Similarity networks are a powerful form of knowledge representation that are useful for many artificial intelligence applications. Similarity networks are used in applications ranging from information analysis and case based reasoning to machine learning and linking symbolic to neural processing. Strengths of similarity networks include simple construction, intuitive object storage, and flexible retrieval techniques that facilitate inferencing. Therefore, similarity networks provide great potential for space applications

Bailey, David

Feinstein, Jerald

Thompson, Donna

English

NASA Technical Reports Server

N88-16411 •
Similarity Networks as a Knowledge Representation for Space Applications
David Bailey, Donna Thompson and Jerald Feinstein
ICF/Phase Linear Systems, Incorporated
9300 Lee Highway
Fairfax, VA 22031-1207
(703) 934-3800
ABSTRACT: Similarity networks are a powerful form of knowledge
representation that are useful for many artificial intelligence
applications. Similarity networks are used in applications ranging from
information analysis and case-based reasoning to machine-learning and
linking symbolic to neural processing. Strengths of similarity networks
include simple construction, intuitive object storage, and flexible
retrieval techniques that facilitate inferencing; therefore similarity
networks provide great potential for space applications.
INTRODUCTION
Space exploration depends upon computers to aid in such tasks as
navigational control, mechanical and electrical systems monitoring, and
flight tracking. As equipment used in space becomes more complex, the
role of computers becomes vital in the areas of design, monitoring,
control, and maintenance. To keep the pace with this complexity, computer
hardware has developed faster processers using RISC architectures and
parallel processing. Computer software must now become more intelligent
as well as more abundant. Intelligent software is needed to enhance the
capabilities of limited personnel, whether they be crew members or design
teams. Potential areas for increased use of intelligent software include
system design, decision support, simulation, and information retrieval.
The intelligent software necessary to facilitate the various tasks
mentioned above utilizes artificial intelligence (AI) techniques.
Artificial intelligence applications are based upon some type of mapping
between concepts in the physical world and abstract software datatypes.
This mapping is known as knowledge representation. Choosing the proper
knowledge representation is vital to the success of an artificial
intelligence application. A good knowledge representation has the
following properties:
o Makes important things explicit,
o Exposes constraints,
o Is complete and concise, and
o Is easy to use.
Similarity networks are a powerful form of knowledge representation
that are well-suited to many artificial intelligence applications. This
is especially true in space applications due to the ill-defined nature of
search spaces and formerly intractable problems facing aerospace and
astronautics engineers. Created to assist machine learning programs,
similarity networks may also be used to analyze information, reason from
experience, and support various other AI techniques.
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SIMILARITY NETWORKS DEFINED
Similarity networks are a knowledge representation technique that
stores and links objects based upon their similarity to each other.
Similarity networks are composed of clusters of objects that are connected
via weighted links. As the networks become more complex, hierarchies of
clusters and networks are formed. The objects represent the physical or
abstract concepts that are being stored in the network. An object may be
as simple as a letter of the alphabet or as complex as an electrical
circuit design. The weighted links connect any two objects and designate
the degree of similarity between the objects.
SV = Saturn V Rocket
SH = Space Shuttle
SL = Space Lab
LE = LEM
SS = Space Station
TR = Triton
WS = Weather Satellite
PP = Planetary Probe
CS = Comm. Satellite
TV = Terrain Vehicle
Figure i.
An example of a similarity network, shown in Figure i, describes the
relationships between and among various classes of space vehicles. Notice
that functionality characteristics such as propulsion and data gathering
as well as physical attributes such as dimensions and mass help to form
the clusters within this network. For example, a planetary probe is
linked to a weather satellite in part because they form similar
exploratory tasks.
Similarity networks were first described in Patrick Winston's thesis
"Learning Structural Descriptions from Examples" [Winston75]. They are
later mentioned in Minsky's paper "A Framework for Representing Knowledge"
[Winston 75]. The first implementation of a similarity network was the
result of thesis work done by David Bailey [Bailey 86]. Mr. Bailey
experimented in methods of constructing, searching, representing, and
evaluating similarity networks. Using this experience, ICF/Phase Linear
Systems has been researching the use of similarity networks in artificial
intelligence for a broad range of applications in industry, government,
the military, and in space.
CONSTRUCTING A SIMILARITY NETWORK
The first step in constructing a similarity network is choosing and
