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AIList Digest Volume 2 Issue 129
AIList Digest Tuesday, 2 Oct 1984 Volume 2 : Issue 129
Today's Topics:
Bindings - Syntelligence Address Correction,
Induction - Induction on One Case,
Machine Translation - Sanskrit,
Humor - Onyx BC8820 Stone Block Reader,
Seminar - Learning in SOAR,
Conference - Knowledge-Based Command and Control
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Date: 01 Oct 84 1144 PDT
From: Russell Greiner <RDG@SU-AI.ARPA>
Subject: Syntelligence: Address Correction
Syntelligence, an AI company specializing in building
expert systems for business applications, has just moved.
Its new address and phone number are
Syntelligence
1000 Hamlin Court [not 100]
PO Box 3620
Sunnyvale, CA 94088
(408) 745-6666
Dr Peter Hart, its president, can also be reached as
HART@SRI-AI.arpa. (This net address should only be used for
professional (e.g., AAAI related) reasons.)
------------------------------
Date: Mon 1 Oct 84 14:10:23-MDT
From: Stan Shebs <SHEBS@UTAH-20.ARPA>
Subject: Re: Induction on One Case
(My my, people seem to get upset, even when I think I'm making
noncontroversial statements...)
It wasn't clear whether Tom Dietterich (and maybe others) understood
my remark on induction. I was merely pointing out that "induction on
one case" is indistinguishable from "generalization". Simple-minded
generalization IS easy. Suppose I have as input a Lisp list (A B),
(presumably the first in a stream), and I tell my machine to create
some hypotheses about what it expects to see next. Possible hypotheses
are:
(A B) - the machine expects to see (A B) forever
(?X B) - the machine expects to see 2nd element B
(A ?X) - similarly
(?X ?Y) - 2-element lists
Since these are lists, presumably one could get more elaborate...
(?X ?Y optional ?Z)
...
And end up with "the most general hypothesis":
?X
All of these patterns can be produced just by knowing how to form
Lisp lists; I don't think there's any hidden assumptions or biases
(please enlighten me if there are). I would say that in general,
one can exhaustively generate all hypotheses, when the domains
are completely specified (i.e. a pattern like (<or A B> B) for the
above example has an undefined entity "or" which has nothing to do
with Lisp lists; one would have to extend the domains in which one
is operating). Generating hypotheses in a more reasonable order is
completely domain-dependent (and no general theory is known).
Getting back to the example, all of the hypotheses are equally
plausible, since there is only one case to work from (unless one
wants to arbitrarily rank these hypotheses somehow; but none can
be excluded at this point).
I agree that selecting representations is very hard; there's not
even any consensus about what representations are useful, let alone
about how to select an appropriate one in particular cases.
(Have I screwed up anywhere in this? I really wasn't intending
to flame...)
stan shebs
------------------------------
Date: 1 Oct 1984 16:01-PDT (Monday)
From: Rick Briggs <briggs@RIACS.ARPA>
Subject: Sanskrit
In response to the flood of messages I recieved concerning the
ambiguity-free natural language, here is some more information about it.
The language is a branch of Sastric Sanskrit which flourished
between the 4th century B.C and 4th century A.D., although its
beginnings are somewhat older. That it is unambiguous is without
question. (I am writing two papers, one for laymen and one for those with
AI background). A more interesting question is one posed by Dr. Michael
Dyer, that is "is it a natural language?".
The answer is yes, it is natural and it is unambiguous. It
would be difficult to call a language living and spoken for over a
millenium with as rich a literature as this langauge has anything but a
natural language. The problem is that most (maybe all) of us are used
to languages like English (one of the worst) or other languages which
are so poor as vehicles of transmission of logical data. We have
assumed that since all languages known have ambiguity, that it is
a necessary property of natural languages, but there is no reason to
make this assumption. The complaint that it is awkward to speak
with the precision required to rule out ambiguity is one based on
(I would guess) the properties of Engish or other common Indo-European
languages.
If one were to take a specific formulation such as a semantic
net and "read" it in English the result is a cumbersome mass of
detail which nobody would be willing to use in ordinary communication.
