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IRList Digest Volume 1 Number 05
IRList Digest Sunday, 1 Sep 1985 Volume 1 : Issue 5
Today's Topics:
Query - Bibliographies on Representation of Knowledge
Discussion - Teaching Law & AI/DB/IR Techniques
Announcement - Report on Univ. Regina Workshop on Adaptive Inf. Proc.
- Fast DB Support in Mu-Prolog
References - Recent Articles, Bibliography
Call for Papers - Intelligent Tutoring Systems, J. AI
Jobs - AI Work in Education, Expert System Design
----------------------------------------------------------------------
Date: Mon, 8 Jul 85 00:00:19 cdt
From: Mark Turner <mark%gargoyle.uchicago.csnet@csnet-relay.arpa>
Subject: representation of knowledge
[Copied from AIList Digest Volume 3 : Issue 90 - Ed]
I am gathering for my students a bibligraphy
of works on representation
of knowledge. I am particularly concerned with
cognitive psychology, artificial intelligence,
philosophy, linguistics, and natural language processing.
I would appreciate receiving copies of bibliographies
others may already have on-line.
Mark Turner
Department of English
U Chicago 60637
>ihnp4!gargoyle!puck!mark
------------------------------
From: ihnp4!utzoo!lsuc!dave@UCB-VAX
Date: 7 Jul 85 13:10:14 CDT (Sun)
Subject: AI techniques for teaching tax law?
[Forwarded from AI-ED. Also could be of interest to legal IR people. - Ed]
...
I've developed a fully-operating CAI course on Canadian income
tax law which is used to teach Ontario's law students. This course
uses no AI techniques at all. I believe that within certain domains
(such as tax) and subject to certain limitations, one can design
a surprisingly "intelligent" CAI system without AI. My CAI runs
with an interpreter from disk files, and it's trivial to modify the
disk files to catch wrong student answers which have been flagged
by the system as unrecognized. After plenty of iteration with test
students, therefore, I now have a system which "recognizes" and
correctly responds to almost every imaginable wrong answer which
students will try for a given question.
Sample:
++++++++++
Computer: [long fact situation]
How much tax is payable by X?
Student: $1,500
Computer: Sorry, that's wrong.
You're miscalculating the capital gain. Remember to
take the cost base into account, and don't forget that
only one-half of the gain is taxable!
++++++++++
Having said that, I do think AI has a role to play in my course
and I'd like to explore using it in future lessons. In particular,
I'd like to develop a mini-simulation using Prolog, and let the
student play with variables or with input and output values and
examine the results. Does anyone out there use Prolog models for
CAI in this kind of way?
The other advantage AI techniques would have over my present code
is that questions could have randomly-different numbers instead
of being the same each time through. However, it must still be
possible to easily write tailored answers. In the above example,
I would need a simple way to indicate "1500" as "(a+b)*d", where
the correct answer is known to be "(a+b-c)*d/2".
It must also be possible to generate only those fact situations
(i.e., numbers) which are (a) easy for students to compute the
answers to, and (b) unambiguous in terms of different answers -
that is, 1500 in the example above must not be able to reflect
two different chains of wrong reasoning. Perhaps, given these
limitations, it would be better to hand-code a set of possible
values for a, b, c and d rather than have the computer generate them.
Any ideas out there? Has anyone done this kind of thing?
Dave Sherman
The Law Society of Upper Canada
Toronto
------------------------------
From: "V.J. Raghavan" <ihnp4!sask!regina!raghavan@UCB-VAX>
Date: Wed, 14 Aug 85 15:47:30 cst
Subject: new wkshp rprt
WORKSHOP REPORT
A workshop was organized and conducted on June 10-11, 1985 at
the University of Regina, Regina, Canada. It was entitled "Workshop On
Foundations Of Adaptive Information Processing" and was sponsored by the
Faculty of Graduate Studies and Research and the Computer Science
Department of our University.
