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In Artificial Intelligence Department of Computer Science Stanford University Syllabus for Qualifying Exam in Artificial Intelligence Department of Computer Science Stanford University Spring 1980 The syllabus is organized to present a picture of the range of knowledge expected of Ph.D. candidates in Artificial Intelligence, rather than specifying a fixed list of readings. There are a number of different dimensions along which we could divide up the material. The attempt in the earlier version to establish a thorough categorization has been replaced this year with a less formal, more realistic organization. We have listed a number of "topics" with a short paragraph describing the necessary reading for each. These topics overlap in various ways, and reflect idiosyncratic views of how things divide up without any attempt to provide a consistent classification. Hopefully the set of references included with each item will make it possible for students to select a reasonable number of readings which will fill any knowledge gaps. The long reading list is intended as a source of details on individual references, not as a necessary set of things to read. It includes a rough indication of what sort of understanding is most important for each reference whether there is a general perspective, one or more specific concepts, and/or—a body of detail with which students are expected to be fluent. These indications are of course based on the prejudices and peculiarities of the committee making up the syllabus, and should not be taken as representing the views of anyone else (including the members of individual exam committees). Please send any comments or suggestions on the syllabus to Doug Lenat (LENATgSUMEX) . We are hoping to get lots of feedback, and continue building toward a syllabus which really describes what there is to know [and what is important to know] about AI , to be built on year after year. Stanford AI Syllabus 1980 Page 1 1 Mechanics of the Examination The examination will be an individually-scheduled oral, before a committee consisting of three members, chosen from the faculty, adjunct faculty, senior research staff, and possibly appropriate senior researchers from AI facilities in the area like SRI and Xerox PARC. Each committee will include at least one faculty member and at least one person in a potential area of specialization of the candidate. The candidate can request a particular person in his or her area, but the Qual committee has final choice of examining committees. We plan to hold the examinations during the first two weeks of June. If there are special reasons why someone cannot take it at that time, we will try to make other arrangements. At least two weeks before a student's examination, he or she will be given either: (i) a problem to be worked on an open-book basis; or (ii) a research paper on which he or she will write a critique. Those students who prepared papers for the qual last year, but did not pass the oral exam can use the same papers as the basis for this year's exam if they wish. The papers are to be handed in to the qual committee (Doug Lenat or Terry Winograd) no later than one week before the exam. The members of the individual exam committee will be given copies of the solution or paper, and the first section of the exam (up to an hour) will center around issues raised by the work done. The rest of the exam will be on any questions the examiners consider appropriate. The purpose of the exam is to demonstrate that the student has done sufficient reading and thinking to fit his or her individual research into a perspective of other work in AI . This includes detailed knowledge of some other existing work, both in the sub-area in which the student intends to do research, and in other sub- areas. The committee should be satisfied that the student already has a sufficient grasp of both the general issues and of a reasonable amount of technical detail. It would be unwise to assume that it is only necessary to know a subset of the topics listed below by matching them to the individual examiners. The purpose of the exam is to look for bredth, not for conformity to the particular committee, and questions from all areas are fair game. The exam is not intended as a device to decide who will and will not be able to continue in the program, but rather a way of focussing effort on a comprehensive study of AI , and a way of providing students with specific diagnostics for gaps in their knowledge or understanding. The possible outcomes of the exam are: Pass unconditionally: The student has a satisfactory knowledge of all areas. Pass conditionally: The student has some lacks which can be made up by directed work, such as the completion of specific course(s) or specific research project (s). The committee will set both the scope of the work and a time period in which it must be completed in order for the examination to count as passed. Continuation of the examination: The student has a lack which demands a moderate amount of further