From: timm@spectrum.cs.unsw.oz.au (Tim Menzies)
Subject: Re: Teaching AI
Reply-To: timm@spectrum.cs.unsw.oz.au (Tim Menzies)
Organization: none
Date: Sat, 5 Sep 1992 00:19:30 GMT

From article <Bu14s1.IJC@matilda.vut.edu.au>, by awenn@matilda.vut.edu.au (Andrew Wenn):
> Dear AI people,
> 
> If you were planning a subject that specifically looked at the way AI
> techniques could be applied in commercially oriented environments, what type of
> things would you include?
> 

There  is an  emerging theory  of  knowledge engineering. As 
near as I can tell four main schools. I would teach students
something about each school as theory. And for a programming
assignment, I'd make them code up one of Chandra's generic
tasks.

Some notes on the four school follow....

[Gaines 90] observes that the field of knowledge  acqusition
methodologies,  techniques, and tools is marked by a diverse
set of (mostly) incompatible  tools  and  techniques.   This
diversity, he remarks, is the hallmark of a immature discip-
line and should not be viewed complacently in the 1990s.
A unified knowledge acquisition framework would require some
common  knowledge ontology  (roughly:  ontology  means  "the 
shape of conceptual classes). Such a common  ontology  would
have several advantages:

   o permit the integration of  knowledge  gathered  from  a
     multitude of sources;

   o permit the portability of knowledge from environment to
     environment;

   o permit the combined processing of  different  kinds  of
     knowledge representations;

   o provide principled guidelines for  the  development  of
     knowledge   processing  tools  thus  simplifying  their
     design and construction;

   o provide principled  guidelines  for  the  education  of
     novices to the knowledge-processing industry (knowledge
     engineers, etc.).

Recent research on  the  development  of  generic  knowledge
ontologies  has  focused  on  shared or re-usable components
between domains.  These components may  be  knowledge  frag-
ments, execution modules, or data structures:

   o [Neches 91 ] and [Lenat 90] propose the creation  of  a
     core  of  knowledge  fragments  that underlies multiple
     domains and can be used to bootstrap the development of
     a knowledge-based system.

   o [Clancey 92] describes the  underlying  theory  of  the
     HERACLES system. This is a generic knowledge-based sys-
     tem architecture whose processing is defined  in  terms
     of  graph  operations on a model constructed at runtime
     in response to a particular query.

   o [Rappaport 91], [Skuce 89] and  [Gaines  90]  adopt  an
     object-oriented  approach.  Gaines and Skuce et al base
     their systems on the Brachman KL-ONE  style  of  object
     systems ([Brachman 85]) Brachman identified two  essen-
     tially   different  types  of  knowledge:   assertional
     knowledge and terminological knowledge (which he  terms
     the a-box and the t-box respectively). He then offers a
     clean semantics  for  the  t-box  (subsumption  without
     overrides).  Gaines'  KSS0 implements both in C++ while
     Skuce's CODE system uses Smalltalk for  the  t-box  and
     Prolog  for  the a-box.  Rappaport takes a more concep-
     tual  approach  and  advocates  the   construction   of
     libraries  of  shareable  super-knowledge  objects. The
     "objects" in the Rappaport schema are at one  of  three
     levels:  atomic,  agenda, or macro-operators. Different
     knowledge structures can communicate and be  integrated
     via their abstract common data structures (ACDS).

   o [Marques 91], [Tu 91], and  [Chandrasekaran  86]  argue
     that   knowledge-based   systems   development  can  be
     enhanced via  the  use  of  libraries  of  task-related
     architectures  which  can  be  applied  to a particular
     problem.   Tu   et   al   takes   a   somewhat   hybrid
     tasks/object-oriented  approach in that their tasks are
     specified within a subclassing hierarchy  thus  permit-
     ting  the  customisation of tasks via class specialisa-
     tion.


                         REFERENCES

 [Gaines 90] Gaines B.R.  An Architecture  for  Integrated  Knowledge
    Acquisition  Systems  in  the  5th  AAAI-Sponsored Banff
    Knowledge   Acqusition   for   Knowledge-Based   Systems
    Workshop, Banff, Canada, November, 1990, 18 pages.
 [Skuce 89] Skuce  D.,  Shenkang  W.  &  Beauville  Y.   A   Generic
    Knowledge  Acqusition  Environment  for  Conceptual  and
    Ontological Analysis in  the  4th  AAAI-Sponsored  Banff
    Knowledge   Acqusition   for   Knowledge-Based   Systems
    Workshop, Banff, Canada, October  ,  1989,  pp  31-1  to
    31-20
 [Neches 91] Neches R.  Acqusition of Knowledge for Sharing and Reuse
    in the 6th AAAI-Sponsored Banff Knowledge Acqusition for
    Knowledge-Based Systems Workshop, Banff, Canada, October
    6-11, 1991, pp 18-1 to 18-20.
 [Lenat 90] Lenat D.B., & Gutha R.V.  CYC: A Midterm  Report  in  AI
    Magazine, Fall 1990, pp32-59.
 [Clancey 92] Clancey W.J.  Model Construction Operators in Artificial
    Intelligence, 53, (1992) pp1-115.
 [Rappaport 91] Rappaport  A.T.  A  Theory  of  Local  and  Interstitial
    Knowledge,  in  the  6th  AAAI-Sponsored Banff Knowledge
    Acqusition for Knowledge-Based Systems Workshop,  Banff,
    Canada, October 6-11, 1991, pp 21-1 to 21-18.
 [Brachman 85] Brachman R.J. & Schmolze J.G.  An Overview of the KL-ONE
    Knowledge Representation  System  in  Cognitive  Science
    9(2), April-June 1985, pp171-216.
 [Marques 91] Marques D.,  Klinker  G.,  Dallemange  G.,  Gautier  P.,
    McDermott  J. & Tung D. More Data on Usable and Reusable
    Programming  Constructs  in  6th  AAAI-Sponsored   Banff
    Knowledge   Acqusition   for   Knowledge-Based   Systems
    Workshop, Banff, Canada, October 6-11, 1991, pp 14-1  to
    14-19.
 [Tu 91] Tu S., Shahar Y., Dawes J., Winkles J., Puetra A., Musen
    M.   A  Problem-Solving  Model of Episodic Skeletal-Plan
    Refinement in the  6th  AAAI-Sponsored  Banff  Knowledge
    Acqusition  for Knowledge-Based Systems Workshop, Banff,
    Canada, October 6-11, 1991, pp 34-1 to 34-20.
 [Chandrasekaran 86] Chandrasekaran B. Generic Tasks in Knowledge-Based  Rea-
    soning:  High-Level  Building  Blocks  for Expert System
    Design in IEEE Expert, Fall 1986, pp23-30.


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