Syllabus for Qualifying Exam in

Department of 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]. Students should be familiar with the basic techniques (e.g. Minimax and alpha-beta) and some of the more subtle problems (e.g. the horizon effect [Berliner IJCAI3] and the use of patterns [Simon 1973]). Berliner's articles in IJCAIS and IJCAI6 also merit attention. Basic techniaues are taught in [Nilsson Al] and [Slagle Al].

1.8 Planning and Common-sense Reasoning

Much of the AI work related to robotics dealt with the planning of action sequences. GPS [Newell, Shaw and Simon] was the early classic, and other well known systems are STRIPS [Fikes, Hart, Nilsson] and NOAH [Sacerdoti NONLINEAR]. An early discussion of the problems is in [McCarthy ADVICE-TAKER] .

1 .9 Mathematical theorem proving and discovery

Mathematics has always been an important domain for AI . The Geometry theorem prover [Gelernter] was an early program. More recent efforts in mathematical theorem proving are discussed in [Bledsoe MAN-MACHINE]. The mechanization of mathematical discovery is discussed in [Lenat IJCAIS]. Non- Computer Science treatments of some interest are [Polya] and [Lakatos].

1.10 Natural Language

Much of the research on natural language is summarized in the handbook articles [Handbook NL] and the drafts of [Winograd LANGUAGE]. Students should be aware of the general content of these and familiar with the issues which arise in parsing and reasoning with natural language. This includes a level of understanding of parsing techniques (CFG, ATN , TG, etc.) similar to that of heuristic search and theorem proving discussed in sections above. Early work in NL is described in [Simmons SURVEY 1965] [Simmons SURVEY 1970], [Minsky SIP (browse)], and [Weizenbaum ELIZA]. [Handbook NL] gives a summary of work in Machine Translation. Many papers reflecting current research interests are found in [TINLAP] 1 and 2.

1.11 Speech understanding

Work in Speech systems is well summarized in [Handbook SPEECH]. Stanford AI Syllabus - 1980 Page 5

1.12 Vision

[Winston VISION] contains a good introduction to some areas of scene analysis. [Winston 4 Brown], V2, Sed, present more recent MIT vision efforts, notably Marr's work on representation. [Waltz] presents work on analysis of line drawings. [Thomas and Binford] give an early comparison of machine perception and natural perception. [Nevatia and Binford] describe recognition of complex objects. [Moravec], [Gennery], and [Marr and Poggio] deal with stereo vision. [Erooks, Greiner, and Einford] present a model-based vision system. [Barrow 4 Tenenbaum] describe their efforts at SRI. [Land] describes color visual perception.

1.13 Physical manipulation

Stanford AI films make a good introduction to robotics. [Bolles and Paul] describe the first computer-controlled assembly. [Finkel, Bolles, and Taylor] present AL, the Stanford language for mechanical assembly. [Lieberman] gives a description of a very high level language for robotics. [Binford et al] give an overview of robotics at Stanford. [IJCAI6] contains many brief articles which give a picture of current Japanese projects and achievements (unfortuanately, said picture could stand some image enhancement, and we refrain from recommending any particular articles.)

1.14 Automatic Programming and Program Verification

There has been work in AI devoted to automating the process of writing programs. Some surveys of the field are given in [Biermann APPROACHES] [Green- INFORMAL], and in the last section of [Manna and Waldinger DREAMS]. Also see [Handbook AP] for a detailed, up-to-date survey. Approaches range from formal theorem-proving techniques [Manna and Waldinger DEDUCTIVE] to knowledge-based systems [Green IJCAIS] to systems that attempt to simulate human reasoning [Lenat BEINGS], [Shrobe MONOLOGUE]. Students should be familiar with these approaches, but do not need to know the details of any particular system. A closely related area is program verification — ascertaining that a program does, indeed, do what it is supposed to. Some perspectives on current verification research and discussion of opportunities for extension are found in [Gerhart-1980s]. Other surveys of the field of verification are [Luckham PV 4 VOP] and [London PERSPECTIVES].

1.15 Learning and Inductive Inference Much of the work in AI can be view as an effort to get a program to 'learn ' typically, to view a set of examples, and induce the common concept uniting —them. An excellent survey of this work is in [Smith et al IJCAIS]. Winston [STRUCTURAL] provided a seminal contribution, emphasizing both the necessity for adequate description, and the use of negative examples to more precisely circumscribe the concept desired. Students should be familiar with these Stanford AI Syllabus - 1980 Page 6 papers, and with the issues involved. They should also understand the application of learning techniques to various domains (e.g., [Buchanan THEORY- FORMATION], [Fikes et al GENERALIZED], [Sussman SKILL]), but need not know the technical details of all these cases.

1.16 Psychological Models and Human Problem Solving

Many AI programs have been intended as models of human information processing. Information processing psychology in general is described in [Newell and Simon HPS (Sections 1 and 5 especially)]. An early program which modelled human verbal behavior was [Feigenbaum EPAM]. More recent models are found in [Anderson 4 Bower HAM (chapters 4 and 7)], [Norman 4 Rumelhart EXPLORATIONS], and [Collins 4 Quillian USER]. A famous early program which took one form of intelligence tests was [Evans ANALOGY], and a controversial model of paranoia is described in [Colby SIMULATIONS], Relevant classics from psychology include [Bartlett REMEMBERING] and [Miller MAGICAL]. Some good books on human problem solving are those by Wickelgren, Polya, and Lakatos; see also [Sloman AIJ]. Students should be familiar with (at least) the first and last chapters of [Newell4Simon HPS].

1.17 Automata and Formal Language Theory

Although this is not AI per se, it forms an important part of the background. Relevant work is in [Minsky COMPUTATION], [Manna MTC] and the material on perceptrons in [Hunt Al] (extra detail in [Minsky 4 Papert PERCEPTRONS]).

1 . 18 Programming Languages for AI

List Processing LISP String processing— SNOBOL Associative mechanisms— LEAP/SAIL Active data structures — SIMULA/SMALLTALK/ACTORS Pattern Matching — [Bobrow 4 Raphael] Data Structures [Knuth Vol.l] PLANNER, CONNIVER, QA4, etc. [Bobrow 4 Raphael] Production Systems [Davis 4 King OVERVIEW]

The candidate is expected to be familiar enough with some AI language (e.g. LISP or SAIL) to demonstrate the ability to write simple programs. He or she should also know enough about the features of the more specialized languages (MicroPlanne QA4, the LEAP and multiple process features in SAIL) to discuss the kinds of problems for which they are useful, and the limitations they force the programmer into. It is not necessary to know syntactic details of these features.

r, Stanford AI Syllabus 1980 Page 7

1.19 Note:

Last year there was some controversy about including the following topics on the reading list. McCarthy stated, "I think the syllabus should downplay philosophical and social issues, because the we certainly don't want to grade students on their own views, and the field is fluffy enough as it is without letting or requiring students to be able to regurgitate various people 's views on the issues. If the faculty wants students to have exposure to these issues it should require attendance at a seminar or lecture series and take attendance but not examine." Winograd said, "The purpose of the qualifying examinations is to ensure that students graduating from our department have a sufficient background to work as qualified researchers and teachers in the field. I believe that a perspctive on what we are doing and why is a critical part of the needed background, and that it would be irresponsible for us as a teaching institution to leave it out. Since our general philosophy is to judge competence in all areas by exams rather than required courses, this is no different. Indeed nobody is expected to endorse or regurgitate anyone's views, but (as in all areas) to demonstrate that they are aware of the important issues and have thought about them."

