From: AAAI Technical Report SS-94-01. Compilation copyright © 1994, AAAI (www.aaai.org). All rights reserved.
The GAMES-IIMethodology for medical KBSdevelopment
Giordano Lanzola+ Gertjan van Heijst $ Mario Stefanelli + $Guus Schreiber
+University of Pavia, Medical Informatics
Via Abbiategrasso209, 1-27100, Pavia, Italy
Tel: +39 382 39 1350; Fax: +39 382 42 2881
E-mail mstefa@ipvstefa.unipv.it, giordano@ipvaimedl.unipv.it
$University of Amsterdam,Social Science Informatics
Roetersstraat 15, NL- 1018 WB,Amsterdam,The Netherlands
Tel: +31 20 525 6789; Fax: +31 20 525 6896
E-mail{ gertj an, schreiber } @swi.psy.uva.nl
1 Introduction
GAMES-II
is an european AIMproject aiming at
developing a general methodologyfor the construction of medical knowledge based systems (KBS)
based on a general modelof medical reasoning. Such
a modelsplits medical reasoning in two subsequent
steps. Whilethe first oneis aimedat selecting a set of
hypotheses representing possible solutions of the
problemat hand, the second one is aimedat testing
each of them. Becauseof the paradigmon whichit is
rooted, the modelhas been dubbedselect and test
model(STModel).It can represent the three generic
medical tasks: diagnosis, therapy planning and
patient monitoring, by defining the knowledgeroles
played by the domainentities in each one of them.
The GAMES-IImethodology is based on three
principles that haveemergedduring the last decadeof
AI research:
(i) knowledgelevel modeling,
(ii) reusability of both task and domainknowledge,
(iii) integration of multiple reasoningtechniques.
The knowledgemodeling (Newell, 1982) principle states that knowledgeshould be modeledon a
higher level than that of exploited knowledgerepresentation formalisms, to avoid premature design
decisions, and to facilitate communicationwith
domainexperts. Thesecondprinciple implies that the
complexity of KBSdevelopmentmust, just as with
any other engineeringactivity, be tackled by the construction of libraries of reusable components
(Puerta
et al., 1992). Thethird principle is basedon the consideration that the "weakmethods"are too weak, and
therefore that KBSshould use multiple specialised
78
reasoning techniques for the different steps in the
problemsolving process. (Wielingaet al., 1992)
GAMES-IIviews the KBSdevelopment process
as composed
of two phases. In the first phasean epistemological modelis constructed representing the
conceptual modelof the KBSwewant to build. In the
secondphase this modelis transformedinto a computational model, a model of a computer implementation to performthat task, preservingthe structure of
the epistemological modelas muchas possible. The
construction of this modelis a four-step process;
(i) construction of a task model,
(ii) configuration of a domainontology,
(iii) mappingthe task modelonto the domainontology (Ramoniet al., 1992) and,
(iv) instantiating the domainontology.
Theavailability of libraries of generic task instantiations and libraries of generic ontologiesmakeseasier the construction.
Within GAMES-IItools
supporting all of these activities have been developed.
Weidentify three steps in the construction of the
computational model:
(i) selection/formulation of the meta rules,
(ii) selection of problemsolvers,
(iii) representation of the domainknowledgethrough
the formalisms used by the selected problem
solvers.
The availability of the epistemological model
ensures that all the relevant knowledgeis present
whenthe computational model is constructed. The
construction of the computationalmodelis supported
by the availability of a blackboardshell.
Oneof the main theses of GAMES-II,
is that one
single modelof reasoning(the STModel)
is sufficient
to describe all kinds of reasoning found in medical
practice. The STModelviews reasoning as a cyclic
process of ABSTRACTION
of relevant problem features from data, ABDUCTION
of potential solutions
(hypothesis) from these relevant problemfeatures,
subsequent DEDUCTION
of expected observations
if the hypotheses would hold, and eliminative
INDUCTION
of hypotheses whose deduced predictions were false. In data rich environmentssuch as
clinical environmentsabstraction is the key to successful problemsolving.
The GAMES-IIapproach is being used in nine
clinical pilot projects, and provedto be successfulin
managingpatients integrating several types of problem solvers and exploiting time reasoning.
