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AIList Digest Volume 7 Issue 023
AIList Digest Thursday, 9 Jun 1988 Volume 7 : Issue 23
Today's Topics:
Queries:
Response to: inductive expert system tools
Stock Price Forecasting
ITS Conference
Response to: AI in weather forecasting
Rule Based ES References
Response to: Stock Price Forecasting
Applications of AI for fault tolerance
Seminars:
Partial Computation of Database Queries (UNISYS seminar)
Feasible Learnability and Locality of Grammars (UNISYS seminar)
----------------------------------------------------------------------
Date: 6 Jun 88 18:42:42 GMT
From: esosun!cogen!alen@seismo.css.gov (Alen Shapiro)
Subject: Response to: inductive expert system tools
In article <402@dnlunx.UUCP> marlies@dnlunx.UUCP (Steenbergen M.E.van) writes:
>
> . I am engaged in artificial intelligence research. At the
>moment I am investigating the possibilities of inductive expert systems. In
>the literature I have encountered the names of a number of (supposedly)
>inductive expert system building tools: Logian, RuleMaster, KDS, TIMM,
>Expert-Ease, Expert-Edge, VP-Expert. I would like to have more information
>about these tools (articles about them or the names of dealers in Holland). I
>would be very grateful to everyone sending me any information about these or
>other inductive tools. Remarks of people who have worked with inductive expert
>systems are also very welcome. Thanks!
>
There are basically 2 types of inductive systems
a) those that build an internal model by example (and classify future
examples against that model) and
b) those that generate some kind of rule which, when run, will classify
future examples
a) includes perceptron-like systems and more recently neural-net technology
as well as some of the work my company does that is NOT neural-net based)
b) may be split into 2 camps; 1) systems that produce a single decision tree
for all decision classes (e.g. Quinlan's ID3 upon which RuleMaster,
Expert-Ease, Ex-Tran, Superexpert, First Class and more are based);
2) systems that produce a decision for each class-value (e.g. Michalski's
AQ11).
I do not include those systems that are not able to generalise in either
a or b since strictly they are not inductive!!
I don't know about dealers in Holland but ITL at George House, 36 N. Hanover
St., Glasgow Scotland G1 2AD (U.K.) are experts in producing REAL expert
systems that are inductively derived. The Turing Institute (same address)
are also well known in this regard.
--alen the Lisa slayer (it's a long story)
DISCLAIMER: I work for a company delivering inductively derived expert systems
into the real world doing real work and saving real money. I can be counted
on to be very biased!!
....!{seismo,esosun,suntan}!cogen!alen
------------------------------
Date: Tue, 7 Jun 88 15:52:30+0900
From: Minsu Shin <msshin%isdn.etri.re.kr@RELAY.CS.NET>
Subject: Stock Price Forecasting
I am looking for references (books, articles,...) or any information
concerning "Forecast of Stock Price using Pattern-Recognition".
I will produce the gathered information after receiving some
amount of information, if anyone wants.
Replies via email are fine.
Many thanks in advance for this favor.
My addresse is as follows:
Network Intellegence Section
ISDN Development Dept.
ETRI
P.O.Box 8, Tae-Deog Science Town
Dae-Jeon,Chung-Nam, 302-350, KOREA
Fax : 82-042-861-1033, Telex : TDTDROK K45532
------------------------------
Date: 7 Jun 88 08:13:51 GMT
From: mcvax!unido!cosmo!hase%cosmo.UUCP@uunet.uu.net (Juergen Seeger)
Subject: ITS Conference
I'm searching for Papers, readers, protocols and so on
on the ITS-Conference in Montreal.
Please send to:
Juergen Seeger
c/o Heinz Heise Verlag
Helstorfer Strasse 7
D-3000 Hannover 61
------------------------------
Date: Tue, 7 Jun 88 07:55:06 EDT
From: m06242%mwvm@mitre.arpa
Subject: Response to: AI in weather forecasting
To: AILIST@AI.AI.MIT.EDU
From: George Swetnam
Subject: AI in Weather Forecasting
In 1985, The MITRE Corporation and the National Center for Atmospheric
Research collaborated in an experimental expert system for predicting
upslope snowstorms in the Denver, Colorado area. An upslope storm is
one which gets the necessary atmospheric lifting from translation of a
moist airmass up a topographic slope. Upslope storms are responsible
for roughly 60% of the precipitation in the Denver region; in this case
the topographic slope is the slow, long rise from the Mississippi River
to the foot of the Rocky Mountains.
The most recent published information on this work is the paper whose
title and abstract appear below.
FIELD TRIAL OF A FORECASTER'S ASSISTANT FOR THE PREDICTION OF
UPSLOPE SNOWSTORMS
G. F. Swetnam and E. J. Dombroski, The MITRE Corporation
R. F. Bunting, University Corporation for Atmospheric Research
AIAA 25th Aerospace Sciences Meeting, January 12-15, 1987
Paper No. AIAA 87-0029
ABSTRACT
An experimental expert system has been developed to assist a
meteorologist in forecasting upslope snowstorms in the Denver, Colorado
area. The system requests about 35 data entries in a typical session
and evaluates the potential for adequate moisture, lifting, and cold
temperatures. From these it forecasts the expected snowfall amount.
The user can trace the reasoning behind the forecast and alter selected
input data to determine how alternative conditions affect the
expectation of snow.
Written in Prolog, the system runs on an IBM PC or PC compatible
microcomputer. A field trial was held in the winter of 1985-86 to test
system operation and improve the rule base. The system performed well,
but needs further refinement and automatic data collection before it can
be considered ready for evaluation in an operational context.
