"Verlyn M Johnson" <VERLYN@rchvmp.vnet.ibm.com>
Date: Fri, 9 Apr 1993 05:38:00 -0700
Message-id: <199304091234.AA16269@venera.isi.edu>
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From: "Verlyn M Johnson" <VERLYN@rchvmp.vnet.ibm.com>
To: Multiple recipients of list <srkb-list@ISI.EDU>
   A summary (abstract, introduction, references) of the
   following work is now available through anonymous ftp from

The complete thesis is available from:

           UMI Dissertation Services
           300 North Zeeb Road
           Ann Arbor
           Michigan    48106-1346

   The following abstract is taken from

 ------- [JOHNSON] ftp: /pub/knowledge-sharing/papers/johnson.ps

      Investigations into Database Management System Support
                   for Expert System Shells

                       Verlyn M Johnson


    Many expert system shells are available for developing
 production rule based expert system applications.  However, it is
 difficult to rapidly change those applications to respond to
 changing business conditions.  Each shell has its own production
 rule language and inferencing capabilities.  It is unclear what
 information can be shared (reused).  Use of main memory instead
 of a shared, common source for rules constrains the size of
 applications and can result in duplication.  Maintenance is not
 immediately available to existing inference sessions and
 updates made by a session only affect that session.

    This thesis approaches production rules and working storage as
 data that can be managed by enhanced database management systems
 (DBMSs).  Five expert system shells are studied.  A composite
 (canonical) production rule syntax is developed which provides
 knowledge engineers with a common language for production rules.
 It is mapped into an integrated data model for use by tool
 developers who wish to design common production rule storage
 databases and maintenance tools.  Extensions to the data model
 allow expert system shell developers to reduce main memory
 constraints by using a DBMS to store and manage execution data.
 The analysis performed in building the data model reveals where
 translation, system enhancements, or standard definitions are
 required to share production rules.

    Two DBMS enhancements are defined to facilitate management of
 production rule and execution data (but which also have other
 applications).  Reflexive indexes enable a DBMS to incrementally
 maintain transitive closures (including multiple tables,
 duplicates, side paths, and accumulated values) as a database
 index.  They simplify query formats, and eliminate the need for
 recursive processing during retrieval.  One use is to accumulate
 rule premise evaluation values during inferencing.  The inference
 locking protocol allows concurrent, dynamic access by those
 maintaining and executing control data.  For example, it provides
 greater flexibility in maintaining production rules by allowing
 knowledge engineers to use multiple versions and notification to
 control how updates to production rules affect other maintenance
 and inference sessions.  The protocol can also be used to extend
 production rule capabilities by allowing production rules to
 maintain production rules concurrently with other maintenance and
 inference sessions.