices are falling very rapidly and the other is that NSF and other foundations are beginning to make instrumentation grants available to smaller institutions for the purchase of sophisticated equipment of this type. Our most difficult task has proven to be finding faculty to help teach these courses. We have had to draw on local industry to help with part time instructors
The whole cluster consists of 6 courses and a 6 credit internship. The first introductory course. Artificial Intelligence, involves an overview of the field. The history of the movement from its roots to its current popular status are explored. We based the course on Patrick Winston's text Artificial Intelligence. Each of the major research areas is explored. The approach taken was one of studying the classic toy problems. This made it possible to show the difficulties that have been encountered at a practical level and then to review progress in the field, while permitting students to gain actual programming experience. For example, in Natural Language we introduce the Eliza program as an early example of an attempt to use the keywords approach. Students are required to produce a modified conversation. We also look at the Turing test. Drawing from Rodger Schank's book The Cognitive Computer we go on to talk about scripts, using the Restaurant Script as an example. Students are asked to put together a travel script in teams of four. This section is wrapped up with a discussion of natural language programs today.
In the area of vision research, so critical in Robotics, we used the classic toy blocks problem, encouraging the students to key it in and modify it. We found that actually working with the code helped the students to gain a much more vivid appreciation of the difficulties involved. Winston has an excellent presentation on the subject. It was difficult to improve on it.
Having introduced two specialized problems in the field, we move the students into the challenge of finding a general problem solving technique. At this stage they have no difficulty in accepting the fact that no such algorithm can be developed and that it is rather a matter of determining the rules of thumb or heuristics governing the specific problem domain with a sufficiently large knowledge base to cover all likely contingencies and options.
To help to clarify this point, we delve into game playing strategies. Students are asked to write a tic tac toe game wherein the computer learns from its own mistakes. The first version they are asked to produce simply stores patterns of play in an array. The program searches for a pattern match. If one is found it will play the next move in the pattern string. If none is found a move is picked at random from those available. As it plays it keeps adding to the number of pattern strings it recognizes. The key element in this program is that the last byte in the pattern string contains a score for that particular pattern. The score for each winning pattern is incremented by one each time it is selected. Losing patterns have one subtracted from their score. Eventually when the score reaches zero that particular pattern will not be played again. The principle involved is that of reward and punishment. The patterns that win are reinforced by means of higher scores, while losing patterns are punished by having points taken away.
The next refinement students are asked to add in, is an algorithm that will assist the computer to pick a better move not at random but with a set of clearly specified rules. This has proven to be an interesting and challenging exercise for our students. It has provided them with a thorough backdrop for an in-depth discussion of game-playing strategies…brute force versus attempting to minimize the search path.
To help students to gain a better appreciation of the work being done, we also made use of many examples of industrial applications captured on videotape from the Texas Instruments satellite broadcasts done last year. The lectures by various prominent figures were used to stimulate discussion of the relative benefits of artificial intelligence applications and the new technologies available.
This leads very naturally to the final topic, the Japanese Challenge and 'our response to it, particularly in the development of new hardware. We look at the progress in VLSI chips and parallel processing that is making possible much larger and more sophisticated applications.
Obviously it is also important to give the students training in the languages of the field. We have developed a course that concentrates on Lisp and then follow it with a course in Prolog. I would like to have added several other of the artificial intelligence languages including Ops5, and Flavors but decided to wait until we obtained the artificial intelligence workstations in order to keep our software costs down to manageable levels.
The Lisp course, based on Golden Common Lisp, available for micros with 512k minimum, focuses on the structure of list processing languages in general and the use of primitive functions to build ever more complex functions to perform all sorts of information processing tasks on lists. It took a considerable amount of time to select the version of Lisp that would provide all the functions that we required for the course. While Golden Common Lisp was not as comprehensive as the version of Franz Lisp we had been running on a VAX mainframe, it did have enough features to support a first course.
During the course students have a number of small assignments and are required to write a variety of functions. Here the emphasis is on the mechanics of list processing languages. We usually finish up with two larger assignments. The first is an inheritance problem where students explore the use of properties and a search problem where they are asked to get the computer to determine the best route from one city to any other when several options are available. A variety of search strategies are proposed and then compared…breadth first, depth first, hill climbing. The text we used for this course was Robert Wilensky's Lispcraft.
Meanwhile, running concurrently, students have also been taking a course in Learning, Memory and Cognition, where they are studying in some depth human information processing and problem solving behavior. In our case, because we do not have the expertise in the Computer Science Department to handle this course it is being taught by the Psychology Department. The course is important because it provides a strong theoretical base upon which to build. If, in fact, we can develop computers that can think like humans and exercise judgment, then we must have a good working knowledge of how we do in fact perform these tasks ourselves.
A constant theme running through these courses is the concept that there is no one problem solving technique but rather that in each type of problem specialised knowledge is needed and in addition we need to have rules of thumb or heuristics to guide us when more than one option is available.
In the Prolog course students begin working with developing several small expert systems. We build a diagnostic system using the bicycle repair one as a model. Students are free to choose their own topic. They have ranged from diagnosing physical symptoms of illness to lawnmover repair. Here they are working with a very limited domain. As part of this course we discuss numerous examples of expert systems currently being used. Another assignment that students find quite challenging is the Monkey and Bananas toy problem, taken from Lee Brownston's book “Programming Expert Systems in Ops5: an introduction to rule based programming”. Here the problem domain is limited to a room with a number of objects in it. The monkey is presented with the problem of reaching the bananas no matter where they may be positioned in the room, even if they appear on the ceiling. Several other objects are in the room, including a ladder, a sofa. Students explore in some detail how to handle all the various sets of rules that are needed depending on the room layout and the actual location of the banan