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Chapter 2 EMR


Thematic Information Extraction: Artificial Intelligence Dr. John R. Jensen Department of Geography University of South Carolina Columbia, SC 29208 – PowerPoint PPT presentation

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Title: Chapter 2 EMR

Thematic Information Extraction Artificial
Dr. John R. Jensen Department of
Geography University of South Carolina Columbia,
SC 29208
Artificial Intelligence
the study of how to make computers do things
which, at the moment, people do better. But
how do we know when an artificially intelligent
system has been created? We could use the Turing
test, which suggests that if we are unable to
distinguish a computers response to a problem of
interest from a humans response to the same
problem, then the computer system is said to have
intelligence. The test is for an artificial
intelligence program to have a blind conversation
with an interrogator for 5 minutes. The
interrogator has to guess if the conversation is
with an artificial intelligence program or with a
real person. The AI program passes the test if it
fools the interrogator 30 of the time.
Unfortunately, it is very difficult for most
artificial intelligence systems to pass the
Turing test. For this reason, the field of AI as
a whole has paid little attention to Turing
tests, preferring instead to forge ahead
developing artificial intelligence applications
that simply work.
Jensen, 2005
Artificial Intelligence
Artificial intelligence research was initiated in
1955 when Allen Newell and Herbert Simon at the
RAND Corporation proved that computers could do
more than calculate. They demonstrated that
computers were physical symbol systems whose
symbols could be made to stand for anything,
including features of the real world, and whose
programs could be used as rules for relating
these features. In this way computers could be
used to simulate certain important aspects of
intelligence. Thus, the information-processing
model of the mind was born.
Jensen, 2005
Artificial Intelligence
Unfortunately, artificial intelligence was
oversold in the 1960s much like remote sensing
was oversold in the 1970s. General artificial
intelligence problem solving was found to be much
more difficult than originally anticipated.
Scientists could not get computers to solve
problems that were routinely solved by human
experts. Therefore, scientists instead started to
investigate the development of artificial
intelligence applications in micro-worlds, or
very narrow topical areas. This led to the
creation of the first useful artificial
intelligence systems for select applications,
e.g., games, disease diagnosis (MYCIN),
spectrograph analysis (DENDRAL). NASAs REMOTE
AGENT program was the first on-board autonomous
planning program to control the scheduling of
operations for a spacecraft traveling a hundred
million miles from Earth.
Jensen, 2005
Expert Systems
A knowledge-based expert system is defined as
a system that uses human knowledge to solve
problems that normally would require human
intelligence. It is the ability to solve
problems efficiently and effectively in a narrow
problem area and to perform at the level of an
expert. Expert systems represent the experts
domain (i.e., subject matter) knowledge base as
data and rules within the computer. The rules and
data can be called upon when needed to solve
problems. A different problem within the domain
of the knowledge base can be solved using the
same program without reprogramming.
Knowledge-based expert systems are used
extensively in remote sensing research.
Jensen, 2005
Components of a Typical Rule-based Expert System
Domain (thematic) knowledge contained in an
experts mind is extracted in the form of a
knowledge base that consists of hypotheses,
rules, and conditions that satisfy the rules. A
user interface and an inference engine are used
to encode the knowledge base rules, extract the
required information from online databases, and
solve problems. Hopefully, the information is
of value to the user who queries the expert
Jensen, 2005
Expert System User Interface
The expert system user interface should be easy
to use, interactive, and interesting. It should
be intelligent and accumulate user preferences in
an attempt to provide the most pleasing
communication environment possible. The figure
depicts a commercially available Knowledge
Engineer interface that can be used to develop
remote sensingassisted expert systems. This
expert system shell was built using
object-oriented programming. All of the
hypotheses, rules, and conditions for an entire
expert system may be viewed and queried from the
single user interface.
Jensen, 2005
Creating the Knowledge Base
Images, books, articles, manuals, and periodicals
have a tremendous amount of information in them.
Practical experience in the field with
vegetation, soils, rocks, water, atmosphere, and
urban infrastructure is also important. However,
a human must comprehend the information and
experiences and turn it into knowledge for it to
be useful. Many human beings have trouble
interpreting and understanding the information in
images, books, articles, manuals, and
periodicals. Similarly, some do not obtain much
knowledge from field work. Fortunately, some
laypersons and scientists are particularly adept
at processing their knowledge using three
different problem-solving approaches
Jensen, 2005
Creating the Knowledge Base
  • Algorithms using conventional computer programs
  • Heuristic knowledge-based expert systems
  • Human-derived rules
  • Machine-derived rules
  • Artificial neural networks

Jensen, 2005
Creating the Knowledge Base
Algorithmic Approaches to Problem Solving
Conventional algorithmic computer programs
contain little knowledge other than the basic
algorithm for solving a specific problem, the
necessary boundary conditions, and data. The
knowledge is usually embedded in the programming
code. As new knowledge becomes available, the
program has to be changed and recompiled.