However, if one were to take that same semantic net and translate it
into the language I am studying you get (probably) one very long word
with a series of affixes which convey very compactly the actual meaning
of the semantic net. In other words, translations from this language
to English are of the same nature as those from a semantic net to
English (hence the equivalence to semantic nets), one compact structure
to a long paragraph.
The facility and ease with which these Indians communicated
indicates that it is possible for a natural language to serve all
purposes of artificial languages based on logic. If one could say
what one wishes to say with absolute clarity (although with apparent
redundancy) in the same time and with the same ease as you say
part of what you mean in English, why not do so? And if a population
actually got used to talking in this way there would be much more
clarity and less confusion in our communication. Sastric Sanskrit
allows you to say WHAT YOU MEAN without effort. The questions
"Can you elaborate on that?" or "What exactly are you trying to say?"
would simply not come up unless the hearer wished to go to a deeper
level of detail.
This language was used in much the same way as language found
in technical journals today. Scientists would communicate orally
and in writing in this language. It is certainly a natural language.
As to how this is accomplished, basically SYNTAX IS ELIMINATED.
Word order is unimportant, speaking is thus comparable to adding a
series of facts to a data-base.
What interests me about this language is:
1) Many theories derived recently in Linguistics and AI were
independently in use over a thousand years ago, without
computers or any need to eliminate ambiguity except for
precise thinking and communication
2) A natural language can serve as a mathematical (or artificial
language) and thus the dichotomy between the two is false.
3) There are methods for translating "regular" Sanskrit into
Sastric Sanskrit from which much could be learned from NLP
research.
4) The possibilities of this language serving as interlingua
for MT.
There are no translated texts and it takes Sanskrit experts a
very long time to analyze the texts, so a translation of a full work
in this language is a way off. However, those interested can get
a hold of "Vaiyakarana-Siddhanta-Laghu-Manjusa" by Nagesha Bhatta.
Rick Briggs
NASA Ames
------------------------------
Date: Thu, 27 Sep 84 16:05:37 edt
From: Walter Hamscher <walter@mit-htvax>
Subject: Onyx BC8820 Stone Block Reader
[Forwarded from the MIT bboard by Laws@SRI-AI.]
Professor Petra Hechtman of the Archaeology Dept has an Egyptian
tombstone written in Hieroglyphs on an Onyx C8002 system running
ONYX IV.II that he needs to read. The Onyx system that the
block was written with has died (legend has it that it is archived
in the temple of Tymsharin). He needs to get the data off the
rock soon so that the exact date of Graduate Student Lunches can
be calculated (the most recent prediction fixes the date of the
next "bologna eclipse" as Friday the 28th at noon in the Third Floor
Playroom, hosted by David "Saz" Saslov and Mike "Mpw" Wellman).
According to Data Gene-rock, the original Filer was 1/4 cubit,
6250 spd (strokes per digit), 90 RAs, up to 10K BC. Anyone who has,
knows of, or has chips off the original device that might be
able to decipher the stone, please contact Prof. Hechtman at
x5848, or at /dev/null@mit-htvax.
------------------------------
Date: Mon 1 Oct 84 10:25:14-PDT
From: Paula Edmisten <Edmisten@SUMEX-AIM.ARPA>
Subject: Seminar - Learning in SOAR
[Forwarded from the Stanford SIGLUNCH distribution by Laws@SRI-AI.]
DATE: Friday, October 5, 1984
LOCATION: Chemistry Gazebo, between Physical and Organic Chemistry
TIME: 12:05
SPEAKER: Paul S. Rosenbloom
Assistant Professor
ABSTRACT: Towards Chunking as a General Learning Mechanism
Chunks have long been proposed as a basic organizational unit for
human memory. More recently chunks have been used to model human
learning on simple perceptual-motor skills. In this talk, I will
present recent progress in extending chunking to be a general learning
mechanism by implementing it within a general problem solver.
Combining chunking with the SOAR problem-solving architecture
(described by John Laird in the SigLunch of September 28) we can take
significant steps toward a general problem solver that can learn about
all aspects of its own behavior. The combination of a simple learning
mechanism (chunking) with a sophisticated problem-solver (SOAR)
yields: (1) practice speed-ups, (2) transfer of learning between
related tasks, (3) strategy acquisition, (4) automatic
knowledge-acquisition, and (5) the learning of general macro-operators
of the type used by Korf (1983) to solve Rubik's cube. These types of
learning are demonstrated for traditional search-based tasks, such as
tic-tac-toe and the eight puzzle, and for R1-SOAR (a reformulation of
a portion of the R1 expert system in SOAR).
