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AIList Digest Volume 1 Issue 097
AIList Digest Monday, 14 Nov 1983 Volume 1 : Issue 97
Today's Topics:
Pattern Recognition - Vector Fields,
Psychology - Defense,
Ethics - AI Responsibilities,
Seminars - NRL & Logic Specifications & Deductive Belief
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Date: Sun, 13 Nov 83 19:25:40 PST
From: Philip Kahn <v.kahn@UCLA-LOCUS>
Subject: Need references in field of spatial pattern recognition
This letter to AI-LIST is a request for references from all
of you out there that are heavily into spatial pattern recognition.
First let me explain my approach, then I'll hit you with my
request. Optical flow and linear contrast edges have been getting a
lot of attention recently. Utilizing this approach, I view a line
as an ordered set of [image] elements; that is, a line is comprised of a
finite ordered set of elements. Each element of a line is treated
as a directed line (a vector with direction and magnitude).
Here's what I am trying to define: with such a definition
of a line, it should be possible to create mappings between lines
to form fairly abstract ideas of similarity between lines. Since
objects are viewed as a particular arrangement of lines, this analysis
would suffice in identifying objects as being alike. Some examples,
the two lines possessing the most similarities (i.e.,
MAX ( LINE1 .intersection. LINE2 ) ) may be one criterion of comparison.
I'm looking for any references you might have on this area.
This INCLUDES:
1) physiology/biology/neuroanatomy articals dealing with
functional mappings from the ganglion to any level of
cortical processing.
2) fuzzy set theory. This includes ordered set theory and
any and all applications of set theory to pattern recognition.
3) any other pertinent references
I would greatly appreciate any references you might provide.
After a week or two, I will compile the references and put them
on the AI-LIST so that we all can use them.
Viva la effort!
Philip Kahn
[My correspondence with Philip indicates that he is already familiar
with much of the recent literature on optic flow. He has found little,
however, on the subject of pattern recognition in vector fields. Can
anyone help? -- KIL]
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Date: Sun, 13 Nov 1983 22:42 EST
From: HEWITT%MIT-OZ@MIT-MC.ARPA
Subject: Rational Psychology [and Reply]
Date: 28 Sep 83 10:32:35-PDT (Wed)
To: AIList at MIT-MC
From: decvax!duke!unc!mcnc!ncsu!fostel @ Ucb-Vax
Subject: RE: Rational Psychology [and Reply]
... Is psychology rational?
Someone said that all sciences are rational, a moot point, but not that
relevant unless one wishes to consider Psychology a science. I do not.
This does not mean that psychologists are in any way inferior to chemists
or to REAL scientists like those who study physics. But I do think there
....
----GaryFostel----
This is an old submission, but having just read it I felt compelled to
reply. I happen to be a Computer Scientist, but I think
Psychologists, especially Experimental Psychologists, are better
scientists than the average Computer "Scientist". At least they have
been trained in the scientific method, a skill most Computer
Scientists lack. Just because Psychologist, by and large, cannot defend
themselves on this list is no reason to make idle attacks with only
very superficial knowledge on the subject.
Fanya Montalvo
------------------------------
Date: Sun 13 Nov 83 13:14:06-PST
From: David Rogers <DRogers@SUMEX-AIM.ARPA>
Subject: just a reminder...
Artificial intelligence promises to alter the world in enormous ways during our
lifetime; I believe it's crucial for all of us to look forward to the effects
our our work, both individually and collectively, to make sure that it will be
to the benefit of all peoples in the world.
It seems to be tiresome to people to remind them of the incredible effect that
AI will have in our lifetimes, yet the profound mature of the changes to the
world made by a small group of researchers makes it crucial that we don't treat
our efforts casually. For example, the military applications of AI will dwarf
that of the atomic bomb, but even more important is the fact that the atomic
bomb is a primarily military device, while AI will impact the world as much (if
not more) in non-military domains.