The impetus for the workshop came as result of the following
eventualities:
(i) There has been a great surge of interest in the recent
years on investigating research progress in AI and
their implications for computer science research in general,
(ii) In a number of sub-disciplines of computer science such
as Information Retrieval, Pattern Recognition, Expert systems
and Decision Support, there is a striking similarity in the
manner in which the problems of interest are formulated,
(iii) There are several faculty members in the computer
science department at University of Regina whose research
interests overlap information retrieval, AI, expert
systems and so on,
(iv) The ACM - SIGIR annual conference was in Montreal,
Canada in the first week of June and several individuals,
coming to that conference, had expressed a desire to visit
University of Regina, and
(v) The dean of our Graduate Studies and Research faculty
was enthusiastic in providing financial support for
this venture.
The total attendance at the workshop was 20, including faculty
members and graduate students of our department and eight guests who
agreed to participate upon our invitation. There were, in all, twelve
presentations each lasting for approximately forty minutes. Four of the
twelve presentations were by faculty members of our computer science
department. There was ample opportunity for informal discussions and
exchange of ideas. This contributed greatly to the success of the
workshop.
The participants had many varied backgrounds. This was in line
with the main emphasis of the workshop, which was to compare and
contrast the various problem formulations in the disciplines represented
by the participants and to discuss models and theoretical foundations
upon which solutions to these problems are based. Following is a list of
individuals who participated and the titles of their presentations.
* Research into fuzzy extensions of information retrieval
D. Kraft, Louisiana State University
* Problems of introducing a Boolean structure into
probabilistic retrieval
A. Bookstein, University of Chicago
* Adaptive Boolean information retrieval
T. Radecki, Louisiana State University and Tech.
University of Wroclaw
* On metric data models and associated search strategies
L. Goldfarb, Univ. of New Brunswick
* A unified model for information retrieval
M. Wong, Univ. of Regina
* Effectiveness of genetic algorithm for document
redescription
M. Gordon, The Univ. of Michigan
* Cluster analysis and genetic algorithm in machine
learning
L.A. Rendell, Univ. of Illinois at Urbana-champaign
* A system for detecting "deep" and "shallow" concepts in
simple definitions
M. Janta-Polczynski, Univ. of Regina
* A concept-learning information retrieval system - basic
ideas
W. Ziarko, Univ. of Regina
* A generalized retrieval facility for management decision
support
J.S. Deogun, Univ. of Nebraska-Lincoln
* Two axioms for performance evaluation of information
retrieval systems
P. Bollmann, Tech. University of Berlin
* Some thoughts on adaptive clustering for information
retrieval
V.V. Raghavan, Univ. of Regina
Dr. Jeff Sampson of Univ. of Alberta was to give a presentation
entitled "Genetic algorithms - A class of adaptive search procedures".
But to our great disappointment and deep sorrow, we learned that he
passed away, the week earlier, while travelling in France.
It is expected that a proceedings of the workshop would be
published in the near future. In addition, a summary of each
presentation is due to appear over next several issues of the SIGIR
Forum.
Workshop Chairman
Vijay Raghavan
University of Regina
------------------------------
Date: 05 Jul 85 13:45:05 +1000 (Fri)
From: John Shepherd <munnari!mungunni.oz!JAS@Seismo>
Subject: New Deductive Database System
[Forwarded from PROLOG Digest Volume 3 : Issue 30 - Ed]
A System for Very Large Deductive Databases
using a Superimposed Codeword Indexing Scheme
=====
This note is to announce the (near) availability of a
deductive database system suitable for dealing with very
large databases of Prolog rules. The indexing scheme used
by the system is based on the method of two-level
superimposed codewords as described in [1], which allows
partial match retrieval. Superimposed codeword schemes
provide a very efficient method of retrieving records from
large databases in only a small number of disk accesses.
Further, the access method can be tuned so that the ratio
of "false matches" can be reduced by an arbitrary amount
(with a corresponding increase in storage costs). Unlike
many earlier systems, this system supports the storage and
retrieval of completely general Prolog terms, including
functors and variables, and it is even possible to store
Prolog rules in the database.
The system is in the final stages of development under
Berkeley Unix (4.2BSD) and has already been interfaced to
the MU-Prolog system[2,3]; it will be incorporated into
release 3.2db of MU-Prolog which will be available soon
for Unix and VMS. It is being developed as part of the
Machine Intelligence Project at the University of
Melbourne on a Pyramid 90x which was loaned to the project
by Pyramid Technology in Australia. The figures given
below are taken from the Pyramid running version 2.3.1 of
the OSx operating system (in the Berkeley universe) with
one 400 Mb disk.
