/P „..P'-,- V 'PI Spring 1982

C5224: Open Book (and Open-Mind!) Take-Home Final

We offer five questions in this exam, ot which you should answer three. Each answer should about

five pages long. Some of the questions involve reading articles and commenting on them; the articleswill be available in JakeBrown's office (Room 226, Margaret Jacks Hall), or if you are in TV-land you

can call Jake at 497-4878 and she will mail them to you. The Handbook of Artificial Intelligence

provides information pertinent to the other questions.

Our goal for this exam is to provide you with an opportunity to think like an AI researcher. Thequestions we ask are, for the most part, difficult and unanswered in AI. By "unanswered" we mean

that no-one has offered an answer that is generally acknowledged as "the last word". Many of thesequestions have stimulated much discussion in AI, and we expect you to add your own thoughts to

these discussions. We are much more interested in your thoughts than in areiteration of the literature

of AI; after all, we alreadyknow the literature.

We are quite flexible about this exam. If there is a topic in AI that you would prefer to write about,

please write a one-page proposal for us to review. If it holds promise (and most questions in AI do) we

will let you know and you can go ahead. Please mail or give your proposal to Jake Brown, with your

name and telephone number, and we will call you within a day to discuss your plans. Once again, our

goal is to have you think like an AI researcher, so your paper should not simply be a literature review,but should offer your own thoughts and designs. Finally, the paper should be about fifteen pages

long.

Don't go into shock or panic because this exam is so hard. It was intended to be that way --- to giveycu some substantial food for thought.

Question 1. There are hundreds of people working in AI, thousands working in cognitive psychology,

but relatively few practicing cognitive science. With the exception of these few, AI researchers andcognitive psychologists are not on speaking terms. This puzzles many of us who believe that thecomputer is the ideal tool for modeling human intelligence. Some observations about this rift can be

found in Lawrence Millar's paper, called "Has artificial intelligencecontributed to an understanding of

the human mind? A critique of arguments for and against." Miller offers the view that AI and

psychology "use different criteria to evaluate scientific progress," and naturally raises the question

of what criteria one should use to evaluate theories of how the mind works, especially when the

theory is a computer program. This is the question that we want you to discuss. What is the role of

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"theorizing"? Is sufficiency an adequate criterion for a theory of the mind? If not, what other criteriaare there? What are the advantages and disadvantages of modeling thought with computerprograms? What other ways might you model thinking processes? What is the status in "scientificmethod" of the AI approach? You might read another paper by Allen Newell, called "Remarks on therelationship between artificial intelligence and cognitive psychology," in which Newell discusses eightroles that computer models of ttie mind play in cognitive psychology.

Question 2. One of the most striking of human mental abilities is reasoning by analogy. Our speechandwriting are full of metaphors. We often use analogies to explain new concepts, for example, short-term memory (STM) is analogous to a buffer. Also, we sometimes discover things by analogy, forexample, the analogybetween STM and a buffer leads us to ask what is the capacity of STM and whathappens when it is full. (The answer to the first question was one of the first dramatic discoveries ofinformation-processing psychology. As George Miller put it: "My problem is that I have beenpersecuted by an integer." The integer was, of course, 7.) In AI we recognize the importance ofanalogical reasoning but we have very few ideas about how to make our programs do it. For thisquestion, we want you to think of a handful of analogies, or of a task in which analogy plays animportant role. Then consider the twin problems of understanding your analogies and generatingthem. Which task seems easier; why do you think this is? What role does the concept of similarity playin analogical reasoning? What tools do we already have in AI for representing the knowledge that isused to generateand understand analogies? What tools do we lack? Consider the role of "context" inunderstanding and generating analogies.

Question 3. Everyone is an expert at something. Some of you play musical instruments, some aregreat storytellers, some rollerskate, or ski, or play darts. Some cook very well, or bake bread, or evenbrew beer. Chooseyour favorite area of expertise, and consider the task of building an expert system.

What will your system do? Is your task a good candidate for an expert system, given the current AItechnology? Why? What kind of representation would be especially appropriate for your knowledge?What kind of inference mechanism should you use to "drive" the program. As you know, expertsystems are often used as consultants to provide advice to non-experts. Give a fragment of a sampledialogue between your hypothetical expert system and its user.

Question 4. Expert systems are expert primarily because they use masses of knowledge. As youknow, knowledge is power! With a few exceptions, expertise has been force-fed to our programs; it isan arduous task to sit for dozens or hundreds of ?rhours with a human expert, understanding, andthen filtering the knowledge out of the conversation and encoding it as heuristic rules. Perhaps aprogram could learn for itself. Typically it learns by "rote," that is, we tell it everything it needs to

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know. Consider an expert system such as PROSPECTOR (pp. 155-162, Vol. II of the Handbook) ormaybe the system you designed for your own area of expertise in the previous question. Step throughthe actions of the program and think about how it could learn from the results of each of them. Thinkabout what kinds of things might be learned; for example, could PROSPECTOR learn for itself thatcertain combinations of minerals are especially common or rare? At what point in the consultationwould it learn this? What could it learn at other points in the consultation? What could it learn bykeeping track of previous consultations? What sort of facts about geologywould be easy to learn, andwhich would be hard, and why? You must consider the role of feedback from the user: What kind offeedback do you need for differentkinds of hypotheses about geology (or other domains.) You mightread about TEIRESIAS (pp. 87-101, Vol. II), a program which facilitated learning after a consultationwas completed.

Question 5. There is a traditional distinction between perception and cognition. Crudely put,perception was what one did with the five senses, and cognition was what one did with one's mind.But the distinction has been blurred by the discovery that a lot of "thinking" goes on in the senseorgans; for example, the retina seems designed to report "edges" in the visual field, and attention isfocused on edges automatically. This result pertains to human vision and is derived from humanphysiology; we want you to discuss the perception-cognition distinction as it applies to AI programs.Think about programs that interact with the physical world, such as speech understanding systems,vision systems, or robotic programs. These programs are just masses of computer instructions-canany part of the program be considered "perceptual" and not "cognitive," or vice-versa? How do'perception and cognition interact in these programs? Consider the functional differences betweenmodel-driven reasoning ("top-down" reasoning) and data-driven ("bottom-up" reasoning). Canthese work together in a program? How? Pay attention to how the programs control their processing.You might compare several programs to answer these questions.

This paper must be handed in or mailed in to Professor Feigenbaum by noon on Monday, June 7. Ifmailed, it must arrive in the Monday morning mail, your paper must be an individual effort, needlessto say. It is a violation of the Stanford Honor Code to do otherwise.

Please write on the paper (somewhere) whether you are taking the course Pass-Fail or for a lettergrade.

Think creatively, write crisply and clearly, and havefun with these difficult questions.