Physics in the early part of this century was at the cutting edge of knowledge,
similar to the current place of AI. The culmination of their work in the atomic
bomb changed their field immensely and irrevocably; even on a personal level,
researchers in physics found their lives greatly impacted, often shattered.
Many of the top researchers left the field.
During our lifetimes I think we will see a similar transformation, with the
"fun and games" of these heady years turning into a deadly seriousness, I think
we will also see top researchers leaving the field, once we start to see some
of our effects on the world. It is imperative for all workers in this field to
formulate and share a moral outlook on what we do, and hope to do, to the
world.
I would suggest we have, at the minimum, a three part responsibility. First, we
must make ourselves aware of the human impact of our work, both short and long
term. Second, we must use this knowledge to guide the course of our research,
both individually and collectively, rather than simply flowing into whatever
area the grants are flowing into. Third and most importantly, we must be
spokespeople and consciences to the world, forcing others to be informed of
what we are doing and its effects. Researches who still cling to "value-free"
science should not be working in AI.
I will suggest a few areas we should be thinking about:
- Use of AI for offensive military use vs. legitimate defense needs. While the
line is vague, a good offense is surely not always the best defense.
- Will the work cause a centralization of power, or cause a decentralization of
power? Building massive centers of power in this age increases the risk of
humans dominated by machine.
- Is the work offering tools to extend the grasp of humans, or tools to control
humans?
- Will people have access to the information generated by the work, or will the
benefits of information access be restricted to a few?
Finally, will the work add insights into ourselves a human beings, or will it
simply feed our drives, reflecting our base nature back at ourselves? In the
movie "Tron" an actor says "Our spirit remains in each and every program we
wrote"; what IS our spirit?
David
------------------------------
Date: 8 Nov 1983 09:44:28-PST
From: Elaine Marsh <marsh@NRL-AIC>
Subject: AI Seminar Schedule
[I am passing this along because it is the first mention of this seminar
series in AIList and will give interested readers the chance to sign up
for the mailing list. I will not continue to carry these seminar notices
because they do not include abstracts. -- KIL]
U.S. Navy Center for Applied Research
in Artificial Intelligence
Naval Research Laboratory - Code 7510
Washington, DC 20375
WEEKLY SEMINAR SERIES
14 Nov. 1983 Dr. Jagdish Chandra, Director
Mathematical Sciences Division
Army Research Office, Durham, NC
"Mathematical Sciences Activities Relating
to AI and Its Applications at the Army
Research Office"
21 Nov. 1983 Professor Laveen Kanal
Department of Computer Science
University of Maryland, College Park, MD
"New Insights into Relationships among
Heuristic Search, Dynamic Programming,
and Branch & Bound Procedures"
28 Nov. 1983 Dr. William Gale
Bell Labs
Murray Hill, NJ
"An Expert System for Regression
Analysis: Applying A.I. Ideas in
Statistics"
5 Dec. 1983 Professor Ronald Cole
Department of Computer Science
Carnegie-Mellon University, Pittsburgh, PA
"What's New in Speech Recognition?"
12 Dec. 1983 Professor Robert Haralick
Department of Electrical Engineering
Virginia Polytechnic Institute, Blacksburg, VA
"Application of AI Techniques to the
Interpretation of LANDSAT Scenes over
Mountainous Areas"
Our meeting are usually held Monday mornings at 10:00 a.m. in the
Conference Room of the Navy Center for Applied Research in Artificial
Intelligence (Bldg. 256) located on Bolling Air Force Base, off I-295,
in the South East quadrant of Washington, DC.
Coffee will be available starting at 9:45 a.m.
If you would like to speak, or be added to our mailing list, or would
just like more information contact Elaine Marsh at marsh@nrl-aic
[(202)767-2382].
------------------------------
Date: Mon 7 Nov 83 15:20:15-PST
From: Sharon Bergman <SHARON@SU-SCORE.ARPA>
Subject: Ph.D. Oral
[Reprinted from the SU-SCORE bboard.]
