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7
Artificial Intelligence
Artificial intelligence (AI) looms large in the public's perception
of the future of computer science and technology and has contributed
much to the emergence of this field. In this chapter, we focus on
what we consider to be particularly promising aspects of AT: sensory
computing, expert systems, deeper cognitive systems, and robotics.
SENSORY COMPUTING
Understanding the workings of the human sensory apparatus
and implementing comparable capabilities on machines, particularly
speech and vision, is an important scientific challenge and a tech-
nological imperative. It is vita] to the development of autonomous
devices such as robots and for improved communication between
machines and their human users.
In the area of speech understanding, it has proved to be more
difficult than expected to get computers to recognize untrained hu-
man speech. At present, systems can recognize a limited number of
words, they take a relatively long time to do it, and speakers must
usually pause between words. Even this modest success requires the
speaker to familiarize the computer with the unique qualities of his or
her voice by reading aloud lists of all the words to be used. For such
speaker-dependent systems, the machine, after training, can achieve
word recognition of several thousand words with a success rate in
51
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the upper 90 percent range. Speaker-independent systems that can
understand continuous speech appear to be feasible but are at least
3 to 5 years away. Advances in natural language understanding
and cognitive science, combined with the potential of multiprocessor
systems to provide the huge processing power required, hold out a
big promise but not a guarantee of expanded capabilities for speech
comprehension via computer.
Machine vision represents another critical area in which, as in
speech, significant progress is likely to depend on the combination of
cognitive research with the evolution of massively parallel and most
probably special-purpose multiprocessor systems. Machine vision is
the process of deriving useful information about a scene from images,
for example, the conversion of a huge list of numbers representing
the light intensities of millions of minute dots, which make up an
overall picture as perceived by a video camera, into a description of
the pictured objects, their location, and spatial relationships. This
description may be used, in turn, to control a manipulator that picks
up an object or to guide a vehicle on a road.
Since as long ago as 1950, demonstrations of machine vision
have included recognition of printed characters, medical unage anal-
ysis (e.g., counting blood cells), some industrial vision (e.g., printed
circuit board inspection), flexible assembly, and military target de-
tection. Despite these successes, however, the capabilities of machine
vision today are still largely limited to printed character recognition,
medical image analysis, and some industrial inspection. This is due
in part to the low computational power available and in part to the
youth of current theoretical foundations and algorithms that address
visual perception.
Advances in machine vision are expected to have a significant
impact over a large number of uses for the same reason that our
own eyes are so important in everything that we do. Autonomous
systems, be they military vehicles or robots on the factory floor,
will have vision with far greater capability and flexibility than to-
day's repetitive-motion robots. Another important consequence of
improved machine vision and better speech comprehension will be
the evolution of more natural interfaces that span nearly all applica-
tions of computers and permit users to speak or show things to their
machines as naturally as they do in interacting with people.
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E:XPl:RT SYSTEMS
Expert systems involve techniques for representing knowledge
and methods by which that knowledge can be used by a machine to
reason toward the solution of problems that are difficult enough to
require significant human expertise for their solution.
Every expert system consists of three principal parts: the knowI-
edge base, the reasoning or inference methods, and their interface
with the user. Knowledge bases contain factual knowledge and
heuristic knowledge. The factual knowledge, like the knowledge in
textbooks or journals, is widely shared and easily obtained. In con-
trast, the heuristic knowledge is rarely discussed and is largely in the
private domain of experts. It is the knowledge of good practice, good
judgment, and plausible reasoning in the field. It is the knowledge
that underlies "the art of good guessing.
The inference methods used by expert systems are often based
on propositional calculus or predicate logic. Most commonly used are
"forward chainings methods, which follow causal paths from condi-
tions presented to the program to conclusions reached by the program
(modus pollens applied repeatedly), or Backward chainings meth-
oafs, which proceed from goal statements to conditions (same logic
backward). Probabilistic frameworks and some ad hoc frameworks
are also used for inference.
As one would expect from a technology so broadly conceived,
the span of applications ~ as wide ~ the world of professional and
semiprofessional work. The earliest applications of expert systems
were in such esoteric areas as the analysm of chemical data, medical
diagnosis and therapy planning, the interpretation of data from oil
wed logging, and the defense-related interpretation of deep-ocean
sound. As the applications of expert systems began to grow in
the m~-1980s, other mainline commercial and industrial applica-
tion~ began to emerge. Finally, in government, expert systems are
used to assist government officteab in interpreting health care man-
agement data and complex pension laws. In m-1987, there were
approximately 1,500 applications in use and several thousand under
development (Feigenbaum et al. 1988~.
As expert system continue to evolve, it is becoming apparent
that two applications areas manufacturing (in particular, the white-
colIar aspects) and financial services are beginning to dominate. In
each of these, the sheer economic volume of goods and services means
that even small enhancements to the average human professional skis
in decision making is leverage for great economic gain. Examples of
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expert systems in manufacturing include: the design of a manufac-
turable configuration of subsystems, given a customer order for a
rn~nicomputer, and the design of an associated floor layout; real-time
scheduling and rescheduling (due to a machine failure) of the progress
of wafer~in-process in a huge microchip manufacturing facility; and
planning the manufacturing process for jet fighter parts. In finance,
expert systems are used to assist bank officers In deciding the credit
worthiness of a loan applicant and to asset insurance underwriters
in deciding price and terms for insurance contracts.