1.20 Philosophical Implications

There has been a continuing discussion about the philosophical implications of intelligent machines. The original paper often cited is [Turing TEST]. The classical book criticizing AI from a philsophical point of view is [Dreyfus CAN'T]. Many of the general issues are discussed in the fascinating [Anderson MINDS] and in [Dennett BRAINSTORMS]. There is an accessible discussion of the issue in [Boden Al]. A recent controversy dealing with AI and the philosophy of science is in [Dresher 4 Hornstein SUPPOSED], [Winograd CONTESTED], and [Dresher and Hornstein RESPONSE]. [Sloman REVOLUTION] is a new important reference (JMC has a copy and the library has one). [Hofstater GODEL] is bizarre, whimsical, at times provocative, and at times informative.

1.21 Political and Social Implications

One of the pioneers in the field discussed these issues in [Weiner HUMAN], and more recently there has been a discussion raised by [Weizenbaum REASON], and responses like [Buchanan, Lederberg, and McCarthy REVIEWS]. [Boden Al] also discusses some of the important questions. [Firschein 4 Coles IJCAI3 survey] attempts to predict some of the future applications of AI . An important controversy in British support for AI is in [Lighthill Al] and responses in the same booklet and in [McCarthy LIGHTHILL].

1.22 History and Politics of the Field

A critical part of being able to do and evaluate research in a field is having some perspective on what things have been done and why — in particular, what lessons have been learned and how can they be applied to keep from Stanford AI Syllabus 1980 Page 8 repeating mistakes. In addition, students need to know where and how things are published in the field if they hope to keep up with current work. It is difficult to give specific references, but the general books on AI [e.g. Boden, Winston, McCorduck, Raphael] each give some perspective. It is also critical to look at some of the early collections of papers, including a careful reading of most of [Feigenbaum 4 Feldman]. Students should have some familiarity with the early history of AI — its connections to cybernetics and machine translation in the 50 's. It is interesting to note some of the early optimistic attempts at "self-organizing systems" and work on perceptrons and neural nets. Today, there is much discussion about the emergence of a discipline called "cognitive science" which includes work now considered artificial intelligence, psychology, linguistics, and philosophy. Students should have done some thinking about the relationships between these disciplines, especially where they adopt differing methodologies in looking at the same phenomena.

The following journals (and conferences) present material relevant to AI , and it is useful to have a general idea of what kinds of things each of them contains. Given a paper, you should be able to discuss where it ought (not) to be submitted.

Journal of AI SIGART SIGCAS Machine Intelligence (1-9) IJCAI proceedings (1-6) TINLAP proceedings (1-2) CACM JACM Cognitive Psychology American Journal of Computational Linguistics Cognitive Science Journal Cognitive Science Society The Behavioral 4 Brain Sciences AAAI AISB Special interest conferences: cybernetics, natural language, robotics

2 Readings

2. 1 Explanation:

The following list is NOT a list of required readings, but a guide to interesting, available materials in each subfield. Some of the papers are not yet published but will be made available. Stanford AI Syllabus - 1980 Page 9

A simple annotation system has been used to give some feeling for the level and importance (measured idiosyncratically by the syllabus preparers) of each paper. The level is indicated as:

ESS: An essay giving general perspective and discussion of basic issues SURV: A survey of research work in some area SUM: A summary at a non-detailed level of a specific piece of research DR : A detailed research report TEXT: A textbook

The importance is based on whether the paper is important for understanding a general perspective (P), whether it has one or more specific important ideas (I), or whether it gives a set of details over which students are expected to have fluent command (D). We repeat: these are opinions, hastily arrived at!

A basic background can be acquired via the books marked "_@"; gaps may be filled with those papers marked "§". These together can be thought of as an initial reading list.

Many of the references below point to more specialized sources for those highly interested; it is expected that each candidate will be highly interested in some areas (hint). We also hope that each student will have only a few gaps (at study-time, not at exam-time) and it will be possible to glance over the outline, choosing to read in those areas where the references pointed to are not familiar.

2.2 References

Adams, J. Conceptual Blockbusting, W.H.Freeman.

§Agin, G. and Binford, T. (1973) Computer Description of Curved Objects. Proc IJCAI3, 1973, PP 629-640 (SUM,l:the basic representation)

.gArtificial Intelligence Handbook; [AlHandbook], a comprehensive encyclopedia of work in AI , prepared by HPP; copies are available in the library (or by bothering Barr or Feigenbaum). (SURV!!)

§Amarel, S. (1968) On Representations of Problems of Reasoning About Actions, in Machine Intelligence _3, pp. 131-171 (eds Meltzer and Michie), New York: American Elsevier Publishing Company. (DR,P ,l:effects of change of repr. on problem solubility, )

Anderson, J. R., and Eower, G. H., Human Associative Memory, V. H. Winston and Sons, Washington, D.C, 1973. Especially Chapters 4, 5, &7. (SURV & DR.P)

Anderson, (ed) , Minds and Machines, Contemporary Perspectives in Philosophy Series, Prentice-Hall, Inc, N.J., 1964. (Collection of essays, each of which is rated at least: (ESS, )) Stanford AI Syllabus - 1980 Page 10

Arnold, R. D.; "Local Context in Matching Edges for Stereo Vision", Proc ARPA Image Understanding Workshop", Cambridge, May 1978, 65-72. (DR)

Balzer, Robert, Neil Goldman, and David Wile, "On the Transformational Implementation Approach to Programming", Proceedings Second International Conference on Software Engineering, Computer Society, Institute of Electrical and Electronics Engineers, Inc., Long Eeach, California, October 1976, pages 337-344.

Barr, A. and Feigenbaum, E., Artificial Intelligence Handbook. Please see [AlHandbook] above in this listing.