Jensen, 2005
Characteristics that Distinguish Knowledge-based
Expert Systems from Conventional Algorithmic
Problem-solving Systems
Creating the Knowledge Base
Heuristic Knowledge-based Expert System
Approaches to Problem Solving
Knowledge-based expert systems, on the other
hand, collect many small fragments of human
know-how for a specific application area (domain)
and place them in a knowledge base that is used
to reason through a problem, using the knowledge
that is most appropriate. Characteristics that
distinguish knowledge-based expert systems from
conventional algorithmic systems are summarized
in the table. Heuristic knowledge is defined as
involving or serving as an aid to learning,
discovery, or problem solving by experimental and
especially by trial-and-error methods. Heuristic
computer programs often utilize exploratory
problem-solving and self-educating techniques (as
the evaluation of feedback) to improve
Jensen, 2005
Characteristics that Distinguish Knowledge-based
Expert Systems from Conventional Algorithmic
Problem-solving Systems
Jensen, 2005
Creating the Knowledge Base
The Problem with Experts Unfortunately,
most experts really do not know exactly how they
perform their expert work. Much of their
expertise is derived from experiencing life and
observing hundreds or even thousands of case
studies. It is difficult for the experts to
understand the intricate workings of complex
systems much less be able to break them down into
their constituent parts and then mimic the
decision-making process of the human mind.
Therefore, how does one get the knowledge
embedded in the mind of an expert into formal
rules and conditions necessary to create an
expert system to solve relatively narrowly
defined hypotheses (problems)? This is the
responsibility of the knowledge engineer.
Jensen, 2005
Creating the Knowledge Base
The knowledge engineer interrogates the domain
expert and extracts as many rules and conditions
as possible that are relevant to the hypotheses
(problems) being examined. Ideally, the knowledge
engineer has unique capabilities that allow him
or her to help build the most appropriate rules.
This is not easy. The knowledge engineering
process can be costly and time-consuming.
Recently, it has become acceptable for a domain
expert (e.g., biologist, geographer) to create
his or her own knowledge-based expert system by
querying oneself and hopefully accurately
specifying the rules associated with the problem
at hand, for example, using ERDAS Imagines
expert system Knowledge Engineer. When this
activity takes place, the expert must have a
wealth of knowledge in a certain domain and the
ability to formulate a hypothesis and parse the
rules and conditions into understandable elements
that are amenable to the knowledge
representation process.
Jensen, 2005
Knowledge Representation Process
The knowledge representation process normally
involves encoding information from verbal
descriptions, rules of thumb, images, books,
maps, charts, tables, graphs, equations, etc.
Hopefully, the knowledge base contains sufficient
high-quality rules to solve the problem under
investigation. Rules are normally expressed in
the form of one or more IF condition THEN
action statements. The condition portion of a
rule statement is usually a fact, e.g., the pixel
under investigation must reflect gt 45 of the
incident near-infrared energy. When certain rules
are applied, various operations may take place
such as adding a newly derived derivative fact to
the database or firing another rule. Rules can be
implicit (slope is high) or explicit (e.g., slope
gt 70). It is possible to chain together rules,
e.g., IF c THEN d IF d THEN e therefore IF c
THEN e. It is also possible to attach confidences
(e.g., 80 confident) to facts and rules.
Jensen, 2005
Knowledge Representation Process
For example, a typical rule used by the MYCIN
expert system is IF the stain of the organism is
gram-negative     AND the morphology of the
organism is rod         AND the aerobicity of
the organism is anaerobic             THEN there
is strong suggestive evidence (0.8) that the
class of the organism is
Enterobacter iaceae.
Following the same format, a typical remote
sensing rule might be IF blue reflectance is
(Condition) lt 15     AND green
reflectance is (Condition) lt 25 AND
red reflectance is (Condition) lt 15
AND near-infrared reflectance is (Condition) gt
45 THEN there is strong
suggestive evidence (0.8) that the
pixel is vegetated.
