From: AAAI Technical Report SS-97-01. Compilation copyright © 1997, AAAI (www.aaai.org). All rights reserved.
Opportunities for AI Applications
in Knowledge Management
Kenneth J. Meltsner
JohnsonControls, Inc.
Controls Group
507 East MichiganStreet
Milwaukee,WI 53202
kenneth.j.meltsner@jci.com
Abstract
TheControls Groupof JohnsonControls has a strong
interest in the applicationof techniquesandtools from
the Artificial Intelligence (AI) community
to its core
problems of capturing, preserving, and applying
knowledge. One important distinction between
Knowledge Management and AI is that the
knowledgecan be captured, preserved, and applied
withoutthe explicit representationneededfor expert
systemsor other performancesupport systems. Other
"delivery vehicles" include expert analyses,
documentedmethodologies, and training. Onetopic
of significant interest to us is the nature of the
differences between such approaches and more
traditional AI systems, and whethersimilar methods
can be used to developboth.
Statement of Interest
Johnson Controls has determined that knowledge
managementwill support much of Controls Group’s
business strategy in the next decade. Eachbusiness unit
within the group needs to use and to developknowledgebasedapplications andtools to supportthe integration of
the individual units. Thefundamentalproblemis to move
from an appreciation of knowledge managementand
related activities as a desirable conceptto practical and
cost-effective implementations.This process is expected
to take years, andwill include both short-termapplication
projectsandlonger-termresearchefforts.
TheControls Grouphas tens of thousandsof employees
worldwide
supportinga full range of facilities management
services, maintenance,installation, manufacturing,and
sales efforts. This workforceis both international and
local: programs maybe worldwidefor a customer in a
specificindustry,or limitedto a singlecity or region.
Preliminary
knowledge management (KM)
investigationshavehighlightedthe needfor flexible tools
to collect informationandexperiences,organizeandfilter
this highly diverse body,and deliver it in as succinct a
formas possiblefor eachapplicationanduser.
Supportingthis effort will be conventionalgroupware
and documentmanagementtools, as well as detailed
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informationanddata modeling,but it is expectedthis will
not be sufficient to provide effective knowledge
management.A substantial investment in editing and
indexing would be required to makethe information
gathered useful for traditional training and retrieval.
Conventional
retrieval technology,such as full-text search
engines, is not expectedto be fully effective in retrieving
relevant information.In addition, the time required woald
guarantee that a significant portion of the assessed
knowledgewouldbe out-of-date.
Apreferredalternative wouldbe to use AI-basedtools ilt
combinationwith humanefforts to organizeand assess the
bodyof knowledge.Other techniques such as data mining
hold significant promise as worldwidedata sources are
integrated and madeavailable throughoutthe company.
Tools drawn from AI for knowledge acquisition,
knowledgerepresentation, and knowledgeexchangecould
be adapted to serve Controls Group employees.
Technologies which hold promise include constraint
languagesfor design and configuration, conceptmapsfor
organizingbodies of knowledgeand mediatingdifferences
in representation, ontologies and logic systems for
knowledge representation and exchange, and natura
language methodsfor generation of documentationanti
reports.
Whilethe literature is filled with examplesof "expert
systems"andthe AI tools usedto developthem,the use o "
such tools to supporthumanknowledge
delivery is not a~,
well understood. The transition from traditional exper
systems to a more human-based knowledge deliver)
approachmayalso require newtools as well. Wehopethk,.
symposium
will demonstratetools and methodscapable e’
assisting such a change,and will developa moreexplici~
vision of the next generation of knowledgetools suitabk
for bothhuman
andartificial intelligenceapplications.