study in one or more areas, and a second oral examination (with the same committee) will be scheduled within the next two quarters. Defer: The student has significant gaps in knowledge which cannot be made up by limited correctives. Candidate is required to take Stanford AI Syllabus 1980 Page 2 the exam another time it is offered, under whatever system is in effect then. 1.1 Topics to be studied As mentioned above, this is not intended as a complete or structured classification. It is a list of answers to the vague question "What kinds of things should AI students know about?". There is no significance to the ordering. General Perspective Weak Methods Epistemological problems of AI and the use of formal logic Knowledge Engineering - Expert Systems Knowledge Representation Formalisms Game Playing Planning and Common-sense Reasoning Mathematical theorem proving and discovery Natural Language Speech understanding Vision Physical manipulation Automatic Programming and Program Verification Learning and Inductive Inference Psychological Models Automata and Formal Language Theory Programming Languages for AI Philosophical Implications Political and Social Implications History and politics of the field 1.2 General Perspective There are several recent books on AI which attempt to provide an overview. Of these, [Boden Al] and [Winston Al] are the best starting point. One recent book ([McCorduck Al]) detailes the early history and sociology of the field. Shorter articles which provide long-term perspective are [Minsky STEPS], [Feigenbaum IFIP], [Lenat UEIQ], and [Nilsson OVERVIEW]. In addition, the AI handbook will provide an overview of lots of AI issues. [Nilsson Al2] is now available, and its novel organization cuts across most of the categories on our list above. 1 .3 Weak Methods Classicaly, AI has been associated with a set of methods for problem solving and search which have been called "weak methods". These include early work such as GPS [Newell, Shaw, and Simon], notions of heuristic search as described in [Nilsson Al] and [Handbook SEARCH], and more theoretical ideas abot problem spaces discussed in [Newell ILL] and at length in [Newell 4 Simon HPS]. Stanford AI Syllabus 1980 Page 3 Students are expected to be familiar enough with the technical details to demonstrate how the techniques operate, but will not be asked to prove theorems or remember complex results. 1 .4 Epistemological problems of AI and the use of formal logic The problem of describing facts about the world including the effects of actions has been studied apart from specific problem solving programs. This work has used first order logic to express facts about the world. These issues are discussed in [McCarthy and Hayes] and in [Hayes DEFENCE], Many current issues are discussed in [McCarthy SIJCAI], A new approach is described in [Weyrauch PROLEGOMENA]. Truth maintenance is covered by de Kleer, Doyle, and others in [Winston 4 Brown], The relevance of theorem proving to problem solving is discussed in [Green TP], and the techniques of resolution theorem proving are described in [Nilsson Al]. As with weak methods, students are expected to be familiar with the basic mechanisms (e.g. be able to demonstrate a simple proof by resolution, or explain the issues in unification) but will not be required to know sophisticated technical results (e.g. prove the completeness of resolution with the X heuristic). A tutorial on some of the relevant mathematics is in [Manna MTC]. 1 .5 Knowledge Engineering - Expert Systems Much of current AI work is being subsumed under the heading of "knowledge engineering". [Bernstein KBS] is a general survey of knowledge based systems. [Winston Al] gives a general idea of systems of this kind done at MIT, and [Feigenbaum IJCAIS] describes the general approach. A number of expert systems have been built in the past few years. Students should be familiar with the general capabilities and design. [Handbook APPLICATIONS] provides some details (and many pointers) for most of the recent efforts. Applications to Music [Moorer][Zaripov] and Art [Gips][Cohen IJCAI6] should also be looked over. The MIT perspective is covered in [Winston & Brown], V.l, Sec.l. 1.6 Knowledge Representation Formalisms A number of current AI research projects are centered around the development of knowledge representation languages. Some of the early issues in representation are discussed in [Amarel ACTIONS] and [Bobrow DIMENSIONS], More recent discussions include [Winograd FRAME], [Hayes DEFENCE], and [Winograd EXTENDED]. Students should be familiar with at least the following general approaches: Procedural Embedding, Semantic Networks, Conceptual Dependency, Frames (Scripts, etc.), Production Systems, and Description Languages. These are treated well in [Handbook REPRESENTATION], Stanford AI Syllabus 1980 Page 4 1 .7 Game Playing One of the earliest and most publicized areas of AI research has been game i playing programs, such as those for checkers [Samuel in C4T] and Chess [Greenblatt FJCC].
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