Barrow, H.G., J.M.Tenenbaum; "MSYS: A System for Reasoning about Scenes"; SRI AI Center Tech Note 121, April 1976. (SUM)

Barrow, H.G. and Tenenbaum, J.M.; "Recovering Intrinsic Scene Characteristics from Images"; SRI International AI Center, Apr 1978, Tech note 157. (ESS)

Barstow, David R., A Knowledge Base Organization for Rules about Programming, Proc. IJCAIS, 1977, 382-388 (DR)

Bartlett, Frederick, Remembering: A Study in Experimental and Social Psychology, Cambridge, University Press, 1932 (DR,I: memory is active, not passive)

Baumgart, B.; "Geometric Modeling for Computer Vision", Stanford AI Memo AIM 249, CS-463, 1974. (DR)

Berliner, Hans, Some Necessary Conditions for a Master Chess Program, Proc IJCAI3, 1973. PP. 77-85. (SUM,I) Berliner, Hans, Experiences in Evaluation with BKG - A Program that Plays Backgammon, Proc LJCAIS, 1977. (SUM,)

Berliner, Hans, On the Construction of Evaluation Functions for Large Domains, Proc IJCAI6, 1979. (SUM, I)

_Bernstein, M.1., Knowledge-Based Systems: A Tutorial, TM-(L)-5903/000/00A, SDC, June 1977 (SURV.P)

Biermann, Alan W., "Approaches to Automatic Programming," in Advances in Computers, Volume 15, Academic Press, Inc., New York, New York, 1976. (SURV.P)

SBinford, T.O; "Visual Perception by Computer" Invited paper for lEEE Systems and Control, Miami, 1971. (ESS, I: generalized cones)

Binford, T.0., "Computer Integrated Assembly Systems," Proc NSF Adv Prod Techn Grantees Conf, Nov, 1978.

Bledsoe, W.W., and Bruell (1974) A Man-Machine Theorem-Proving System, Journal of AI, 5, 1974, pp 51-72. (SUM.P) gßobrow, Dan, and Allan Collins, editors, Representation and Understanding, New York: Academic Press, 1975 (a collection of papers, each at least (SUM.D) See esp. "Dimensions of Repr." and Woods' "What's in a Link?" Stanford AI Syllabus 1980 Page 1 1

gßobrow and Raphael, (1973) New Programming Languages for AI Research, Computing Surveys, 6, 1974, 155-174. (SURV, I) Still quite relevant, after all these years.

@Bobrow, D. and T. Winograd An Overview of KRL , a Knowledge Representation Language, Cognitive Science, 1, 1977, 3-46 (SUM, I: controlled processing and matching)

Boden, Margaret A. Artificial Intelligence and Natural Man, New York, Books, Inc., 1977 (TEXT 4 SURV.P) Probably the best comprehensive nontechnical introduction to AI .

Bolles, R.C.; "Verification Vision for Programmable Assembly"; Proc. IJCAIS, 1977, 569-575. (DR)

Bolles, R. and R.Paul, "The use of Sensory Feedback in a Programmable Assembly Systems", Stanford AI Lab Memo AIM-220, CS-396, AD772064/2WC, 1973.

_Brachman, R.J., What's in a Concept: Structural Foundations for Semantic Networks, Int'l J. Man-Machine Studies, 9, 1977, 127-152 (DR.P)

Braid,l.C.; Designing with Volumes, Univ of Cambridge, Cantab Press, Cambridge, England, 1973. (ESS & DR)

Brooks, R., R. Greiner, and T.O. Binford; "ACRONYM: A Model-Based Vision System"; Proc IJCAI6, 1979.

Bruce, Bertram, Case systems for natural language. Artificial Intelligence, 6:4, Winter 1975, 327-360. (SURV.P)

Buchanan, 8. , Feigenbaum, and Sridharan (1972) Heuristic Theory Formation, Machine Intelligence 7, pp. 267-280 (the appendix may be omitted). (SUM, I:choosing proper domain is important)

Buchanan, Bruce, Joshua Lederberg, and John McCarthy, Three reviews of J Weizenbaum's Computer Power and Human Reason, Stanford AIM-291, STAN-CS=76 577, November 1976.

Chandrasekharan and Reeker (1974) AI: A Case for Agnosticism, in lEEE Transactions on Systems, Man, and Cybernetics ; January, 1974, pp. BB-94. (ESS.P)

Chase, W.G., editor, Visual Information Processing, Academic Press, New York, 1973. (Collection, each of which is at least (SUM, )) See esp. Newell "Production Systems: Models of Control Structures".

Cohen, Harold, What is an Image?, Proc IJCAI6, 1979. (DR, just glance over; SUM, P, I: Intelligence is in the eye of the beholder)

Cohen, Philip On Knowing What to Say: Planning Speech Acts, Tech Rpt 118, U. of Toronto CSD, January 1978 (DR,I: use of planning to recognize/generate speech)

Darlington and Burstall (1973) A System Which Automatically Improves Programs, Stanford AI Syllabus 1980 Page 12

Proc. IJCAI3, pp. 479-485. (SUM, Irencoding knowledge into schemata rewriting rules)

§Davis, Randall Generalized Procedure Calling and Content-Directed Invocation, Proc. ACM Symposium AI 4 PL, 1977, 45-54 (SURV, I: procedures should state their effects)

Davis, Randall, Meta-Level Knowledge: Overview and Applications, Proc IJACIS, 1977, 920-927 (SUM, I: meta-knowledge)

.Davis, Randall, and Jonathan King An Overview of Production Systems, in Machine Intelligence 8, E. Elcock and D. Michie (eds), Chichester, Ellis Horwood, 1977 (SURV.P)

Davis, Randall, Bruce Buchanan, and Edward Shortliffe, Production Rules as a Representation for a Knowledge-Based consultation Program, Artificial Intelligence, 8, 1977, 15-45 (DR,I: organization and use of knowledge, computer-based consultation)

Dresher, E.E., and N. Hornstein. On Some Supposed contributions of Artificial Intelligence to The Scientific Study of Knowledge, Cognition, 4, 1976 (ESS.P)

Dresher, 8.E., and N. Hornstein. Reply to Winograd, Cognition, 5, 1977, 379-392 (ESS.P)

Dreyfus, Hubert, What Computers Can 't Do, Harper and Row, 1972 (and later edition). (ESS.P)

§Erman, L.D. and V.R. Lesser A Multi-level Organization for Problem Solving Using Many Diverse Cooperating Sources of Knowledge, Proc IJCAI4, 1975, 483- -490 (DR,I: modular knowledge sources, blackboard)

Ernst, George W., and Newell, Allen, GPS: A Case Study in Generality and Problem Solving, Academic Press, New York, New York, 1969. Not central, but many good ideas are inside. (DR, )

Fahlman, Scott E. A System for Representing and Using Real-World Knowledge, AI- TR-450, MIT AI Lab, December 1977 (DR.I: certain inferences should be made quickly and easily, using hardware) gFalk, G. (1972) Interpretation of Imperfect Line Data as a Three-dimensional Scene, Artificial Intelligence, 3, 1972, pp. 101-144. (SUM, I. good when nature falls into a few tens of categories, so task is hard but doable.)