Ph.D. Oral
COMPILING LOGIC SPECIFICATIONS FOR PROGRAMMING ENVIRONMENTS
November 16, 1983
2:30 p.m., Location to be announced
Stephen J. Westfold
A major problem in building large programming systems is in keeping track of
the numerous details concerning consistency relations between objects in the
domain of the system. The approach taken in this thesis is to encourage the
user to specify a system using very-high-level, well-factored logic
descriptions of the domain, and have the system compile these into efficient
procedures that automatically maintain the relations described. The approach
is demonstrated by using it in the programming environment of the CHI
Knowledge-based Programming system. Its uses include describing and
implementing the database manager, the dataflow analyzer, the project
management component and the system's compiler itself. It is particularly
convenient for developing knowledge representation schemes, for example for
such things as property inheritance and automatic maintenance of inverse
property links.
The problem description using logic assertions is treated as a program such as
in PROLOG except that there is a separation of the assertions that describe the
problem from assertions that describe how they are to be used. This
factorization allows the use of more general logical forms than Horn clauses as
well as encouraging the user to think separately about the problem and the
implementation. The use of logic assertions is specified at a level natural to
the user, describing implementation issues such as whether relations are stored
or computed, that some assertions should be used to compute a certain function,
that others should be treated as constraints to maintain the consistency of
several interdependent stored relations, and whether assertions should be used
at compile- or execution-time.
Compilation consists of using assertions to instantiate particular procedural
rule schemas, each one of which corresponds to a specialized deduction, and
then compiling the resulting rules to LISP. The rule language is a convenient
intermediate between the logic assertion language and the implementation
language in that it has both a logic interpretation and a well-defined
procedural interpretation. Most of the optimization is done at the logic
level.
------------------------------
Date: Fri 11 Nov 83 09:56:17-PST
From: Sharon Bergman <SHARON@SU-SCORE.ARPA>
Subject: Ph.D. Oral
[Reprinted from the SU-SCORE bboard.]
Ph.D. Oral
Tuesday, Nov. 15, 1983, 2:30 p.m.
Bldg. 170 (history corner), conference room
A DEDUCTIVE MODEL OF BELIEF
Kurt Konolige
Reasoning about knowledge and belief of computer and human agents is assuming
increasing importance in Artificial Intelligence systems in the areas of
natural language understanding, planning, and knowledge representation in
general. Current formal models of belief that form the basis for most of these
systems are derivatives of possible- world semantics for belief. However,,
this model suffers from epistemological and heuristic inadequacies.
Epistemologically, it assumes that agents know all the consequences of their
belief. This assumption is clearly inaccurate, because it doesn't take into
account resource limitations on an agent's reasoning ability. For example, if
an agent knows the rules of chess, it then follows in the possible- world model
that he knows whether white has a winning strategy or not. On the heuristic
side, proposed mechanical deduction procedures have been first-order
axiomatizations of the possible-world belief.
A more natural model of belief is a deductions model: an agent has a set of
initial beliefs about the world in some internal language, and a deduction
process for deriving some (but not necessarily all) logical consequences of
these beliefs. Within this model, it is possible to account for resource
limitations of an agent's deduction process; for example, one can model a
situation in which an agent knows the rules of chess but does not have the
computational resources to search the complete game tree before making a move.
This thesis is an investigation of Gentzen-type formalization of the deductive
model of belief. Several important original results are proven. Among these
are soundness and completeness theorems for a deductive belief logic; a
corespondence result that shows the possible- worlds model is a special case of
the deduction model; and a model analog ot Herbrand's Theorem for the belief
logic. Several other topics of knowledge and belief are explored in the thesis
from the viewpoint of the deduction model, including a theory of introspection
about self-beliefs, and a theory of circumscriptive ignorance, in which facts
an agent doesn't know are formalized by limiting or circumscribing the
information available to him. Here it is!
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End of AIList Digest
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