Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems

Kellner, A.

English

NASA Technical Reports Server

N88-16434
KNOWLEDGE REPRESENTATION BY CONNECTION MATRICES :
A method for the on-board implementation of large
EXPERT SYSTEMS
A. Kellner
Space Systems Group
MBB/ERNO
Bremen, West Germany
ABSTRACT
Extremely large knowledge sources and efficient knowledge access characterizing future
real-life AI applications represent crucial requirements for on-board AI systems due to obvious
computer time and storage constraints on spacecraft.
In this paper a type of knowledge representation and corresponding reasoning mechanism is
proposed which is particularly suited for the efficient processing of such large knowledge bases
in expert systems.
1. INTRODUCTION
Many of today's AI systems are still experimental prototypes aiming at feasibility demonstra-
tions. These systems normally have relatively limited knowledge bases, since research so far has
had its focus more on the conceptual side of reasoning than on the treatment of large knowledge
sources. However, it is becoming increasingly clear that for AI to display its full potential power
not only the reasoning capacity of the human mind needs to be imitated, but also the fact that
it has enormous knowledge sources at its disposal and is able to draw on this knowledge with
extreme efficiency. Given the computer time and space constraints on spacecraft, this aspect
becomes particularly crucial for on-board applications of AI and might necessitate, to a certain
degree, a re-assessment of today's state of the art which has been mainly dictated by the endeav-
our to capture the reasoning aspect of intelligence only, regardless of the software overhead,
high memory space demands, low performance, garbage collection problems etc. this often
entailed.
In this paper a type of knowledge representation and corresponding reasoning mechanism is
thus outlined, which is particularly suited for the processing of large knowledge bases in expert
systems, maintaining the usual functionality of expert systems at the same time.
2. CONNECTIVITY IN THE BRAIN
Looking at the brain as the great example for processing speed and compactness of know-
ledge storage one notices, among other features :
fl ) extremely high performance in classification processes, i.e. fast mapping of large data sets
on discrete descriptors (such as optical or acoustical data on corresponding objects or
words)
f2) relatively low performance in inference processes, i.e. comparatively slow generation of
(long) chains of logically connected elementary operations (such as in doing maths prob-
lems step by step, where each step is usually solved or instantiated by a classification process
"learned by heart", such as performing the simple mapping 2x2 = 4)
f3 ) extremely efficient prompting of associated information (often quite unsolicited)
f4) tolerance to incomplete or locally erroneous information in the classification process,
such as in the identification of partially obliterated images, correction of misspelt words
etc.
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https://ntrs.nasa.gov/search.jsp?R=19880007052 2020-03-20T07:26:43+00:00Z
These features are supplemented by some knowledge about the brain's structure, such as :
sz) incoming continuous data (e.g. optical or acoustical) is spatially discretisized prior to
further processing by resonant excitation of dedicated sensor cells (e.g. in eyes or ears)
s_) these sensor cells are connected by nerve fibres to neurons, which obviously act as logical
gates to the discretisized data, being themselves interconnected by an intricate fibre
structure
s3 ) which also seems to allow for lateral inhibition, i.e. the weakening of the sensory input
of neurons by neighbouring neurons with stronger sensory input, apparently supporting
the generation of excitation maxima in the neuronal network.
An extremely simplified model inferred from these features could consist for example of a
set O = [o= .... On] of "observation cells" oj and a set D = [d I ,. •, dn] of "descriptor neurons"
dK connected by a "connective structure" composed of nerve fibres as shown in Figure 1.
The descriptor neurons represent particular objects, situations, diagnoses etc. characterized
by sets of discrete features.
Each observation cell represents one of these features and is only excited if this feature is
found in the data stream (such as a particular frequency in the case of incoming acoustic data).
The connective structure is such that fibres link each descriptor neuron to all observation cells
characterizing it. Conversely, this means that each observation cell oj is linked to all descriptor
neurons which contain oj as a feature.
After the "local" classification of incoming data by the excitation of discrete observation
cells (structural feature sl ) this excitation is thus passed on via the nerve fibres (feature s. ) in
• ;8
such a way that each descriptor neuron, whose characteristic features are contained m the
incoming data, receives some input• Maximum input obviously is received by the neuron rep-
resenting the situation, object etc. generating the incoming data, and feature s3 given above
could be viewed as a clue to the fact that the "global" classification of the incoming data is
indeed achieved by some comparison of the input intensity of the descriptor neurons.
This could, for example, be achieved by setting the threshhold controlling the "firing" of
a neuron dK so that it only fires if input has come from the full set of all the observation cells
Okj connected to it.
Given the functionality of the human nervous tissue, the local data classification, message
transmission from observation cells to descriptor neurons and particularly the checking of
the firing conditions of each neuron could be performed concurrently, thus leading to an
extremely efficient classification process (feature fl )"
Allowing for secondary, lower thresholds permits the firing of neurons having received input
from less than all the observation cells linked to them, thus generating associations, i.e. situations
or objects sharing features with the primary data source, in an equally efficient manner (feature
f3 )" Moreover, lower threshholds obviously could also be used for approximate classifications
based on some maximum input evaluation in the case of incomplete or locally erroneous data
(feature f, ).