This work has been pursued in collaboration with John Laird (Xerox
PARC) and Allen Newell (Carnegie-Mellon University).
------------------------------
Date: 24 Sep 1984 18:13-EDT
From: ABN.CJMERRICK@USC-ISID.ARPA
Subject: Conference - Knowledge-Based Command and Control
SYMPOSIUM & EXHIBITION ON "ARTIFICIAL
INTELLIGENCE" TO BE HELD IN
KANSAS CITY, MISSOURI
"THE ROLE OF KNOWLEDGE BASED SYSTEMS
IN COMMAND & CONTROL"
SPONSORED BY:
KANSAS CITY CHAPTER OF AFCEA
OCTOBER 17-19, 1984
The Kansas City Chapter of the Armed Forces Communications and
Electronics Association is proud to announce that it is sponsoring
its Second Annual Symposium and Exhibition to discuss the applicability
of artificial intelligence and knowledge based systems to command and
control requirements, in both the military and commercial environments.
The Symposium will be enhanced by the presence of hardware and
software exhibits, representing advances in technology related to the
theme.
Highlights of the Symposium will include noted individuals such
as Dr. Joseph V. Braddock of the BDM Corporation addressing user
perspectives of utilizing knowledge based systems to fulfill command
and control needs. Dr. Robert W. Milne of the Air Force Institute
of Technology will address AI technology and its application to
command and control.
A luncheon presentation will be given by Lieutenant General
Carl E. Vuono, Commander, Combined Arms Center, Fort Leavenworth
and Deputy Commander, Training and Doctrine Command.
General Donn A. Starry (Ret), Vice President and General Manager,
Space Missions Group of Ford Aerospace and Communications Corporation
will be the guest speaker following the evening meal on Thursday.
The Symposium and Exhibition will be held over a three-day
period commencing with an opening of the exhibit area and a cocktail
and hors d'oeuvres social on October 17, 1984. Technical sessions
will begin at 8:00 a.m. on October 18. The format of the technical
presentation will consist of two high intensity panel discussions,
a session in which pertinent papers will be presented and two guest
lectures.
ABBREVIATED AGENDA
WEDNESDAY, 17 OCTOBER 1984
1200-1700 Check in & Registration
1700-1900 Welcome Social & Exhibits Open
THURSDAY, 18 0CTOBER 1984
0800-1145 SESSION I - Panel Discussion: "Status and Forecast of
of AI Technology as it applies to Command and Control"
Panel Moderator:
Mr. Herbert S. Hovey, Jr.
Director, U.S. Army Signals Warfare Laboratory
Vint Hill Farms Station
Warrenton, Virginia 22186
1145-1330 Luncheon/Guest Speaker:
Lieutenant General Carl E. Vuono
Commander, U.S. Army Combined Arms Center
Deputy Commander, Training and Doctrine Command
Fort Leavenworth, Kansas 66207
1330-1700 SESSION II - Presentation of Papers
1700-1830 Social Hour
1830-2030 Dinner/Evening Speaker:
General Donn A. Starry (Ret)
Vice President & General Manager
Space Missions Group of Ford Aerospace and
Communications Corporation
FRIDAY, 19 OCTOBER 1984
0800-1200 SESSION III - Panel Discussion: "User Perspectives of
Pros and Cons of Knowledge Based Systems in Command and
Control"
Panel Moderator:
Brigadier General David M. Maddox
Commander, Combined Arms Operations Research Activity
Fort Leavenworth, Kansas 66027
To make reservations or for further information, write or call:
AFCEA SYMPOSIUM COMMITTEE
P.O. Box 456
Leavenworth, Kansas 66048
(913) 651-7800/AUTOVON 552-4721
MILITARY POC IS:
CPT (P) CHRIS MERRICK
CACDA, C3I DIRECTORATE
FORT LEAVENWORTH, KANSAS 66027-5300
AUTOVON: 552-4980/5338
COMMERCIAL: (913) 684-4980/5338
ARPANET: ABN.CJMERRICK
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End of AIList Digest
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