Date: Tue  7 Jun 1988 22:14-EDT
From: AIList Moderator Nick Papadakis <AIList-REQUEST@AI.AI.MIT.EDU>
Reply-To: AIList@AI.AI.MIT.EDU
Us-Mail: MIT Mail Stop 38-390, Cambridge MA 02139
Phone: (617) 253-2737
Subject: AIList Digest   V7 #22
To: AIList@AI.AI.MIT.EDU
Status: RO


AIList Digest           Wednesday, 8 Jun 1988      Volume 7 : Issue 22

Today's Topics:

 Queries:
  Response to: inductive expert system tools
  Stock Price Forecasting
  Response to: AI in weather forecasting

  Talk Announcement - "Mundane Reasoning"

----------------------------------------------------------------------

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: 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: Tue, 7 Jun 88 00:13:03 EDT
From: research!dlm@research.att.com
Subject: Talk Announcement

______________________________________________________________________

                TALK ANNOUNCEMENT


Speaker:        Mark Derthick - Dept. of CS, Carnegie Mellon University

Title:          Mundane Reasoning

Date:           Tuesday, June 7
Time:           10:00
Place:          AT&T Bell Laboratories  MH 3D436

Abstract:

Frames are a natural and powerful conception for organizing knowledge.
Yet in most well-defined frame-based knowledge representation systems,
such as KL-ONE, the knowledge base must be logically consistent, no
guesses are made to remedy incomplete knowledge bases, and they
sometimes fail to return answers in a reasonable time, even for
seemingly easy queries.  On the other hand are connectionist knowledge
representation systems, which are more robust in that they can be made
to always return an answer quickly, and knowledge is combined
evidentially.  Unfortunately these systems, if they have a well
defined formal semantics at all, have had much less expressive power
than symbolic systems.  The differing characteristics result from two
independent decisions.  First, the statistical technique of Maximum a
Posteriori estimation is used as a semantic foundation rather than
logical deduction.  Second, heuristic simplifications of the models
considered give rise to fast, but errorful behavior.  Having made this
distinction, it is possible to use the same powerful syntax of
symbolic systems, but interpret it statistically and implement it with
a connectionist network.  Although correct networks are exponentially
large, they serve as a basis from which architectural simplifications
can be made which preserve an intuitive connection to the formal theory.
The knowledge base must be tuned to alleviate errors caused by the
heuristic simplifications, so the system is intended for familiar
everyday situations in which past performance has been used for
training and in which the ramifications of wrong answers are not
serious enough to justify the exponential search time required for
provably correct behavior.

Sponsor: Ron Brachman & Deborah McGuinness (allegra!dlm)

------------------------------

End of AIList Digest
********************