Inference processes could be realized by supplementing the set O of observation cells by
additional cells d'K which are not excited by incoming data, but by the firing of descriptor ceils,
thus providing additional input to the next cycle of mapping observations on descriptors.
The fact that such inference cycles have to proceed in series could be one reason for the relatively
low performance in inference processing, as mentioned in f=.
416
3. CONNECTION MATRICES IN EXPERT SYSTEMS
In the terminology of expert systems, where one distinguishes between a knowledge base and
an inference engine, the main feature of the brain model described in the previous chapter and
illustrated in Figure 1, is the fact that its knowledge base is mainly embodied in the connective
structure between observations and descriptors, the appropriate knowledge representation
being given by "connection matrices"
.1 if observation j is connected to the descriptor k (1)Mkj = otherwise
As implied by the model described in Chapter 2, data processing using this knowledge repre-
sentation requires a prior local classification in which slots representing observations oj of
individual features or symptoms such as "temperature higher than nominal value" or "valve
V1 is open" etc. are instantiated by values oj between 0 and 1 describing the degree to which
the local classification holds (in many cases a two-valued classification : oj = 0 or 1, however,
is sufficient) providing an input ikj (O or oj) to the kth descriptor via the connective structure
Mkj :
ikj = MkjOOj for eachj. (2)
These inputs are collected by accumulation functions F k to provide an integrated input
intensity :
Ik = Fk (ikl,..,ikn), (3)
a possible example of Fk being
Fk = 7-- akj ikj, akj = 1 (4)
J
=kj describing the "implication strength" of oj with respect to dk
The global classification is then performed by identifying the descriptors dk for which Ik = 1
holds. If none can be found, as in the case of incomplete or erroneous information, this thresh-
hold value Io = 1 is reduced to yield approximate classifications as described in chapter 2.
(Details of the treatment of uncertainty on which this reasoning mechanism is based can be
found e.g., in Ref. 1).
The ability to ask the user for missing data in case of incomplete information which is dis-
played by many expert systems, can be achieved by simply looking up the observations oj
connected to the approximate classifications dk via Mkj and asking for them. This method
can also be used to accelerate the classification process by first generating approximate classi-
fications based on just a few randomly picked observations oj # 0 and then automatically
collecting the remaining evidence for just those classifications, a process which might be called
"attention focussing on clues".
Knowledge processing can be further accelerated by parallel processing, dedicating a proces-
sor to each function Fk or at least to subsets of functions Fk,
Consecutive inferences are realized according to the method outlined in chapter 2.
4. ON-BOARD IMPLEMENTATIONS
The main functions of on-board AI systems will be failure diagnosis and recovery, MMI sup-
port (mainly by natural language understanding systems) and planning. AI systems for the first
application fall into the category of typical expert systems, these being roughly characterized
by their functionality as knowledge-based classification systems, as opposed to the somewhat
417
different functionality of natural language understanding and planning systems, although they
also encompass classification processes.
Thus connection matrices can be used particularly for the first type of application but also
for the other two as far as classification is involved.
Obviously knowledge bases using this type of knowledge representation require much less
computer storage and knowledge processing time (even more so if parallel processing is em-
ployed) than in the case of systems based on representations which imply symbolic knowledge
representation, symbol matching techniques, symbol handling overhead, special implementa-
tion languages and garbage collection problems.
For example, whereas the realization of a connection matrix embodying the knowledge to
classify, say, 400 sensor readings into 20.000 different malfunctions roughly requires 1 Mbyte
of computer storage which is still quite feasible for on-board implementation, the realization of
a corresponding rule-based system with tens of thousands of rules would pose a formidable
problem concerning the required memory and processing time.
Moreover, a rule based system of this size could also pose a formidable problem as far as the
development, verification, validation and maintenance of the knowledge-base is concerned,
whereas the simple structure of connection matrices and the underlying reasoning processes
greatly enhance the transparency of the system, the development of the knowledge base simply
being effected by an enumeration of observations and states and an identification of their
interconnections. Methods to automize this process on the basis of FMECA (Failure Mode
Effect and Criticality Analysis) and system simulations are presently being investigated.
5. CONCLUSIONS
On the basis of an assessment of some of the features of the human brain which seem to
pertain to its extremely efficient utilization of very large knowlege sources a type of know-
ledge representation and corresponding inference mechanism for expert systems has been
presented which is particularly suited for the processing of large knowledge bases at compara-
tively low storage and computer time requirements.
Whereas expert systems of this type are conceptually similar to systems involving the treat-
ment of uncertainty, they are representationally different in that the effort in describing the
connectivity between observations and descriptors has been reduced to a minimum by re-
placing symbolic descriptions by simple elements of connection matrices thus eliminating high
storage and computer time demands typical of systems characterized by symbolic knowledge
representation, symbol matching techniques, symbol management overhead, special implemen-
tation languages and garbage collection problems.
6. REFERENCES
. Zimmermann, H.F.
Fuzzy Set Theory and Its Applications
Kluwer-Nighoff Publication, 1986.
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Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

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