Preliminary tests, on a database of mail transfer pathways
through Usenet containing one million facts, have been
very encouraging. To store these facts, which have an
average length of 60 bytes, required just over 80Mb, which
means a storage overhead of about 30%. In the present
system, with a one-million record database indexed on
three attributes, the rate of insertion is six records per
CPU second. The rate of insertion could be significantly
increased if the system were run as a single-user batch-
type system without locking controls. Specifying just two
of the fields (each record contains four fields),
retrieved on average just 3 records for a query which had
only one correct answer. The system can achieve a record
retrieval rate of around 1000 records per second for a
query on highly clustered records, to about 50 records per
second for a query on unclustered records, even on this
large database; for smaller databases, even faster rates
are achievable. A query with complete information,
required on average 1.1 retrievals, and required 4 disk
accesses (excluding overheads from the Unix file system).
This system overcomes some of the limitations of the Unix
file system. For example, it overcomes the limit of
twenty open files per process by caching on Unix file
descriptors, thus allowing several database relations to
be accessed simultaneously. The system also provides data
buffering to reduce the number of file opens and data
reads. The processing time of logic programs such as the
"ancestor" relation can be minimised by this feature.
We would be interested in hearing from other groups who
are developing similar systems. For further information
on this system, contact Dr. K. Ramamohanarao (Rao) or John
Shepherd at the following addresses:
HardMail:
Department of Computer Science
University of Melbourne
Parkville, Victoria, 3052
AUSTRALIA
SoftMail:
UUCP: {seismo,ukc,prlb2}!munnari!jas
{decvax,eagle,pesnta}!mulga!jas (SLOW)
ARPA: munnari!jas@seismo.ARPA
CSNET: jas@munnari.oz
("jas" can be substituted by "rao")
Also, Dr. Rao will be attending the Logic Programming
Symposium in Boston, and would be willing to discuss the
system there.
[1] R.Sacks-Davis and K.Ramamohanarao "A Two Level
Superimposed Coding Scheme for Partial Match
Retrieval", Information Systems, v.8, n.4, 1983
[2] By way of comparison, this system eliminates a number
of restrictions which were associated with the
deductive database system provided with release 3.1
of MU-Prolog. That system implemented the database
manager as a separate process from the Prolog
interpreter, communicating via Unix pipes. The
present system is designed as a library package which
is compiled into the host system; it could be
incorporated fairly easily into most Prolog
interpreters, or, in fact, into any systems that
wished to perform partial match retrieval. The use of
pipes in the old MU-Prolog system placed severe
limitations (because of Unix file descriptor
limitations) on the number of transactions (queries)
which could be active concurrently; the new system
has eliminated this restriction. Finally, this system
lifts the restriction that only ground facts could be
stored in the database, by allowing the storage of
arbitrary Prolog terms (including rules).
[3] L.Naish "MU-Prolog 3.2db Reference Manual", Technical
Report, Department of Computer Science, University of
Melbourne, 1985.
------------------------------
Date: 10 Aug 1985 02:25-EST
From: leff%smu.csnet@csnet-relay.arpa
Subject: Recent Articles, bibliography
... [Extracted from messages in AIList Digest Volume 3: #109 - Ed]
%A Barbara Kellam-Scott
%T Harvard Law School Computerizes the Paper Chase
%J Hardcopy
%D JUL 1985
%P 19
%V 14
%N 7
%K DEC
%X Harvard Law School is automating their Legal Services Clinics.
They have plans to include an expert system to assist lawyers in
handling these cases. Digital Equipment has contributed to this program.
%A Clara Y. Cuadrado
%A John L. Cuadrado
%T Prolog Goes to Work
%J MAG4
%P 151-159
%K Symbolics Al Despain Yale Patt Berkely
%X Al Despain and Yale Patt of Berkeley have achieved 425,000 LIPS using
a custom designed processor. Symbolics has achieved 100,000 LIPS using
custom microcode. Discusses general issues of Prolog in the contex of
a maze traversing system. Also discusses the Japanese Fifth Generation
project.