George Swetnam (gswetnam@mitre)
The MITRE Corporation
7525 Colshire Drive
McLean, VA 22102
Tel: (703) 883-5845
*
* George
::
------------------------------
Date: 7 Jun 88 19:05:59 GMT
From: unh!ss1@uunet.uu.net (Suresh Subramanian)
Subject: Rule Based ES References
I am building a rule based expert system as a part of the learning
system for my thesis. The RB expert system consists of a rule base, which has
the rules; a working area where problems to be solved are represented and
a interpreter which runs the RB expert system using Forgy's Rete Match
algorithm. I need references and suggestions regarding the evaluation of the
rule based expert system.
Please email to one of the following addresses.
1) ss@unhcs.unh.edu
2) ss1@unh.unh.edu or ss1@descartes.unh.edu
3) ss1@unh.UUCP
4) Internet : unh!ss1@uunet.uu.net
Thanks in advance for the information.
Suresh Subramanian
------------------------------
Date: 8 Jun 88 13:08:04 GMT
From: cbosgd!osu-cis!dsacg1!ntm1169@clyde.att.com
Subject: Response to: Stock Price Forecasting
> Date: Tue, 7 Jun 88 02:52 EDT
> From: Minsu Shin <msshin%isdn.etri.re.kr@RELAY.CS.NET>
> To: AILIST-REQUEST@mc.lcs.mit.edu
> Subject: Stock Price Forecasting
>
> I am looking for references (books, articles,...) or any information
> concerning "Forecast of Stock Price using Pattern-Recognition".
I am not sure if this is the reference that you are looking for, but
I saw an article "NeuralWare Expert System Classifies Stock Patterns" on
pages 21 and 24 oF FEDERAL COMPUTER WEEK, May 9, 1988. It discusses a system
using a software product called Analog Adaptive Pattern Classification System
, a product for IBM PC and compatibles costinga $4995 from NeuralWare INC.
(Sewickley, PA).
--
Mott Given @ Defense Logistics Agency ,DSAC-TMP, P.O. Box 1605,
Systems Automation Center, Columbus, OH 43216-5002
UUCP: {cbosgd,gould,cbatt!osu-cis}!dsacg1!mgiven
Phone: 614-238-9431
------------------------------
Date: Wed 8 Jun 88 15:04:57-PDT
From: Singaravel Murugesan <MURUGESAN@PLUTO.ARC.NASA.GOV>
Subject: Applications of AI for fault tolerance
------------------
I am interested in the area of application of AI/Expert System
Techniques for fault diagnosis and fault tolerance in computers
and real-time control and monitoring systems.
I would appreciate receiving references/bibliography and copies
of reports/publications in these areas. Kindly reply to:
S. Murugesan
NASA Ames Research Center
Mail Stop: 244-4
Moffett Field
CA 94035
Phone: (415)-694-6525
FTS: 464-6525
murugesan@pluto.arc.nasa
------------------------------
Date: Wed, 8 Jun 88 14:59:26 EDT
From: finin@PRC.Unisys.COM
Subject: Partial Computation of Database Queries (UNISYS seminar)
AI SEMINAR
UNISYS PAOLI RESEARCH CENTER
Partial Computation of Database Queries
Susan Davidson
Computer and Information Science
University of Pennsylvania
A critical component of real-time systems is the database, which is
used to store external input such as environmental readings from
sensors, as well as system information. Typically, these databases
are large, due to vast quantities of historical data, and are
distributed, due to the distributed topology of the devices
controlling the application and the critical need for fault tolerance.
Hence, sophisticated database management systems are needed. However,
most of the database management systems being used for these
applications are hand-coded. Off-the-shelf database management
systems are not used due in part to a lack of predictability of
response.
In this talk, an iterative method of processing real-time database
queries will be presented. The method improves the fault-tolerance
and predictability of response in real-time database systems by
guaranteeing an approximate answer to a query at any point in
computation; if for some reason the deadline of a query cannot be met
(for example, due to communication failures or unanticipated locking
which make certain database structures unavailable), a partial answer
can be given. Partial answers monotonically improve with time in the
sense that any fact which is said to be true remains true as
computation proceeds, and any fact which can be inferred to be false
remains false as computation proceeds.
2:00 pm Wednesday, June 15
BIC Conference room
Unisys Paloi Research Center
Route 252 and Central Ave.
Paoli PA 19311
-- non-Unisys visitors who are interested in attending should --
-- send email to finin@prc.unisys.com or call 215-648-7446 --
------------------------------
Date: Wed, 8 Jun 88 15:19:10 EDT
From: finin@PRC.Unisys.COM
Subject: Feasible Learnability and Locality of Grammars (UNISYS
seminar)
AI SEMINAR
UNISYS PAOLI RESEARCH CENTER
Feasible Learnability and Locality of Grammars
Naoki Abe
Computer and Information Science
University of Pennsylvania
Polynomial learnability is a generalization of a complexity theoretic
notion of feasible learnability originally developed by Valiant in the
context of learning boolean concepts from examples. In this talk I
will present an intuitive exposition of this learning paradigm, and
then apply this notion to the evaluation of grammatical formalisms for
linguistic description from the point of view of feasible
learnability. In particular, a novel, nontrivial constraint on the
degree of ``locality'' of grammars will be defined which allows
grammatical formalisms of much linguistic interest to be polynomially
learnable. If time allows possible implications of this result to the
theory of natural language acquisition will also be discussed.
2:00 pm Wednesday, June 1
Paoli Auditorium
Unisys Paloi Research Center
Route 252 and Central Ave.
Paoli PA 19311
-- non-Unisys visitors who are interested in attending should --
-- send email to finin@prc.unisys.com or call 215-648-7446 --
------------------------------
End of AIList Digest
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