Feigenbaum, E. et al (1971) [DENDRAL] On Generality and Problem Solving: A Case Study Using The DENDRAL Program. (Eds Meltzer and Michie) Machine Intelligence 6, pp 165-190. More detail than [AlHandbook] . (SUM, I:A useful application; Choose domain carefully)

@gFeigenbaum, Edward A., and Feldman, Julian, editors, Computers and Thought, [C4T], McGraw-Hill Book Company, New York, New York, 1963. Old but almost each article is still quite worth a careful reading; to name a few: Feigenbaum, Gelernter, Newell et al, Turing, Armer, Minsky. (Collection; some (ESS,), most (SUM,I)) Stanford AI Syllabus - 1980 Page 13

.Feigenbaum, E.A. The Art of Artificial Intelligence: Themes and Case Studies of Knowledge Engineering, Proc IJCAIS. 1977, 1014-1029 (SURV, I: use of large amounts of domain-specific knowledge)

Feldman, Pingle, Binford, Falk, Kay, Paul, Sproull, Tenenbaum. (1971) The Use of Vision and Manipulation to Solve the Instant Insanity Puzzle, Proc IJCAI2. (SUM, )

Feldman, Jerome A. & Robert F. Sproull Decision Theory and Artificial Intelligence II: The Hungry Monkey, Cognitive Science, 1, 1977, 158-192 (DR,I: use of decision theory to allocate scarce resources)

_Fikes, R. E. et al (1972) Learning and Executing Generalized Robot Plans. Artificial Intelligence, Vol. 3 (Winter 1972). (SUM, I.Triangle Tables; handling of Frame problem,plans. D: Triangle tables) eTikes, R. and G. Hendrix A Network-based Knowledge Representation and its Natural Deduction System, Proc IJCAIS, 1977, 235-246 (SUM, I) Findler, N.V., and Meltzer, 8., editors, Artificial Intelligence and Heuristic Programming, Edinburgh University Press, Edinburgh, Scotland, 1971, ? + ? pages, $15.00. (SUM, a collection of papers)

Finkel, R., Russel Taylor, Robert Bolles, Richard Paul, Jerome Feldman, "AL, A Programming System for Automation", AIM-243, CS-456, 130 pages, November 1974. (DR, )

§Firschein, 0., and Coles, S. (1973) Forecasting and Assessing The Impact of Artificial Intelligence on Society. Proc IJCAI3; 105-120 (SUM, Questionaire results: Look this over, at least.)

§Floyd, R. W. (1971) Toward Interactive Design of Correct Programs, IFIP 7J_, (cd., C.V. Freeman), Volume 1, pp. 7-10. (ESS: An extended example. Brief, but contains an idea I: Auto.pgmming via dialogue)

Fogel, Lawrence J., Owens, Alvin J., and Walsh, Michael J., Artificial Intelligence through a Simulation of Evolution, John Wiley and Sons, New York, New Yorki 1966. Just glance over this and notice what its significance is. (DR, P:title of the book)

§Funt, Brian WHISPER: A Problem-Solving System Utilizing Diagrams, Proc IJCAIS, 1977, 459-464 (SUM, I: analogical reasoning may buy us some nice properties)

Gennery.D.B. ; "A Stereo Vision System for An Autonomous Vehicle"; Proc IJCAIS, 1977, 576-582. (DR)

Gerhart, Susan L., Program Verification in the 1980s: Problems, Perspectives, and Opportunities , Research Report ISI/RR-78-71 , Information Sciences Institute, University of Southern California, Marina del Rey, California, August 1978.

Gips, J. "Shape grammars and their uses", AI memo 231, last third is on aesthetics. Recently published (Stiny 4 Gips): Algorithmic Aesthetics, UC Berkeley Press, 1978. (DR, I:picture grammar, aesthetics related to simplicity) Stanford AI Syllabus - 1980 Page 14

§Goldman, Neil, Sentence Paraphrasing from a Conceptual Base, CACM, 18:2 February 1975, pp. 96-107. (SUM, I:integration of decision tree control with conceptual dependency).

Goldman, N., R. Balzer, and D. Wile, The Inference of Domain Structure from Informal Process Descriptions, Proc. Workshop on Pattern-Directed Inference Systems, SIGART Newsletter, June 1977, 75-82 (SUM) "

Goldstein, Ira, and Artificial Intelligence, Language, and the Study of Knowledge, Cognitive Science, 1, 1977, 84-120 (SURV,P) gGreen, C. C. (1969) CTP] The Application of Theorem Proving to QA Systems, Stanford Technical Report CS 138, SAIL Memo AI-96. (DR, I: use of Resolution to perform deduction 4 answer questions)

Green, Cordell, "An Informal Talk on Recent Progress in Automatic Programming", Lectures on Automatic Programming and List Processing, PIPS-R-12, Electrotechnical Laboratory, Tokyo, Japan, November 1976, pages 1-69. (SURV,)

Green, Cordell, and Barstow, David, "On Program Synthesis Knowledge", Artificial Intelligence, Volume 10, Number 3, November 1978, pages 241-279. (DR, I)

Greenblatt, R.8., D. Eastlake and S. Crocker, The Greenblatt Chess Program Proceedings of the 1967 Joint computer conference, 30:801-810, 1967. (SUM, I:use of a variety of game playing techniques)

Grosz, Barbara J., Utterance and Objective: Issues in NL Communication, Proc IJCAI6, 1979. (SURV.P) If interested, examine her excellent IJCAIS paper also.

Guard, J.R., et al. (1969) Semi-Automated Mathematics, JACM 16, January, 1969, pp. 49-62. (SUM, D:A new result in Math has been established by a computer program: SAM's LEMMA)

Hayes, Patrick J. Computation and Deduction, Proc MFCS Symposium, Czech. Acad. 1973 (ESS,P)

Hayes, Patrick J. Some Problems and Non-Problems in Representation Theory, AISB Summer 1974 (ESS 4 SURV, D)

@Hayes, Patrick J. In Defence of Logic, Proc IJCAIS, 1977, 559-565 CESS.P)

Heidorn, George, Automatic Programming Through Natural Language Dialogue: A Survey, IBM Journal of Research and Development, Volume 20, Number 4, July 1976. (SURV, P) gHendrix, G. Expanding the Utility of Semantic Networks through Partitioning, Proc IJCAI4 1975, 115-121 (SUM, I: use of spaces in nets, to achiev scoping, etc)

Hewitt, Carl Viewing Control Structures as Patterns of Passing Messages, Artificial Intelligence, 8, 1977, 323-364 (DR,I: actors; Hewitt's only comprehensible paper)

Hunt, E. and S. Poltrock The Mechanics of Thought, in Human Information Processing: Tutorials in Performance and Cognition, B. Kantowitz (ed), Hillsdale, Erlbaum, 1974 (SURV.P)