Jensen, 2005
Knowledge Representation Process
Decision Trees The best way to
conceptualize an expert system is to use a
decision-tree structure where rules and
conditions are evaluated in order to test
hypotheses. When decision trees are organized
with hypotheses, rules, and conditions, each
hypothesis may be thought of as the trunk of a
tree, each rule a limb of a tree, and each
condition a leaf. This is commonly referred to as
a hierarchical decision-tree classifier (e.g.,
Swain and Hauska, 1977 Jensen, 1978 Kim and
Landgrebe, 1991 DeFries and Chan, 2000 Stow et
al., 2003 Zhang and Wang, 2003). The purpose of
using a hierarchical structure for labeling
objects is to gain a more comprehensive
understanding of relationships among objects at
different scales of observation or at different
levels of detail.
Jensen, 2005
Knowledge Representation Process
Decision Trees A decision tree takes as
input an object or situation described by a set
of attributes and returns a decision. The input
attributes can be discrete or continuous. The
output value can also be discrete or continuous.
Learning a discrete-valued function is called
classification learning. Learning a continuous
function is called regression. We will
concentrate on Boolean classification wherein
each example is classified as true (positive) or
false (negative). A decision tree reaches its
decision by performing a sequence of tests.
Jensen, 2005
Knowledge Representation Process
Hypothesis 1 the terrain (pixel) is
suitable for residential development that makes
maximum use of solar energy (i.e., I will be able
to put solar panels on my roof ).
Jensen, 2005
Knowledge Representation Process
Specify the expert system rules
Heuristic rules that the expert has learned over
time are the heart and soul of an expert system.
If the experts heuristic rules of thumb are
indeed based on correct principles, then the
expert system will most likely function properly.
If the expert does not understand all the subtle
nuances of the problem, has left out important
variables or interaction among variables, or
applied too much significance (weight) to certain
variables, the expert system outcome may not be
accurate. Therefore, the creation of accurate,
definitive rules is extremely important. Each
rule provides the specific conditions to accept
the hypothesis to which it belongs. A single rule
that might be associated with hypothesis 1 is
specific combinations of terrain slope,
aspect, and proximity to shadows result in
maximum exposure to sunlight.
Jensen, 2005
Knowledge Representation Process
  • Specify the rule conditions
  • The expert would then specify one or more
    conditions that must be met for each rule. For
    example, conditions for the rule stated above
    might include
  • slope gt 0 degrees, AND
  • slope lt 10 degrees (i.e., the terrain should
    ideally lie on terrain
  • with 1 to 9 degrees slope),
  • aspect gt 135 degrees, AND
  • aspect lt 220 degrees (i.e., in the Northern
    Hemisphere the
  • terrain should ideally face
    south between 136 and 219
  • degrees to obtain maximum
    exposure to sunlight), AND
  • the terrain is not intersected by shadows cast
    by neighboring
  • terrain, trees, or other
    buildings (derived from a viewshed
  • model).

Jensen, 2005
Knowledge Representation Process
A human-derived decision-tree expert
system with a rule and conditions to be
investigated by an inference engine to test
Hypothesis 1 the terrain (pixel) is suitable for
residential development that makes maximum use of
solar energy (i.e., I will be able to put solar
panels on my roof ).
Jensen, 2005
Inference Engine
The terms reasoning and inference are used to
describe any process by which conclusions are
reached. Thus, the hypotheses, rules, and
conditions are passed to the inference engine
where the expert system is implemented. One or
more conditional statements within each rule are
evaluated using the spatial data (e.g., 135 lt
aspect lt 220). Multiple conditions within a rule
are evaluated based on Boolean AND logic. While
all of the conditions within a rule must be met
to satisfy the rule, any single rule within a
hypothesis can cause that hypothesis to be
accepted or rejected. In some cases, rules within
a hypothesis disagree on the outcome and a
decision must be made using rule confidences
(e.g., a confidence of 0.8 in a preferred rule
and a confidence of 0.7 in another) or the order
of the rules (e.g., preference given to the
first) as the factor. The confidences and order
associated with the rules are stipulated by the
Jensen, 2005
Inference Engine
The inference engine interprets the rules
in the knowledge base to draw conclusions. The
inference engine may use backward- or
forward-chaining strategies or both. Both
backward and forward inference processes consist
of a chain of steps that can be traced by the
expert system. This enables expert systems to
explain their reasoning processes, which is an
important and positive characteristic of expert
systems. You would expect a doctor to explain how
he or she came to a certain diagnosis regarding
your health. An expert system can provide
explicit information about how a particular
conclusion (diagnosis) was reached.