2 The Computational Model
The computational architecture we adopted is
extensible. This design choice is required to allow a
smoothintegration of different problemsolvers into
the same frameworkduring the process of KBSconstruction. Addingnewproblemsolvers accounts for
the possibility of using each time the most suitable
formalisms for knowledgerepresentation matching
the ontological structure of the givendomainat best.
Amongall the computational architectures found
in the literature, the "BlackboardArchitecture"(BA)
Central Module
Control
Knowledge
Sources
CmtralBlackboard
~/ Control I]B~ Q KnowledgeSources
Problem
Solver1
Fig. 1
Problem
Solvea"
2,
The Distributed Control BlackboardArchitecture.
79
seemsthe mostsuitable in fitting those requirements.
BAimposesas its only requirementthe existence of a
"Blackboard Structure", simply referred as blackboard, and several" KnowledgeSources". While the
blackboardacts as a central repository for the information where the state-of-the-art concerning the
problem solution is kept, the knowledgesources
incrementally add new information on the blackboard, therefore cooperatingin the process of building a set of plausible solutions for a given case. No
control componentis taken into account in the BA
since that frameworkis just aimed at providing an
abstract conceptual wayof structuring and organizing information within a KBS.
The architecture adopted within the GAMES-II
project is based on a slightly modified implementation of the Control Blackboard Architecture (CBA)
proposed by B. Hayes-Roth(1985). Oneof the major
goals of the project is indeedto represent the dynamic
flow of reasoning, makingexplicit howdifferent
generic medicaltasks need to be solved invokingall
the available problemsolvers. Sucha goal is achieved
by describing each task through a suitable taskdecomposition hierarchy. A careful description is
given about the conditions which must be met for
each task to be invoked,as well as aboutthe sub-tasks
whichmust be executedto performit. This process is
iterated on each subtask until a completedescription
of the internal functionality of eachproblemsolver is
achieved. Thepresence of a control moduleis therefore essential for achievingthis goal. Moreover,since
a completedescription of a task at somepoint of the
task decomposition hierarchy will also tackle the
internal arrangementof a problemsolver, a modular
framework must be adopted which allows an easy
integration of different problemsolvers.
Accordingto this scheme, which has been dubbed
Distributed Control Blackboard Architecture (DCBA),each problemsolver is considered as a separate modulebased on CBA,as shownin Figure 1. It is
therefore provided with several local knowledge
sources implementing specialized formalisms for
representing and operationalizing knowledge.Each
moduleis also providedwith a local blackboardable
to interface with its knowledgesources. Finally, a
local control moduleis used by establishing the mappings betweenthe tasks modeland the ontologyat the
problemsolver level, therefore taking advantage of
the particular formalismsimplementedby it.
As shownin Figure 1 a central moduleis also provided which is always included in each KBS.This
module implements the "Frame Ontology" and
somehigh level task representation primitives. The
choice of forcing the frameontologyto be includedin
each KBShas been motivatedby its generality. Actually the need to represent object as classes linked
either by class-subclass or class-memberrelationships is alwaysa basic requirementfor representing
medical knowledge. The central module also provides the system blackboard, whichis rooted on the
frame ontology. This meansthat the basic construct
for representing objects within our frameworkis
given by the class. Classes are described by a set of
features, and features mayhave several attributes,
each one able to accept multiple values characterizing it. Locatingthe systemblackboardin the central
module results in having it module independent,
therefore allowing an easy wayfor modulesto communicate and exchange data amongeach other.
3 Problem Solvers
Problemsolvers are used for implementingdifferent representational formalismscombinedwith a reasoning technique. Fromthe point of view of the
computational architecture they maybe considered
as black boxes able to inspect the blackboard. As a
result of their invocationthey mayreturn suitable values to be stored back on the blackboard. Theconfiguration of problemsolvers into an executablesystemis
supported by M-KAT
(Medical KnowledgeAcquisition Tool), a frameworkimplementingand operationalizing D-CBA.Oneof the key issues of the whole
process is the cooperation amongdifferent problem
solvers. This meansthat the results providedby one
of themwill be used by another one and so on. Sucha
cooperation is madeavailable through the uniform
formalism for data representation implementedby
M-KAT.