Sciences,

Conference, Stanford AI Syllabus 1980 Page 15

Hunt, Earl 8., Artificial Intelligence, Academic Press, Inc., New York, New York, 1975. (TEXT, D:good coverage of pattern recog. and perceptrons)

International Joint Conferences in Artificial Intelligence, held biannually since 1969; proceedings available from program chairmen; best indicator of current research trends

Jackson, Philip C, Jr., Introduction to Artificial Intelligence, Petrocelli Books, New York, New York, 1974. Elementary; if you feel lost in some subfield, consult this. (TEXT, )

Julesz, 8., "Experiments in the Visual Perception of Texture"; Scientific American, April 1975. (SUM)

Kanade, T.; "A Theory of Origami World"; Dept of Comp Sci, Carnegie-Mellon Univ, 1978, CMU-CS-78-144. (DR)

Kant, Elaine, "The Selection of Efficient Implementations for a High-Level Language", Proceedings of the Symposium on Artificial Intelligence and Programming Languages, SIGPLAN Notices, Volume 12, Number 8, SIGART Newsletter, Number 64, August 1977, pages 140-146.

Kellogg, Charles, Philip Klahr, and Larry Travis. Deductive Methods for Large Data Bases, Proc IJCAIS, 1977, pp 203-209 (SUM, I: ABSTRIPS-like skeletal plans can help deduction) de Kleer, Johan, Jon Doyle, Guy L. Steele Jr, 4 . AMORD: Explicit Control of Reasoning, Proc ACM Sym AI PL, SIGART 64, August 1977, 116-125 (SUM, I)

@Kling, Robert E., A Paradigm for Reasoning by Analogy, Artificial Intelligence, 2, 1971, pp. 147-178. (SUM, I:similar to Evans' idea, but analyzed further.)

Kuhn, Thomas, The structure of scientific revolutions, Chicago: University of Chicago press, 1972. (ESS.P, I: Paradigm shifts)

Lakatos, Imre, Proofs and Refutations, (ESS, I: sprial of criticism and improvement of conjectures)

Land, E.H.; "The Retinex Theory of Color Vision"; Scientific American, Dec 1977 (SUM)

Larson, J.8., and R.S. Michalski Inductive Inference of VL Decision Rules, Proc Wkshp Patt-Dir Inf Systems, SIGART 63, 1977, 38-44 (SUM)

Lehnert, Wendy, Human and Computational Question Answering, Cognitive Science 1, 1977, 47-73 (SUM)

Lenat, Douglas 8., BEINGS: Knowledge as Interacting Experts, Proc IJCAI4, 1975 126-133 (DR, Irbeings)

§Lenat, Douglas 8., Automated Theory Formation in Mathematics, Proc IJCAIS, 1977, 833-842 (DR.I: heuristics to generate search) Stanford AI Syllabus 1980 Page 16

Lenat, Douglas 8., and John McDermott, Less Than General Production System Architectures, Proc IJCAIS, 1977, 928-932 (SURV, I: gain power by sacrificing generality)

Lesser, Victor R. , and Lee E. Erman, A Retrospective View of the HEARSAY-II Architecture, Proc IJCAIS, 1977, 790-800 (SUM.P)

.Victor Lesser, Richard Fennell, Lee Erman and D. Raj Reddy, Organization of the Hearsay II Speech Understanding System, lEEE Symposium on Speech Recognition, Computer Science Dept., Carnegie Mellon Univ., 1974. (SUM, I:modular system organiztion)

Lettvin, J., H.R. Maturana, W.S. McCulloch, and W.H. Pitts, What the frog's eye tells the frog's brain, Proceedings of the IRE 47(1959), pp. 1940-1951. (SUM)

Lieberman, L., AUTOPASS, A Very High Level Programming Language for Mechanical Assembler Systems, IBM Watson Research Center Report RC 5599, No. 24205, 1975.

Lighthill, Sir J., and Sutherland, Needham, Longuet-Higgins, and Michie (1973) AI: A Paper Symposium; by the British Science Research Council, April, 1973. A pro/con AI debate. Try to see the McCarthy, Michie vs. Lighthill debate on videotape. (ESS, P, a general survey giving Lighthill 's view on AI . See McCarthy's response)

Lindsay, Peter H., and Norman, Donald A., Human Information Processing: An Introduction to Psychology, Academic Press, Inc., New York, New York, 1972. (TEXT, P a comprehensive elementary introduction to cognitive psychology from a view congenial to AI).

London, R. L., "Perspectives on Program Verification", in Yeh, R. T., (ed), Program Validation, Current Trends in Programming Methodology, Volume 2, Prentice-Hall, Inc., 1977, pages 151-172.

Lozano-Perez, Tomas, and Patrick H. Winston, LAMA: A Language for Automatic Mechanical Assembly, Proc. IJCAI-5, 710-716. (SUM)

Low, James, and Rovner, Paul, "Techniques for the Automatic Selection of Data Structures",. Third ACM Symposium on Principles of Programming Languages, January 1976; also TR4, Computer Science Department, University of Rochester, Rochester, New York, November 1975.

Luckham, D. C, "Program Verification and Verification Oriented Programming", invited paper, in Gilchrist, 8., editor, Information Processing 77: Proceedings of IFIP Congress 77, North-Holland Publishing Company, Amsterdam, The Netherlands, 1977, pages 783-793.

J., [ADVICE-TAKER] "Programs With Common Sense", Stanford AI Memo AIM-7, .AD78504 4 , 7 pages, September 1963. For details, also look at also the following memo: (ESS, I: what we need i5...)

McCarthy, J., "Situations, Actions, and Causal Laws", Stanford AI Memo 2, July 1963. (ESS/DR: I:You can formalize these notions) Stanford AI Syllabus 1980 Page 17

J. and Hayes, P. (1969) Some Philosophical Problems from The Standpoint of AI . Machine Intelligence 4 (eds Meltzer and Michie) pp. 463- -502. Edinburgh University Press. (ESS/DR: I/D: More of the same as the last reading. Further developed.)

McCarthy, John, Review of Lighthill debate, Artificial Intelligence, 5, 1974, 317-322 (ESS.P)

McCarthy, John, Mechanization of Thought Processes, Her Majesty's Stationery Office, 1958. Contains early McCarthy papers.

.McCarthy, J., Epistemological Problems of Artificial Intelligence, Proc. IJCAIS, 1977, 1038-1044 (ESS, I: what is AI still missing?)