describing the objects to be stored. Objects are chosen based upon the
type of application to be built. For example, to build software that
reasons from experience, descriptions of situations and outcomes are used
to build the network. As a second example, if a tool identification
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program is desired, descriptions of various types of tools and their
purposes need to be stored.
The physical, conceptual, or abstract objects to be stored in the
network must then be described. This may be accomplished with object-
attribute-value triplets, property lists, natural language texts, or other
knowledge representations depending upon the type of marcher used to
compare the objects.
The matcher compares each object to all of the others and returns
the number of features in common and the number of features unique to each
object. These numbers are then put into an equation that determines the
similarity score for the objects. If the score exceeds the minimum
threshold established by the system designer, the objects are linked in
the resulting similarity network.
Several choices are made in constructing a similarity network,
including the proper similarity equation to use, the weights to use within
the equation, the description of the objects, and the threshold at which
links are to be formed. The quality of the network can be determined
using heuristics that examine the clusters within a network, as well as
the intuition of the network developer. An advantage of the similarity
network approach is that it has a built-in form of sensitivity analysis
for a final evaluation of the network.
An iterative construction process produces many views of the network.
This ensures that specific knowledge representation requirements and
objectives are met. In addition, new relationships between the objects
that were previously undiscovered now emerge as the network construction
parameters are varied. By creating an awareness of new relationships,
similarity networks provide invaluable assistance in exploring complex,
qualitative, and ill-defined problem spaces.
SIMILARITY NETWORKS IN AI APPLICATIONS
Similarity networks are an effective knowledge representation for
many AI applications. Two types of applications particularly well suited
to similarity networks are information analysis and case-based reasoning.
Information Analysis
Information analysis applications process new data or take a fresh
look at existing data. Similarity networks facilitate several information
analysis applications including:
o Object identification
o Resource substitution
o Perspective changing
o Knowledge acquisition.
Each of these applications is described in more detail in the paragraphs
below. To illustrate some practical uses of these applications, an
example from a spacecraft electrical systems design scenario will
accompany each description.
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An object identification application takes as input the description
of an object and produces as output the name or category of that object.
Identification applications search the similarity network for an object
that matches the input description. If there is not an exact match the
system finds the closest match. This results in three classifications,
the most similar object, the category (or prototypical member of the
category), and the match score. The match score provides a measure of
quality - or similarity - for the object returned. As an example, a
spacecraft electrical systems design assistant with a built-in
identification application might be used in conjunction with a visual
scanner to search for sodder bridges or other problems within a circuit
board in a control panel or other instrumentation.
Similarity networks also provide a good knowledge representation for
resource substitution application. A resource substitution application
increases efficiency by promoting the creative use of materials. For
example, if wire wrap were needed to connect two components but was
unavailable, a resource substitution application might recommend the use
of a sodder bridge based upon the similarity between the two object's
functionality. In the same way, unobvious substitutions can be made.
Again from our electrical systems design example, an unobvious
substitution might be for the application to suggest using the heat sink
of a neighboring electrical system to be a heat source of the
environmental system. This is also an example of a change in perspective
which is discussed next.
A change in perspective provides a different look at the objects in
the similarity network. The change in perspective allows different
properties of network objects to be ranked as more important in certain
situations. In the heat sink to heat source example above, the heat sink
is viewed as a resource and not as a waste. This provides an efficient
solution to the problem of supplying heat.
The final information analysis example is knowledge acquisition.
Given situations as objects, certain types of induction may be used to
produce rules from recurring situations. This technique may be used
either on previously acquired information or dynamically in conjunction
with an expert system that learns as it goes. From the electrical systems