In a very general scenario, different problemsolvers should be available for performingthe sametask.
Users should be allowedto specify the one to be used
according to somecriteria concerning computational
complexity,explanation, representation capabilities,
and so on. Obviouslyfor this to worka set of translators should be implemented within each problem
solver so that eachone of themis able to translate the
same application knowledgefrom a generic representation formalism into its own representation
8O
schema.Several research efforts are currently being
addressed to this topic, whosemost important are
probably represented by Ontolingua (Gruber, 1993)
and KIF(Geneserethet al., 1992)projects. However,
despite these efforts, the availability of suchtools is
still to come.Dueto this problemin the current implementation each problem solver mayalso replicate
part of the domainknowlegdefor its ownpurposes.
This is neededfor representing knowledgein a more
appropriate wayfor the specific formalismsprovided
by the problemsolver itself.
Several problem solvers have been implemented
so far, and a short descriptions for each one of them
will be provided in the following. Qualitative
Abstractionis one of the simplestavailable. Its aimis
that of extracting froma large set of data a lowernumber of problemfeatures from whicha plausible solution can be inferred. Starting from the values of
several findings by meansof lookuptables the system
is able to comeup with a symbolicvalue expressing
the same information content in a more synthetic
way. NumericalAbstraction maybe considered as a
simple formula evaluator, used wheneveran algebraic expression must be evaluated starting from the
numerical values taken by some component evidences. Episodic Abstraction is aimed instead at
identifying a set of episodes starting from a time
sequenceof data and accordingto someuser specified
criteria. The available criteria, which obviously
dependon the data type of the componentevidence,
include: increasing, decreasing, constant, range, pattern of values,set of valuesetc...
Production Rules is one of the most important
problem solvers available. Besides being used for
modeling domainknowledge,it has been chosen for
implementing control knowledge. Rules are structured as usual, with an IF part including a statement
whichmustbe satisfied for their THEN
part to be activated. Oneof the mainfeatures providedby this problem solver is represented by the possibility of
assembling complex clauses starting from elementary statements, therefore simplifying the wholerule
editing process. This has been accomplishedthrough
the identification of a newformalism based on the
idea of operator. Eachoperator maybe described as
an entity able to convert a statement expressed
through a humanunderstandablesyntax into the corresponding computer executable piece of code, so
that there is always an exact matchingbetweenthe
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fINAL -INTERPRETATION’~...
~"DIAGNOSTIC-INTERPRETATION
D-SHOW
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/MONITORING
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//.,
/
CORTISON
-RESPONSE
T-COLLECT-LEAVES
/T-STRUCTURING-SOLUTION
-SPACE
//THERAPV -ROOT~-’--~T
-ABSTRACTION
jT-EP,SOD,C-AHSTRAGT,ON
T -CVCLIC-ABSTRACTiON~’~.-"--T-TABULAR
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~.--~T -FOCUSED
-ABDUCTION--.._ ~"~T -FORMULA-ABSTRACTION
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"T-ABDUCTION~..,._
~’~
~’T-UNFOCUSED-AHDUCTION ~T-PURSUE-FOCUS
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.~-COLLECT -LEAVE5
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Fig.2 The task network in a KBSdeveloped through M-KAT.
declarative statement madeby the user and the system interpretation. The attempt wepursued has been
to providethe user with the very basic buildingblocks
neededto express the ill-shaped knowledgeinvolved
in medical reasoning. Each rule is therefore composed of several clauses, whereeach clause refers to
a particular value of a feature attribute, checksfor
relationships existing in the ontologyor related to the
inference model,invokesthe executionof a task etc...
Additionally, a clause mayjoin toghether several subclauses into a more complexstructure. For implementing each clause a different operator has been
devisedable to supplythe systemwith all the relevant
information about the syntax and semantics of that
clause.
As a first attempt to integrate external problem
solvers into the architecture we used an Influence
Diagram developed with the GAMEES
environment
(Bellazzi et al., 1991). This problemsolver allows the
definition of a networkcomposedof different nodes
connectedthroughinference relations. Threetypes of
nodes have been implementedso far: decision nodes,
chancenodesand utility nodes.