_McDermott, Drew V., Vocabularies for Problem Solver State Descriptions, Proc IJCAIS. 1977, 229-234 (SUM, I)

Manna, Zohar, Mathematical Theory of Computation. (TEXT, I/D: Read some book or article to gain familiarity with Prop and Pred Calc)

Manna, Zohar, and Richard Waldinger, Synthesis: Dreams => Programs, AIM-302, Stanford, November 1977 (DR)

Manna, Zohar, and Richard Waldinger, Knowledge and Reasoning in Program Synthesis, Artificial Intelligence, 6, 1975, 175-208 (ESS.I/P)

Manna, Zohar, and Richard Waldinger, "A Deductive Approach to Program Synthesis," SRI AI Center Tech. Note 177, Dec. 1978.

@Manna, Zohar, Six Lectures on the logic of computer proramming, Stanford AIM 318, Nov 1978 (SURV, I/P)

§Marr,D. and T.Poggio; "Cooperative Computation of Stereo Disparity"; Science, 194, Oct 1976, 283-287. (SUM)

Marr.D., "Analysis of Occluding Contour", MIT AI Memo AIM 372, Oct 1976. (SUM)

Martin and Fateman (1971) The MACSYMA System, in (S. Petrick, cd.) 2nd Symposium on Symbolic and Algebraic Manipulation. NY: ACM SIGSAM. pp 59-75. (SUM, I: application of AI techniques to a specific domain area)

Meltzer, Bernard, and Michie, Donald, editors. Machine Intelligence, volumes 1- 6, American Elsevier Publishing Company, New York, New York, volumes 7-8, Halstead Press, New York; volumes 9- , John Wiley 4 Sons, New York. Almost annually (since 1967). Needless to say, don't study every article. (Collection of articles, each at least (SUM, )).

Bernard, and Bobrow, Daniel, editors. Artificial Intelligence: An International Journal, North-Holland Publishing Company, Amsterdam, The Netherlands, quarterly (since 1970). (Collection of articles, each at least (SUM, )).

Michie, Donald, On Machine Intelligence, John Wiley and Sons, New York, New York, 1974. (SURV, P) Stanford AI Syllabus - 1980 Page 18

§Miller, G., The magical number 7, plus or minus 2, Psychological Review, 63, 1956, 81-97 (ESS, I: memory can hold a fixed number of chunks, regardless of their complexity)

Minsky, Marvin, Computation: Finite and Infinite Machines, Prentice Hall, 1968. Not in AI but you should know at least this much anyway. (TEXT, I/D: Know at least this much about the theory of computation)

Minsky, Marvin, editor, [SIP] Semantic Information Processing, The MIT Press, Cambridge, Massachusetts, 1968. (Collection of MIT natl. lang. dissertations, all at least (SUM/DR, )) See esp. Evans (SUM, I:power of using even a crude version of a single simple heuristic for analogy) and Quillian (DR.I: network flow model for conceptual linking) and Minsky (ESS, P: makes explicit many of the underlying notions of AI models).

Minsky and Papert Perceptrons, MIT 1969. A sufficient expertise can be gained from the appropriate section of [Hunt Al], In looking at this book, try to read through page 25; then look through the rest; especially note the concluding remarks, pp. 227-246. (DR, I:applying rigourous mathematics to what Al-systems of different types can theoretically achieve)

Mitchell, T.M., Version Spaces: A Candidate Elimination Approach to Rule Learning, Proc IJCAIS, 1977, 305-310 (DR.I: the title)

Moore, Jim and Allen Newell, How can MERLIN understand?, in Gregg (cd.) Knowledge and Cognition, New Jersey: Lawrence Erlbaum Associates, 1973. (SUM, I: Beta structures; criteria for understanding systems)

Moore, Robert Carter, Reasoning about Knowledge and Action, Proc IJCAIS, 1977, 223-227 (SUM, I)

Moorer, James A., "Music and Computer Composition", Comm. ACM, January 1972. (SURV)

Moravec.H.P. , "Towards Automatic Visual Obstacle Avoidance" Proc. IJCAIS, 1977 584. (SUM)

Mujtaba, S. , and R. Goldman. "AL User's Manual"; Stanford AI Lab Memo, 1979 (SUM, P)

Nash-Webber, Bonnie L., and Schank, Roger C. (eds.). Theoretical Issues in Natural Language Processing, an interdisciplinary workshop in computational linguistics, psychology, linguistics, and artificial intelligence, MIT, June 1975. Preprints distributed by the Association for Computational Linguistics. (a collection of papers, mostly SUM)

§Nevatia,R. and T.O. Binford; "Description and Recognition of Curved Objects"; Artificial Intelligence, 8, p 77, Feb 1977; (DR) gNewell, A. (1969) [ILL] Heuristic Programming: 111-Structured Problems, in (cd. Aronofsky, A.) Progress in Operations Research 111, John Wiley and Sons. (ESS, P)

Newell, A. (1965) Limitations of The Current Stock of Ideas about Stanford AI Syllabus - 1980 Page 19

Problem:Solving. Proceedings of a Conference on Electronic Information Handling, pp. 195-208. (Eds Kent and Taulbee) New York: Spartan. (ESS, interesting reading)

Newell, A. (1970) Remarks on The Relationship Between AI and Cognitive Psychology, in (Banerji and Mesarovic, eds.) Theoretical Approaches to Non- Numerical Problem Solving, pp 363-400. New York: Springer-Verlag Pub. (ESS, P)

Newell, A., Barnett, Jeffrey, Forgie, James W., Green, Cordell, Klatt, Dennis, Licklider, J.C.R., Munson, John, Reddy, D. Raj, and Woods, William A., [SPEECH] Speech Understanding Systems: Final Report of a Study Group, American Elsevier Publishing Company, New York, New York, 1973, xiv + 137 pages, $6.75. Read especially: Chaps. 1,4; Appendix A2. (SURV, I:Evaluating research goals and guiding research toward them)

§Newell, A., and Simon, Herbert A., [HPS] Human Problem Solving, Prentice-Hall, Englewood Cliffs, New Jersey, 1972, xvi + 920 pages. Read the first and last chapters, look over Chaps. 3,4,8. (DR/ESS/SUM, D:know what LT and GPS were, what a PBG is, production systems)

Nilsson, N. J. (1974) [OVERVIEW] Artificial Intelligence, SRI Technical Note 89 (March, 1974), and also Information Processing 74, North-Holland: Amsterdam, 1975 (SURV)

Nilsson, Nils J., [Al] Problem-solving Methods in Artificial Intelligence, McGraw-Hill Book Company, New York, New York, 1971 (TEXT, but I/D:Good presentation of resolution, searching, , etc.)