design example, recurring use of capacitors to act as surge protectors
might prompt the system to form a rule that "if a surge protector is
needed, then use capacitors".
Case-based Reasoning Applications
The second major category of application utilizing similarity
networks as a form of knowledge representation is case-based reasoning or
reasoning by example. A case-based reasoner performs knowledge-based
functions somewhat like those of an expert system. For example, a case-
based reasoner built upon a similarity network might perform the task of
control monitoring in a life support system.
A case-based reasoner would operate similar to the identification
application described above using situations as objects. An exact match
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would produce the outcome stored with the situation in the similarity
network. This outcome would tell the operator what to expect in the given
situation. Inexact matches produce results qualified by their similarity
scores. The case-based reasoner could also be used to hypothesize on
speculative information. If systems operators wanted to determine how
high a reading could climb before approaching a dangerous level, they
could enter potential readings to monitor the reasoner's reactions.
Case-based reasoners have several advantages over production rule
forms of expert systems. First, case-based reasoners are easy to build.
Sample situations and outcomes are entered directly into the similarity
network. Knowledge engineers are not required to supervise the
acquisition of information. Second, the initial information is retained
by the reasoner, making it possible to return to the initial data to test
assumptions. Finally, the case-based reasoner - using the match score -
knows when it does not have an appropriate solution.
Similarity networks provide an effective knowledge representation for
other types of artificial intelligence applications such as machine
learning, analogical reasoning, classification, and machine vision. More
traditional forms of computing that require information storage and
retrieval may also benefit from the power and promise of similarity
networks.
SIMILARITY NETWORKS IN SPACE APPLICATIONS
The information analysis and case-based reasoning applications
discussed above are directly relevant to space applications. Information
analysis systems can be used for electrical and environmental systems
design, foreign terrain exploration, manufacturing quality control, sensor
data identification, and systems configuration support to name a few.
Case based reasoners can be applied to systems design, control, and
monitoring, physical security advising, and flight tracking.
The following is a sample of the type of information that could be
obtained from a similarity network based application in exploratory
scanning:
Person: What is the fuzzy, round object located at the lower right
portion of the screen?
Computer: I don't know. It is metallic. (75% match score)
Person: It has an unusually high level of radioactivity. Does that help
to identify it?
Computer: Changing perspective. It may be the result of the destruction
of a nuclear-power device. By the shape, it appears to be a
cooling rod. (60%)
Person: How can we retrieve, analyze, and store the object safely?
Computer: Matches radioactive transport situation in case histories (100%
match). Retrieve with a robotic arm. Pack with aqueous
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transport solution in lead containers.
This example is hypothetical, but it is indicative of the types of systems
that can be built with similarity networks.
CURRENT IMPLEMENTATIONS
Similarity networks are currently implemented on two systems. A
research version is running on a Symbolics LISP machine at the MIT AI
Laboratory. The second is an applications-oriented version at ICF/Phase
Linear Systems. The ICF/Phase Linear system is currently being used for
three different projects. In the first project we are attempting to link
symbolic processing with neural networks. In the second we are developing
a business tracking system that monitors successful small businesses over
time. The third project is a legal assistant that works from case history
data to help solve crimes and predict terrorist attacks.
CONCLUSIONS
Similarity networks are a powerful form of knowledge representation
that can be used for a wide variety of artificial intelligence
applications. Certain types of applications, such as information analysis
and case-based reasoning, are a particularly well-suited for similarity
networks. These artificial intelligence programs are applicable to space
applications in such areas as systems design, control, and maintenance,
sensor input identification, exploration, and knowledge-based navigation
of autonomous systems. Current implementations of similarity networks
indicate that they provide a good knowledge representation for flexible,
interactive artificial intelligence applications.
REFERENCES
Bailey, D.L. Similarity Networks as a Means of Indexing and Retrieving
Descriptions. Bachelor's Thesis for the Dept. of Electrical
Engineering and Computer Science at the Massachusetts Institute of
Technology. 1986.
Winston, P.H. The Psychology of Computer Vision. McGraw-Hill Book
Company. 1975.
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