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                         STANFORD UNIVERSITY


                     COMPUTER SCIENCE DEPARTMENT


                            April 1, 1972










                    Ph.D. QUALIFYING EXAMINATION



                       Artificial Intelligence









The examination will be open book.  The first session  will  be  from
9:30  to  12:30  pm, and the second session will be from 1:30 to 4:30
pm.  No work on the exam is to be done during the lunch break.

   SYLLABUS FOR QUALIFYING EXAMINATION IN ARTIFICIAL INTELLIGENCE
    Computer Science Department, Stanford University, April, 1972


	Ideally, a student preparing to take a qualifying examination
in artificial intelligence would be advised  to  read  Grimblefritz's
"Methods  and  Issues  in  Artificial  Intelligence, 5th edition" and
would be assured that if he had  read  this  book  and  mastered  the
techniques  described  therein, he could pass the examination even if
to do research in AI would  require  acquaintance  with  more  recent
work.   Unfortunately, no such book exists, partly because no-one has
written it, but mainly because the field is not in a state that would
permit it.

	Nevertheless, this year the AI Quals Committee has decided to
try a short reading list using one or two references for each of  the
main subfields of artificial intelligence.

	For  the  purposes  of  this  exam  we  will  divide  AI into
subfields as follows:

	1. Heuristics.  This includes methods for searching spaces of
possibilities  for  solutions  to problems.  It includes game playing
and theorem proving as subfields.  The general reference is  (Nilsson
1971)  for  general methods.  However, much of the information in the
field is contained in separate  investigations  that  have  not  been
generalized.   Therefore,  we  also ask you to read (Slagle 1971) and
(Feigenbaum, Buchanan and Lederberg  1971).   Search  algorithms  are
often  written  in  languages devised for AI purposes such as LISP or
more recently Microplanner (Winograd et al, 1971b).  General  purpose
languages  like  Algol  are also used.  There may be questions on the
exam requiring that a program be written  expressing  some  heuristic
procedure,   and   familiarity  with  LISP,  Microplanner,  and  some
Algol-like language is expected.

	2.  Representation.   It is becoming increasingly clear  that
the  representation  of information in the machine about the external
world, the laws that govern the effects of actions, goals, and  where
knowledge  is  to be found is a key problem in AI.  At present, there
is not a uniformly used approach to this.  The student is expected to
understand  the  approaches  described  in (McCarthy and Hayes 1969),
(Winograd 1971a), (Newell 1965), and (Amarel 1968).
	A  related  area  is  the  processing of information given in
natural language.   Here the references are (Winograd  1971a)  again,
(Simmons

	3.  Robotics.  This  includes  methods of getting information
about the physical world into the computer  and  representing  it  in
useful  form  once  it is gotten in.   The references here are not in
particularly good shape but the student should read (Newell 1971, the
Speech Report), (Falk 1970), and (Feldman et. al. 1971).

	The  current  state  of  research in AI is represented in the
journal  "Artificial  Intelligence",  the  Proceedings  of   the   AI
Conferences  (1969  and  1971),  the  volumes  in  the series Machine
Intelligence.  Collections of older work  are  included  in  Minsky's
Semantic  Information  Processing and Computers and Thought edited by
Feigenbaum and Feldman.


                             REFERENCES


Amarel, S. (1968) On Representations of Problems of Reasoning about
	Actions. Machine Intelligence 3, pp. 131-171 (eds Meltzer,
	B. and Michie, D.). New York: American Elsevier Publishing
	Company, Inc.

Buchanan, B., Feigenbaum, E.A. and Lederberg, J. (1971) A Heuristic
	Programming Study of Theory Formation in Science. Proceedings
	of the Second International Joint Conference on Artificial
	Intelligence. London:The British Computer Society.

Falk, Gilbert (1970) Computer Interpretation of Imperfect Line Data
	as a Three-dimensional Scene.     Stanford Artificial
	Intelligence Report:Project Memo AI-132.

Feigenbaum, E.  and Feldman, J. (eds) (1963) Computers  and  Thought.
	New York:McGraw-Hill.

Feldman, J., Pingle, K., Binford, T., Falk, G., Kay,, A., Paul, R.,
	Sproull, R. and Tenenbaum, J. (1971) The Use of Vision and
	Manipulation  to  Solve  the   "Instant   Insanity"   Puzzle.
	Proceedings of the Second International Joint Conference on
        Artificial Intelligence. London:The British Computer Society.

McCarthy,  John and Hayes, P. (1969) Some Philosophical Problems from
        the Standpoint  of Artificial Intelligence.  Machine 
	Intelligence 4, pp. 463-502 (eds Meltzer, B. and Michie, D.).
	Edinburgh:Edinburgh University Press.

Minsky,  Marvin  (ed)   (1968)   Semantic   Information   Processing.
	Cambridge:M.I.T. Press.

Newell, A.  (1965) Limitations of the Current Stock  of  Ideas  about
        Problem-Solving.     Proceeddngs  of  a  Conference   on
	Electronic Information Handling, pp. 195-208, (eds Kent,
        A. and Taulbee,  O.).   New York: Spartan.

Newell, A., Barnett, J., Forgie, J., Green,  C.,  Licklider,  J.C.R.,
	Munson, J., Reddy, R. and Woods, W. (1971) Speech
	Understanding Systems: Final Report of a Study Group.
        Carnegie-Mellon  University:Computer Science Department

Nilsson,  N.J.     (1971)  Problem-Solving  Methods   in   Artificial
        Intelligence .New York: McGraw-Hill.

Simmons, R.  (1970)  Natural  Language  Question  Answering  Systems.
	Communications of the ACM, 13, 1, 15-30.

Slagle,  J.R.     (1971)  Artificial  Intelligence:     The Heuristic
	Programming 	Approach. New York:McGraw-Hill.

Winograd, Terry (1971) Procedures as a Representation for Data  in  a
        Computer Program for Understanding Natural Language.   Ph.D.
	Thesis, M.I.T.

Winograd, T. and Sussman, G.J. (1971) Micro-Planner Reference Manual
	(available at the A.I. Project)