COMMENT ⊗   VALID 00002 PAGES
C REC  PAGE   DESCRIPTION
C00001 00001
C00002 00002	indus[e82,jmc]		Industrial Lectureship in Computer Science
C00012 ENDMK
C⊗;
indus[e82,jmc]		Industrial Lectureship in Computer Science

			ANNOUNCEMENT

INDUSTRIAL LECTURESHIP IN COMPUTER SCIENCE

	The Computer Science Department of Stanford University
is pleased to announce the Industrial Lectureship in Computer
Science and Engineering starting in Spring Quarter 1983.
The purpose of the lectureship is to increase interaction between
Computer Science Department faculty and students and computer scientists in local
industry.

	Each quarter the Computer Science Department will invite
one outstanding computer scientist from the local industry to give
a course in his specialty.  Office space, computer use and salary
appropriate to the teaching of one course will be provided.  It is
expected that the balance of the lecturer's salary will be paid by
his permanent employer.

	Recommendations or applications
should be addressed to the Chairman of the Department, Professor
Gene Golub.

∂14-Jan-83  1501	Bob Moore <BMOORE at SRI-AI> 	visiting industrial lectureship    
Date: 14 Jan 1983 1502-PST
From: Bob Moore <BMOORE at SRI-AI>
Subject: visiting industrial lectureship
To: jmc at SU-AI
cc: nilsson at SRI-AI, grosz at SRI-AI, bmoore at SRI-AI

John,

Here are the course descriptions for the SRI AI Center submissions.
Sandy Pentland and Steve Barnard are prepared to teach this spring.  I
would prefer to wait until next fall, and of course, Stan will not be
back from Israel until then.  I believe Barbara Grosz is also going to
give copies of these course descriptions to Gene Golub.

--Bob

-----------------------------------------------------------------------

                  COMPUTATIONAL APPROACHES TO VISION

                  Alex Pentland and Stephen Barnard
                    Artificial Intelligence Center
                          SRI International

Vision may be studied as a problem in physics, psychology, physiology
or as a computational problem.  Recently, research in computational
vision has attempted to take greater advantage of these other
paradigms, and so has gone in directions which are somewhat separate
from ``mainstream'' artificial intelligence research.  In particular,
more emphasis has been placed on data concerning biological vision and
on mathematical models of image formation.  This seminar will examine
representative examples of these approachs and will explore how, and
to what extent, research in computer vision can take advantage of
these other paradigms.  The initial portion of the seminar will
attempt to provide the student with a sophisticated, albiet
necessarily superficial, grasp of human visual psychophysics and
visual neurophysiology.

Qualifications:

 Alex Pentland:
  * Computer Scientist, in vision research, SRI AI Center.
  * Phd Psychology, MIT (1982), in conjunction with MIT AI Lab (Marr's
	vision group).  
  * Assisted in teaching computer vision seminar at MIT during 3 terms.	
    co-taught course entitled ``Psychophysics And Neurophysiology'' in
      MIT psychology dept.	
  * 10 publications and papers in area of human perception.  I have 
	fairly extensive knowledge of current neurophysiology through
	association with the Schiller lab at MIT.
  * 21 publications and papers in various types of computer vision
	 (primarily AI and remote sensing) over the last 10 years, 
	while at MIT, Arthur D. Little and Environmental Research
	Institute of Michigan (ERIM).

 Steven Barnard:
  * Senior Computer Scientist, vision research, SRI AI Center
  * PhD Computer Science, University of Minnesota (1979)
  * Many quarters of teaching basic computer science courses
  * Several publications in computer vision

-------------------------------------------------------------------------

          THEORETICAL ASPECTS OF ROBOT COGNITION AND ACTION

                           Stan Rosenschein
                    Artificial Intelligence Center
                          SRI International

This course will review fundamental theoretical problems in the design
of artifacts which sense and affect complex environments. The focus of
the course will be on the use of concepts from symbolic logic and
theoretical computer science to rigorously characterize the notion of
a rational cognitive agent.  In particular, the course will
investigate the role of knowledge, belief, desire, intention,
planning, and action from several points of view: (1) their formal
properties as studied in idealized models abstracted from common
sense, (2) their respective roles in allowing an organism to carry out
complex purposive behavior, and (3) various suggested computational
realizations. The course will attempt to unify these topics, suggest
directions for an integrated theory of robot action, and indicate how
such a theory might be applied to concrete problems in AI.

---------------------------------------------------------------------------

                REPRESENTATION, MEANING, AND INFERENCE

                           Robert C. Moore
                    Artificial Intelligence Center
                          SRI International


The problem of the formal representation of knowledge in intelligent
systems is subject to two important constraints.  First, a general
knowledge-representation formalism must be sufficiently expressive to
represent a wide variety of information about the world.  A long-term
goal here is the ability to represent anything that can be expressed
in natural language.  Second, the system must be able to draw
inferences from the knowledge represented.  In this course we will
examine the knowledge representation problem from the perspective of
these constraints.  We will survey techniques for automatically
drawing inferences from formalizations of commonsense knowledge; we
will look at some of the aspects of the meaning of natural-language
expressions that seem difficult to formalize (e.g., tense and aspect,
collective reference, propositional attitudes); and we will consider
some ways of bridging the gap between formalisms for which the
inference problem is fairly well understood (first-order predicate
logic) and the richer formalisms that have been proposed as meaning
representations for natural language (higher-order logics, intentional
and modal logics).
-------

Was that Adrian Rich from IBM San Jose?