Additionalresearch is being carried on concerning
several other problemsolvers. It is worthwilementioning QCMF
(Ironi et al., 1993),a tool that supports
the construction of qualitative modelsof pathophysiological systems. The underlying ontology is based
on the notion of compartments,and the dynamicsof
the system are modeledthrough flows representing
transfers between compartments. QCMF
is associated with a simulationalgorithmable to support rapid
prototyping, and therefore it can be used as a standalone tool for the construction of knowledgebased
systems.
4 Modeling Tasks with Metarules
Within the computational model developed,
metarules were adopted as the basic formalism for
implementing inferential knowledge (Lanzola and
Stefanelli, 1993). Metarulesprovedto be very useful
in associating control situations with actions to perform. Moreover, this formalism showeda semantics
whichcould be easily madeoperational into the system. By meansof a special parser each clause can be
directly translated into the matchingpiece of code
executable by the system.
Thelayout of a rule is structured as usual. Eachone
of themis composed
both of a left-hand side and of a
right-hand side. Theleft handside consists of a conjunction of inquiries over the control blackboardor
over the domainblackboard dependingon the particular kind of metarule, while the right handside triggers the activation of a possible action upon the
satisfaction of its premise. Morespecifically two
kinds of metarulescan be distinguished.
Thefirst kind ofmetarulesdeals with the sequencing of the several inference types involved in the
81
inference modelof the system. Theyrepresent possible strategies to adopt as a consequence
of situations
which mayoccur within the control blackboard.
Theirleft parts therefore includetests abouteither the
executionstatus or possible results achievedby previously executed tasks and substasks, while their
right parts are able, uponsatisfaction of their premises, to trigger the activation of a newtask. Their
purposeis to give rise to a task-subtask decomposition hierarchy starting fromthe top level task, whose
goal is to find a solution for the given medicalproblem, and going on each time with simpler sub-tasks
whoseexecution is required in accomplishing the
super-ones. Metarules are therefore grouped into
classes, whereeach class is linked to others, so forming a network. Figure 2 showsthe rule class network
available in a KBSfor managingpatients affected by
Acute Myeloid Leukemia developed with M-KAT.
Each class includes all the rules able to performa
giventask.
Thesecondkind of metarulesis usually located at
the bottom level of a task-subtask decomposition
hierarchy and deals with the process of establishing a
mappingbetweenthe inference modeland the ontology. Those rules are used to provide a detailed
description of howa particular task can be implementedwith references to the activation of domain
specific knowledge
sources. Their left sides therefore
deal with either inferential or ontologicalelements,in
order to successfullyprovideinstances for their right
sides whichwill trigger the activation of particular
areas of the ontology useful to accomplish the
selected task. Rulesbelongingto this class proveto be
very useful in giving the user the possibility of
describing a task in a highly detailed way, allowing
him to exactly specify which ontological entities
should be exploited to achievea goal.
Whena newsystemis being built fromscratch, the
basic set of metarulesprovidedby the systemis simply aimedat implementinga generic instance of the
STModel.This metarule set will be able to implement
all the functionalities providedby the problemsolvers selected duringthe initial configurationstep without exhibiting any particular domain dependent
strategy. Theuser is then left the job of analyzingand
customizing these rules according to the specific
application needs.
82
5 Conclusions
This paper addresses one of the most exciting
issues in AI, that is knowledgeacquisition based on
the idea of knowledgesharing and reusability. The
approachwe adopted wasaimed at identifying a commonlanguage as the basis for defining conceptual
modelsof problemsolving methods. That formalism
provesto be successfulin givingthe user the possibility of modelinghis strategies within the inference
modelof a KBSin a straightforward way. The immediate contribution of this research is therefore a
methodfor providing KAtools also with powerful
capabilities allowing an end user to modelhis own
strategies within a KBSin a context free situation.
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Acknowledgements
This work is part of the AIM project A2034 entitled
"GAMES-H:
a General Architecture for Medical Expert
Systems",
and was supported
by the European
Commission.It is also supported by a MURST
grant.