%A F. Bouille
%T The 'HBDS' Database Model Kernel of a Structured Data
Base System. Making Databases Work
%J IEEE Proceedings of Trends and Applications
%D 1984
%P 324-331
%A V. P. Kobler
%T Overview of Tools For Knowledge Base Construction
%J International Conference on Data Engineering
%I IEEE
%C Los Angeles, Ca
%D 1984
%P 282-285
%A C. Maioli et al.
%T Prototypes of Expert Systems for a Friendly Man Machine Interaction.
User Terminals for Information/Communication Systems
%J 31st International Congress on Electronics. Proceedings
%D 1984
%P 35-42
%A Z. L. Rabinovich
%T Machine Intelligence and Fifth Generation Computer Structures
%J Cybernetics
%V 20
%N 3
%D MAY-JUN 1985
%P 426
%A W. Dilger
%A W. Womannn
%T The METANET: A Means for the Specification of Semantic Networks as
Abstract Datatypes
%J International Journal of Man-Machine Studies
%V 21
%N 6
%D DEC 1984
%P 463
%A Michael B. First
%A Lynn J. Soffer
%A Randolph A. Miher
%T QUICK (quick Index to Caduceus Knowledge) Using the
Internish/Cadaceus Knowledge Base as an Electronic Textbook of
Medicine
%J Computers and Biomedical Research
%V 18
%N 2
%D APR 1985
%P 137
------------------------------
From: Ken Laws <Laws@SRI-AI>
Date: Fri 9 Aug 85 13:48:26-PDT
Subject: JAI Issue on Intelligent Tutoring Systems
>From CACM, August 1985: [Forwarded from AI-ED. - Ed]
Papers on intelligent tutoring systems are sought for a special issue
of the Journal of Artificial Intelligence. Topics appropriate for
this issue include knowledge representations tailored for use in an
Intelligent Tutoring System (ITS); architectures for ITSs; methods
for building student models; methods for diagnosing student's bugs
and misconceptions; tutoring strategies; the use of natural language;
design of the human-computer interface; case studies of ITSs; and
psychological research relevant to the constructions of ITSs. Manuscripts
should be submitted by February 15, 1986, to one of the guest editors:
Elliot Soloway, Department of Computer Science, Yale University,
P.O. Box 2158, New Haven, CT 06520; or William Clancy, Stanford
Knowledge Systems Laboratory, 701 Welch Road, Building C, Palo Alto,
CA 94304.
------------------------------
From: dartvax!creare!pbb%creare%dartmouth.csnet@CSNET-RELAY
Date: Fri, 2 Aug 1985 09:02 est
Subject: AI Work in ED
[Forwarded from AI-ED. - Ed]
AI Work in ED at Creare
The following describes ongoing work at Creare (KREE-`ARE-EE) in
the field of AI in education and ends with a call for
consultant help.
We at Creare are writing a phase II proposal for the design of
an Expert System which will be used for discovering and
describing scientific giftedness in students, primarily in the
Junior High age group.
The Expert System will try to detect the presence of
giftedness by matching computer administered test results
against knowledge based "feature models". We will develop a
set of knowledge bases to be used in the adaptive testing
environment where the selection of questions and the
interpretation of answers are dynamically compared to a set of
rule based models of gifted scientific thinking. The
administration of questions will be controlled by another
knowledge base which has the goal of efficiently determining
which model best conforms to the testing results.
These models of giftedness do not really exist yet, and we are
not aware of any "experts" currently in this field. We hope
that we can develop expertise in this area as our models are
augmented and refined. We are working with education and
testing specialists to achieve this goal.
We are currently looking for help with the overall design of
the the proposed Expert System. Experienced knowledge-base
system designers are being sought to help review our design
and to become part of the project should Creare be awarded the
phase II grant.
Anyone who might be interested and qualified, particularly
those reasonably close to New Hampshire, should contact:
Phil Bowman
Creare, Inc.
Box 71
Hanover, NH 03755
(603) 643-3800
dartvax!creare!pbb
------------------------------
END OF IRList Digest
********************
