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C00003 00002 indus[e82,jmc] Industrial Lectureship in Computer Science
C00014 00003 Spring 1983 Industrial Lectureship
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C00025 00005 indust[w85,jmc] 1985-86 Industrial Lecturers
C00032 00006 indust[e86,jmc] Industrial lecturers 87-88
C00034 00007 Here are the two course descriptions. I hope to have the other Monday.
C00037 00008 1987-88 Industrial lecturers
C00042 00009 Suggestions for industrial lecturers made at 1987 Jan 16 ai faculty meeting
C00043 00010 ∂05-Jan-88 1817 jcm@navajo.stanford.edu Industrial Lectureship
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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?
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Spring 1983 Industrial Lectureship
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.
The Spring 1983 course is as follows. Watch for an announcement
of the first meeting. The 1983-84 courses have been determined, and they
will be in the regular Stanford Courses and Degrees.
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
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industry lecturers
Here are the Industrial Lecture Courses for 1984-85.
They are numbered CS400A, B and C. Each course will be
given by the named computer scientist from industry.
Each year there is a new group of industrial lecturers,
and the courses are not expected to be repeated.
Clarence (Skip) Ellis (Xerox PARC)
Office Information Systems Design.
Technology, techniques, and design paradigms of electronic office
information systems. The objective is to present a coherent and cohesive
foundation for the understanding and analysis of office systems and
their implementation. Topics include: basic components and media such as
word processors, workstations, PBXs, and local area networks; office
firmware such as RasterOps, virtual keyboards, phone handlers, and
window managers; office system elements such as document editors, mail
systems, calendaring systems, and distributed servers. The course will
describe and discuss issues of user interfaces, user programming, office
modeling, and the social / organizational structures within which the
technology must exist. Prerequisites: computer organization (e.g.
cs111,cs112), computer software (e.g. cs142,cs146).
Fall 84 only.
Joe Halpern (IBM San Jose)
400B Reasoning about Knowledge. Formal Systems for modeling aspects
of reasoning about knowledge, such as modal logic, nonmonotonic logic and
relevance logic will be considered. Discussions will address to what
extent these approaches can be used to deal with such problems as
reasoning in the presence of inconsistency, belief revision, and
knowledge representation. Familiarity with mathematical reasoning and
first-order logic will be assumed.
Winter quarter (Halpern) by arrangement.
Richard Waldinger (SRI International)
Seminar in Program Synthesis:
Recent research on the systematic derivation of programs
to meet given specifications, with an emphasis on deductive
approaches. Related topics in theorem proving, logic prog-
gramming, planning, and program transformation. Individual
projects and some student presentations.
Prerequisites: CS157 A/B or equivalent.
Spring 85 only.
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indust[w85,jmc] 1985-86 Industrial Lecturers
309. Industrial Lectureships in Computer Science ---
Each quarter the Computer Science Department invites one outstanding computer
scientist from the local industry to give a course in his or her
specialty. These courses (309A,B,C) are ordinarily given only once.
Lecturers and topics change from year to year, hence courses with this
number may be taken repeatedly.
The lecturers for 1985-86 are as follows:
Fall: Fernando Pereira of SRI International. His work in Portugal,
UK and now at SRI is well described by the course he is giving.
Winter: John Williams of the IBM Research Laboratory in San Jose.
He has collaborated with John Backus in developing ``functional
programming'' about which he will lecture.
Spring: Daniel Bobrow et. al. The Xerox Palo Alto Research Center
has been active in AI, and one of their major areas of activity
has been AI languages. The approach taken by this group is
distinct from that of the other PARC emphasis on object oriented
programming as in Smalltalk.
309A. Prolog and Natural Language Analysis ---
Introduces the logic programming language Prolog as a tool for
natural language analysis and related topics in artificial
intelligence, through a progression of natural language analysis
examples. No previous experience with logic programming or natural
language analysis is required. The following topics will be
discussed: representing context-free grammars in Prolog; definite
clause grammars; the logical variable; difference lists; top-down
parsing and the Prolog execution model; syntactic analysis of complex
constructions; semantic translation rules and logical form; general
computations in grammars; structure manipulation and multistage
analysis; operations on logical forms; deductive question-answering in
Prolog; metalevel computation and the embedding grammar formalisms in
Prolog; extralogical operations; implementation of alternative parsing
algorithms; the organization of a natural-language question-answering
system. Examples will be available as running Prolog programs and
will be used for exercises. Prerequisites: elementary notions of
logic, formal language theory, and symbolic computation.
3 units, Aut (Pereira)
309B. Functional Programming -- Current research topics in the design and
implementation of functional programming languages, including formal
semantic models, rewriting rules and the algebra of programs, abstract
data types, program transformations, infinite sequences, and the use of
stream-valued stream functions to accommodate persistent memory and
interactive input/output. The particular language FP will be studied in
depth, with examples drawn from other functional languages such as SASL,
ML, KRC, and Hope. Prerequisite: a graduate-level course in programming
languages.
3 units, Win (Williams)
390C. Programming Languages for AI Systems.---The
design of programming languages to provide computational
mechanisms for AI research and expert systems. Topics include
object-oriented and access-oriented programming; logic programming;
unification algorithms; representation of dependencies, contexts and
layers; representations of assumptions; algorithms for truth
maintenance; constraints; meta-circular interpreters; architectures
for reflection. Prerequisites: Working familiarity with LISP. Bobrow,
de Kleer, Kahn, Mittal, and Stefik. (Spring 1986.)