Nilsson, Nils J., Principles of Artificial Intelligence, Tioga, Palo Alto, 1980. (TEXT, attempts a new synthesis of much of the field)

Norman, Donald, D. Rumelhart, and the LNR Research Group, Explorations in Cognition, Freeman, 1975. (SUM, P: a collection of papers done from Norman's Psychology/AI viewpoint)

Park, W.T.; "Minicomputer Software Organization for Control of Industrial Robots"; Proc Int Jt Auto Control Conf, San Francisco, 1977, p164, TA2I. (A survey of existing systems) (ESS, SURV, P)

Pettigrew.J.D.; "The Neurophysiology of Binocular Vision" Scientific American, August 1972. (SUM)

§Polya , G. Three representative books are listed here; you should be acquainted with the kinds of principles Polya tries to impress, his studies of heuristics. It is not necessary to study the detailed contents of these books. How to Solve It, Doubleday Anchor Books, 1945. Induction and Analogy in Mathematics, Princeton U. Press, 1954. Patterns of Plausible Inference, Princeton U. Press, 1968. (DR, I:heuristics and how to use them)

Pople, H., Meyers, J., and Miller, R., DIALOG, a model of diagnostic logic for internal medicine, Proc. IJCAI., 1975, 848-855. (SUM)

Raphael, Bertram, The Thinking Computer, W.H. Freeman, 1979. (ESS, ) Stanford AI Syllabus 1980 Page 20

[lEEE] Reddy, D. Raj, editor, Speech Recognition: Invited Papers Presented at the 1974 lEEE Symposium, Academic Press, Inc., New York, New York, 1975. (Collection of articles, all at least (SUM, )) See esp. Reddy 4 Erman, pp. 457-480.

Reiter, Raymond, On reasoning by default, Theoretical Issues in Natural Language Processing-2, Urfcana, Illinois: Associatior. for Computing Machinery, 1978, 210-218. (SURV, I: non-monotonic logic)

Rieger, Charles J., 111, An Organization of Knowledge for Problem-solving and Language Comprehension, Artificial Intelligence, 7, 1976, 89-128 (SUM, I)

Rovner, Paul D., Automatic Representation Selection for Associative Data Structures, Ph.D. thesis. Harvard University, Cambridge, Massachusetts, TRIO, Computer Science Department, University of Rochester, Rochester, New York, September 1976.

Rubin, S.; "The ARGOS Image Understanding System"; Dept of Comp Sci, Carnegie- Mellon Univ, Nov 1978, Ph.D.thesis. thesis (DR), or Proc ARPA Image Understanding Workshop, Carnegie-Mellon, Nov 1978, 159-162. (SUM)

Rumelhart, D., P. Lindsay, and D. Norman, A Process Model for Long-term Memory, in The Organization of Memory, E. Tulving and W. Donaldson (ed), N.Y., Academic Press, 1972 .(SUM.P)

Rustin, R., editor, Natural Language Processing, Algorithmics Press, New York, New York, 1973. (Collection of articles, all at least (SUM, )) See esp. W. Woods' ATN article.

Ruth, Gregory R., "PROTOSYSTEM I: An Automatic Programming System Prototype", in Ghosh, Sakti P., and Liv, Leonard V., editors, AFIPS Conference Proceedings: 1978 National Computer Conference, Volume 47, AFIPS Press, Montvale, New Jersey, June 1978, pages 675-681.

Rychener, M. , Control Requirements for the Design of Production System Archtectures, Proc. ACM Symposium AI 4 PL, 1977, 37-44 (SUM)

.Sacerdoti, Planning in a Hierarchy of Abstraction Spaces, Proc IJCAI3, 1973, 412-422 (SUM, I:planning is just searching a sparser, more abstract space)

.Sacerdoti, E., The Nonlinear Nature of Plans, Proc IJCAI4, 1975, 206-214 (DR.I the title)

_[S4C] Schank, R. and Colby, K. Computer Models of Thought and Language, San Francisco: Freeman, 1973 (Collection; Note especially chapters 1,4,5,6)

Schank, Roger C, Conceptual Information Processing, Amsterdam: North Holland, 1975, See esp. Rieger (pp. 157-288; DR,I: uncontrolled forward inferencing is necessary in some situations) and Riesbeck (DR,I: use of predictions in parsing) . Schank, Roger, Neil Goldman, Charles Rieger, and Chris Riesbeck, MARGIE: Memory, Analysis, Response Generation, and Inference on English, 3 IJCAI, 1973, pp. 255-261. (SUM, I: conceptual dependency for system integration) Stanford AI Syllabus - 1980 Page 21

Schank, Roger, and the Yale AI Project, SAM — A story understander , Yale University Computer Science Research Report 43, August, 1975. (SUM) gSchank, Roger, and Robert Abelson. Scripts, Plans, and Knowledge, Proc. IJCAI4, 1975, 151-157 (SUM, I: scripts (frames, schemata))

Schank, Collins, and Charniak, eds., Cognitive Science, journal, published quarterly since 1977 by Ablex Publishing Co, New Jersey

Schatz, B.; "The Computation of Immediate Texture Discrimination" MIT Artificial Intelligence Laboratory, AI Memo 426, August 1977. (DR)

Schmidt, C.F., and N.S. Sridharan, Plan Recognition Using a Hypothesize and Revise Paradigm: An Example, Proc IJCAIS, 1977, 480-486 (SUM, I)

Schubert, L., Extending the Expressive Power of Semantic Networks, Proc IJCAI4, 1975, 158-164 (DR.I: how to represent quantification, etc, in semantic nets)

Schwartz, Jacob T., On Programming: An Interim Report on the SETL Project, revised, Computer Science Department, Courant Institute of Mathematical Sciences, New York University, New York, New York, June 1975.

Shortliffe, Edward, MYCIN: Computer-Based Medical Consultations, New York: American Elsevier, 1976. (DR.I: automated diagnosis)

§Shortliffe, Davis, Axline, Buchanan, Green, and Cohen, Computer-based Consultations in Clinical Therapeutics: Explanation and Rule Acquisition Capabilities of the MYCIN System, preprint for article in Volume 8 of the Journal for Computers in Biomedical Research, June, 1975. (SUM, I:Medical application of production systems; communication with experts)

Shrobe, Howard, Richard Waters, and Gerald Sussman, A Hypothetical Monologue Illustrating the Knowledge Underlying Program Analysis, MIT LCS Memo 506, January 1979. (ESS,)

SIGART Newsletter, Special Interest Group on Artificial Intelligence (SIGART), Association for Computing Machinery, New York, New York, quarterly (if you believe that, you probably will have trouble passing the qual). (SURV/SUM, )

Simmons, R. (1965) Answering English Questions by a Computer: A Survey, CACM 8, 1; January, 1965, pp. 53-70. (SURV, gives reasonable picture of state of art at that time)

Simmons, R. (1970) Natural Language QA Systems. CACM 13, 1; Jan., 1970, pp. 15 30. (SURV, gives reasonable picture of state of art at that time)

Herbert A., How Big is a Chunk?, Science, 1974, 183, 482-488 (ESS)

§Simon, H (1973) Lessons from Perception for Chess-Playing Programs (and vice versa), CMU Computer Science Research Review 1972-1973, pp. 35-40. (ESS I)

Simon, Herbert A., and Siklossy, Laurent, editors. Representation and Meaning: Experiments with Information Processing Systems, Prentice-Hall, Englewood Cliffs, New Jersey, 1972, xx + 440 pages, (collection of CMU dissertations each (SUM or DR)) See esp. Simon's "On Reasoning About Actions" (SUM.P)

Simon, Stanford AI Syllabus - 1980 Page 22

Sloman, Aaron (1971) Interactions Between Philosophy and Artificial Intelligence: The Role of Intuition and Non-Logical Reasoning in Intelligence, Journal of AI, 2, 1971, pp. 209-225. Provocative. (ESS, P looking for philosophical implications of AI work)

Sloman, Aaron, The Computer Revolution in Philosophy, Humanities Press, 1979.