Jensen, 2005
Inference Engine
An expert system shell provides a
customizable inference engine. Expert system
shells come equipped with an inference mechanism
(backward chaining, forward chaining, or both)
and require knowledge to be entered according to
a specified format. Expert system shells qualify
as languages, although with a narrower range of
application than most programming languages.
Typical artificial intelligence programming
languages include LISP, developed in the 1950s,
PROLOG, developed in the 1970s, and now
object-oriented languages such as C.
Jensen, 2005
Expert Systems Applied to Remote Sensor Data
The use of expert systems in remote sensing
research will be demonstrated using two different
methodologies used to create the rules and
conditions in the knowledge base. The first
expert system classification is based on the use
of formal rules developed by a human expert. The
second example involves expert system rules
derived automatically by an inductive
machine-learning algorithm based on training data
that is input by humans into the system. Both
methods are used to identify white fir forest
stands on Maple Mountain in Utah County, Utah,
using Landsat Enhanced Thematic Mapper Plus
(ETM) imagery and topographic variables
extracted from a digital elevation model of the
Jensen, 2005
Expert System Applied to Remote Sensor Data
A hypothesis (class), variables, and
conditions necessary to extract white fir (Abies
concolor) forest cover information from Maple
Mountain, Utah, using remote sensing and digital
elevation model data. The Boolean logic with
which these variables and conditions are
organized within a chain of inference may be
controlled by the use of rules and sub-hypotheses.
Jensen, 2005
Classification of White Fir on Maple Mountain,
Utah County using Hierarchical Decision Tree
1 1 m NAPP aerial photography (acquired 17 Aug
1994) is draped over a 10 10 m USGS DEM.
Jensen, 2005
30 30 USGS DEM
Shaded Relief
ETM Panchromatic
ETM RGB 5,4,2
ETM RGB 4,3,2
Jensen, 2005
Terrestrial Photograph
ETM Panchromatic
ETM RGB 5,4,2
Experts Classification of White Fir
ETM RGB 4,3,2
Jensen, 2005
Hierarchical Decision Tree Classifier
ETM Panchromatic
Experts Model
Predicted White Fir
Jensen, 2005
Rules and Conditions Based on Machine Learning
The heart of an expert system is its
knowledge base. The usual method of acquiring
knowledge in a computer-usable format to build a
knowledge base involves human domain experts and
knowledge engineers, as previously discussed. The
human domain expert explicitly expresses his or
her knowledge about a subject in a language that
can be understood by the knowledge engineer. The
knowledge engineer translates the domain
knowledge into a computer-usable format and
stores it in the knowledge base.
Jensen, 2005
Rules and Conditions Based on Machine Learning
  • This process presents a well-known problem
    in creating expert systems that is often referred
    to as the knowledge acquisition bottleneck. The
    reasons are
  • the process requires the engagement of the
    domain expert and/or knowledge
  • engineer over a long period of time, and
  • although experts are capable of using their
    knowledge for decisionmaking, they
  • are often incapable of articulating
    their knowledge explicitly in a format that is
  • sufficiently systematic, correct, and
    complete to be used in a computer
  • application.

Jensen, 2005
Rules and Conditions Based on Machine Learning
To solve such problems, much effort has been
exerted in the artificial intelligence community
to automate the building of expert system
knowledge bases. Machine learning is defined as
the science of computer modeling of learning
processes. It enables a computer to
acquire knowledge from existing data or theories
using certain inference strategies such as
induction or deduction. We will focus only on
inductive learning and its application in
building knowledge bases for image analysis
expert systems.
Jensen, 2005
Rules and Conditions Based on Machine Learning
A human being has the ability to make
accurate generalizations from a few scattered
facts provided by a teacher or the environment
using inductive inferences. This is called
inductive learning (Huang and Jensen, 1997). In
machine learning, the process of inductive
learning can be viewed as a heuristic search
through a space of symbolic descriptions for
plausible general descriptions, or concepts, that
explain the input training data and are useful
for predicting new data. Inductive learning can
be formulated using the following symbolic
Jensen, 2005
Hierarchical Decision Tree Classifier Based on
Inductive Machine Learning Production Rules
ETM Panchromatic
C5.0 Model
Jensen, 2005
Predicted White Fir
Machine Learning-derived Classification Map
Jensen, 2005
Rules and Conditions Based on Machine Learning
The following topics are covered in
Geography 751 Seminar in Remote Sensing -
Machine Learning - Neural Networks
Jensen, 2005