3 units, Spr (Bobrow, de Kleer, Kahn, Mittel, Stefik)
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indust[e86,jmc] Industrial lecturers 87-88
Brad Allen
John Sowa, IBM, pushed by Gio Wiederhold
Cynthia Dwork, IBM Almaden, distributed computation, I promised Ernst Mayr
Dan Green, xerox, theory student
Here are the two course descriptions. I hope to have the other Monday.
If we can delay determining the quarters until I have the other lecturer,
it will be easier to accomodate the last guy, who may be harder to please.
Strong: (408) 927-1758
Nelson: phone him at Digital Equipment Corp. Western Research Laboratories
in Palo Alto.
∂13-Feb-86 1006 STRONG@IBM-SJ.ARPA
Received: from IBM-SJ.ARPA by SU-AI.ARPA with TCP; 13 Feb 86 10:06:07 PST
Date: 13 Feb 86 10:00:19 PST
From: STRONG@IBM-SJ.ARPA
To: jmc@su-ai
Dear Professor McCarthy,
Following is an abbreviated course description. I hope this fits
your requirments.
Sincerely,
Ray Strong
FAULT TOLERANT DISTRIBUTED SYSTEMS
Requirements and solutions to problems arising in the
context of distributed systems that must tolerate faults.
Special emphasis: atomic broadcast and clock
synchronization. Design decisions for a prototype
distributed system that reaches, maintains, and recovers
from failure to maintain agreement. Course organized around
a series of problems of varying difficulty that students are
challenged to solve, including some problems that are still
open.
From: gnelson@decwrl.DEC.COM (Greg Nelson)
Title: Methods for program verification
Description:
An introduction to practical methods for writing difficult programs
without errors. Starting with axiomatic semantics, the predicate
calculus, and E. W. Dijkstra's theory of predicate transformers, the
course will lead into a series of example programs that will be derived
using the methods. Additional topics, to be covered if time permits,
include mechanical theorem proving techniques, constraint languages,
and compiler correctness.
Nelson: Fall quarter
Strong: Winter quarter
Smith: Spring
1987-88 Industrial lecturers
From: Sowa John <SOWA@ibm.com>
One-sentence bio: John Sowa is a member of the IBM Systems Research
Institute where he teaches courses on artificial intelligence and
does research in computational linguistics.
John Sowa
====================================================================
Course 309a: Conceptual Structures
John F. Sowa
Description: Problems and issues in knowledge representation and
the semantics of natural languages. Theory of conceptual graphs.
Structure of the lexicon, canonical graphs for English word classes,
logical forms for various features, including quantifiers,
relative clauses, anaphora, tenses, and contexts.
Schemata and their use in word sense determination, metaphor,
and definitions by family resemblances.
Relationships to Montague grammar, situation semantics, game
theoretical semantics, and discourse representation theory.
Conceptual analysis as a basis for knowledge engineering.
Prerequisites: Knowledge of first-order logic and natural language
syntax.
---------------------------
Weekly seminar, Spring 1988, Paul V. Haley
One-sentence bio: Paul Haley is a chief scientist for Inference Corp. He
is one of the designers of ART. He worked at Carnegie-Mellon University
on several of the DEC expert systems.
Rule-based System Architecture:
Data-driven and contol flow inference engines; the complexity of pattern
matching; the Rete Algorithm.
Subgoaling; reasoning with simultaneous goals; opportunistic backward chaining;
subsumption versus unification.
Propositions; semantic properties of relations; the propositional equivalence and
logic of frames.
Rule independence, evolution and maintenance.
Logical deduction; opportunistic and demand-driven implications; open versus closed
world assumptions; non-monotonicity, soundness and the asynchronous arrival of
information; logical dependencies and the closed-world assumption. Assumptive
truth maintenance; monotonic implementations of non-monotonic logic.
Efficiency of rule-based systems; data driven "query" optimization; real-time
knowledge-based systems; cooperating knowledge-based systems; parallel
inference machines.
---------------------------
Cynthia Dwork
Title for my course: New Directions in Distributed Computing
Cryptographic protocols; interactive proof systems;
zero knowledge and minimum knowledge proofs;
applications of cryptographic and minimimum knowledge techniques
to distributed computing.
Cynthia Dwork, of IBM Almaden Research Center, works in the theory of
parallel and distributed computation.
Paul Haley
412 931-7600, Intelligent technology
215 947-4455, Huntington Valley
307 Hill st. Swickley, PA
Suggestions for industrial lecturers made at 1987 Jan 16 ai faculty meeting
Dick Duda, Gary Hendrix, Reid Smith
∂05-Jan-88 1817 jcm@navajo.stanford.edu Industrial Lectureship
Received: from NAVAJO.STANFORD.EDU by SAIL.STANFORD.EDU with TCP; 5 Jan 88 18:17:35 PST
Received: by navajo.stanford.edu; Tue, 5 Jan 88 18:13:25 PST
Date: Tue, 5 Jan 88 18:13:25 PST
From: John Mitchell <jcm@navajo.stanford.edu>
Subject: Industrial Lectureship
To: JMC@sail.stanford.edu
I think Luca Cardelli from DEC SRC would be a worthwhile
person for one of these lectureships. His area of expertise
is in design and implementation of functional programming
languages, and I suspect a course on pragmatic issues
would have a fairly wide interest.
I don't know yet whether he would be interested. What is involved
in nominating someone?
John