Smith, Erian C, Levels, Layers, and Planes: The Framework of a System of Knowledge Representation Semantics, unpublished Masters thesis, Dept. of E.E. 4 C.S., M.1.T., 1978. (DR.I: new directions in representation and meaning)

"Smith, R.G., T.M.Mitchell, R.A. Chestek, and B.G.Buchanan, A Model for Learning Systems, Proc IJCAIS. 1977, 338-343 (SURV, P)

Soloway, Elliot M. and Edward M. Riseman, Levels of Pattern Description in Learning, Proc IJCAIS, 1977, 801-811 (DR.I)

Sussman, G., and D.V.McDermott, From Planning to Conniving: A Genetic Approach, Proc ACM FJCC. 1972 (SUM, I: where Planner went wrong, and Conniver doesn't)

Szolovits, P., L.B. Hawkinson, and W.A. Martin, An Overview of OWL, an language for knowledge representation, M.I.T. LCS-TM-86, 1977. (SUM)

Thomas,A. J. and T. 0. Binford; "Information Processing Analysis of Visual Perception: A Review"; Stanford AI Lab Memo AIM-227, CS-408 , 1974. (SURV, ESS)

Vere, Steven A., Induction of Relational Productions in the Presence of Background Information, Proc. IJCAIS. 1977, 349-355 (DR.)

gWalker, Donald E., William H. Paxton, et al., Procedures for Integrating Knowledge in a Speech Understanding System, Proc IJCAIS, 1977, 36-42 (SUM, I)

Waltz, David (cd.), TINLAP-2: Theoretical Issues in Natural Language Processing, an interdisciplinary workshop in computational linguistics, psychology, linguistics, and artificial intelligence, University of Illinois, June 1977. (a collection of papers, mostly SUM)

Waterman, D., and F. Hayes-Roth, Pattern-Directed Inference Systems, New York, Academic Press, 1978 (a collection of papers, mostly SUM; good coverage of state-of-the-art work in production systems) See esp. Duda et al; Hayes-Roth, Waterman, 4 Lenat.

Weiner, N. The Human uses of Human Beings: Cybernetics and Society, Anchor 1954. (ESS, P)

Weizenbaum, J., ELIZA, CACM 1966, 9, 36-45. (SUM, lilt's easy to pretend intelligence by reflective listening, or, more generally, by very careful selection of the task to be performed)

"Weizenbaum, Joseph, Computer Power and Human Reason, San Francisco, W.H. Freeman, 1976 (ESS, I: we should be aware of the larger implications of our work) . *

Stanford AI Syllabus 1980 Page 23

Weyhrauch, R. Prolegomena to a theory of mechanized formal reasoning. Stanford A.I. Memo, 1979 (DR.P)

Wickelgren, Wayne A., How to Solve Problems: Elements of a Theory of Problems ' and Problem-solving. San Francisco: W.H. Freeman and Company, 1974. Integrates Newell and Polya 's ideas. (DR, I: Reconciling Polya and one of his students, A. Newell)

Wilks, V., Natural Language Understanding Systems within the AI Paradigm: A Survey and Some Comparsions, AIM-237, Stanford U., 1974 (SURV.P)

.Winograd, Terry, "Five Lectures on Artificial Intelligence", Stanford AIM-246, C5459, ADAOOOOBS/IWC, 93 pages, September 1974. (ESS, P)

Winograd, Terry, Understanding Natural Language, Academic Press, Inc., New York, New York, 1972, viii + 191 pages, $10.00. See paper in Schank and Colby or in 5 Lectures for summary. (DR, I: Procedural knowledge in an integrated system) . Winograd, Terry, Frame representations and the declarative/procedural controversy, in Bobrow 4 Collins (ed), Representation 4 Understanding, 1975 (ESS, I:modularity of knowledge structures, frames)

Winograd, T. , On some contested suppositions of generative linguistics about the scientific study of language, Cognition, 5, 1977 (reply to earlier article by Drescher and Hornstein) (ESS.P)

Winograd, Terry, Towards a Procedural Understanding of Semantics, Revue Internationale de Philosophic, 1976 fasc. 3-4 (117-118). (ESS, I: procedural semantics in linguistics)

Winston, P. H. (1972) The M.I.T. Robot, Machine Intelligence 7, American Elsevier Pub. (SURV, I:Heterarchical systems)

.Winston, Patrick H., editor, The Psychology of Computer Vision, McGraw-Hill Publishing Company, New York, New York, 1975, $18.00. Mostly MIT vision work. See Minsky 's FRAMES paper, Shirai's, Waltz's, Winston's. (Collection, each at least (DR/SUM, ))

__Winston, P. and R. Brown, eds.. Artificial Intelligence: An MIT Perspective, (2 volumes), M.1.T. , 1979. Copies available from MIT, library, and TW. This subsumes the 1974 memo "New Progress in Artificial Intelligence". (SURV, Collection of summaries)

.Winston, Patrick H., Artificial Intelligence, Reading: Addison-Wesley, 1977 (TEXT, P) (one of the best overviews of the field)

Woodham, R.J.; "A Cooperative Algorithm for Determining Surface Orientation from a Single View"; Proc IJCAIS. 1977, 635-641. (DR)

Woods, W. A. and Makhoul, J. (1973) Mechnical Inference Problems in Continuous Speech Understanding. Proc IJCAI3, pp. 200-207. This describes a partly- implemented system. For the final story, see Woods* article in lEEE simulation) Transactions on ASSP, February, 1975. (SUM, r I

Stanford AI Syllabus 1980 Page 24

Woods, W., M. Bates, B. Bruce, and B.L.Nash-Webber , Uses of Higher Level Knowledge in a Speech Understanding System, SIGART, April 1976 (SUM, I)

Yakimovsky, Y. and Feldman, J. (1973) A Semantics-Based Decision Theory Region Analyzer. Proc IJCAI3, Advanced Papers pp 580-8. (SUM, I:pruning using real- world constraints)