Probably more than half of today's expert systems are used for
diagnostic purposes, such as assisting auto mechanics in diagnosing
and repairing subsystems of automobiles and carrying out real-time
remote diagnostic tests of massive steam turbine generators. Appli-
cations to diagnosis wiD continue to be widespread. Motivating this
is the increasing complexity of devices and systems used throughout
industry. Unassisted human abilities in problem solving, training,
and retraining cannot keep pace with current and expected develop-
ments.
There are a number of key research issues in expert systems. (~)
Knowledge representation: How shad the knowledge of a domain of
human endeavor and the world in which it is situated be represented
as data structures in the memory of a computer? (2) Knowledge
utilization: How can this knowledge be used for problem solving?
Essentially, this is the question of the design of inference (reasoning)
procedures and frameworks. (3) Knowledge acquisition: How wiD it
be possible to acquire the knowledge automatically (machine learn-
ing) or at least semiautomatically (transfer of expertise from humans,
their texts, or their data)? (4) Large knowledge bases: The power
of expert systems resides in the specific knowledge of the problem
domain, and for systems to be powerful they must contain a large
amount of high-quality knowledge. Accordingly, an enormous knowI-
edge infrastructure needs to be codified and represented for machine
use and, as one would expect, this is ant] wiD continue to be a huge
endeavor in which machines may participate, as In (3) above.
Applications of precise knowledge delivery are also of increasing
importance. A knowledge delivery application is one in which the
right knowledge, in the context of a problem or a service, is delivered
at the right moment for a human professional to consider. For exam-
ple, one commercially available knowledge delivery system advises
clinical pathologists about tissue diseases and associated features.
Such applications are motivated by the great complexity of human
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system and procedures that are now in place a complexity that
even the mind of a specialist cannot encompass. A knowledge deliv-
ery application is, in essence, a "living rulebook or textbooks that
delivers knowledge in context.
As we look toward the future, the volume of expert systems is
expected to grow and blend with the great stream of more conven-
tional data processing and numeric applications. There is a certain
inevitability at work here. As the cost of computers continues to fall
during the coming two decades, many more of the practitioners of
the worId's professions will be persuaded to turn to information pro-
cessing for assistance in managing the increasing complexity of their
daily knowledge-related tasks. The computers that will act as intel-
ligent assistants for these professionals will have to have reasoning
capabilities and knowledge.
In tune, we wall undoubtedly achieve a broad reconceptualiza-
tion of what ~ meant by an expert system. In the broader concept,
the system will be conceived as a collegial relationship between an
intelligent computer agent and an intelligent person (or persons).
Each will perform tasks that it/he does best, and the intelligence of
the system will be a result of the collaboration. Trues and problems
expected to dominate the agenda of future expert system researchers
include: (1) the creation of more powerful, general, and easy-to-use
programming systems that will liberate the user from knowledge
engineering intermediaries; (2) new knowledge representation for-
malisms and techniques, adequate and effective for representing a
broad body of general knowledge about the everyday world, the
worlds of science and engineering knowledge, biological and medical
knowledge, and so on; (3) new reasoning methods that escape the
elegant but rigid bounds of propositional and predicate logic and
reuse old knowledge for solving new problems—forerunners of such
methods are now called reasoning-by-analogy, case-based reasoning,
script-based reasoning, and chunking; and (4) new machine learning
methods for acquiring knowledge based on analogies, on abstractions
from internal problem-solving processes, on watching human expert
problem solving, and on the automated reading of textual material
from journals and textbooks.
We can envision that as society changes from industrial to post-
industrial and as work becomes increasingly the work of professionals
and knowledge workers, the power tools will be expert systems. The
economic and social well-being of advanced societies increasingly will
be the result of Working smarter" rather than "working harder," and
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expert systems will be agents of that change. Knowledge is power in
human affairs, and expert systems are amplifiers of human thought
and action.
DEEPER COGNITIVE SYSTEMS
Another unportant focus in Al research involves the attempt to
understand and model the deeper cognitive activities fundamental to
intelligence, including learning, explaining, planning, and hypothe-
sizing. Research in this area Is an interdisciplinary enterprise, involv-
ing a synthesis of concepts from experimental psychology, linguistics,
neuroscience, and computer science; advances hold the dual promise
of increasing understanding of human cognitive processes and ~ntro-
ducing more and more intelligence into the computer. Listed below
are some of the promising current thrusts of this research.
1. The organization of memory. Work in the cognitive Al field
has overturned the once-prevalent view that human memory could
be viewed as a largely unorganized mental filing cabinet. AT re-
searchers have developed several sophisticated and influential models
of how humans organize their knowledge. Although these theories,
which include semantic networks, frames, and scripts, involve differ-
ent methods of representing the memory's organization, they share
a common assertion that memory structure consists of a network of
stored associations, with various types of information stored at each
node of the network. Collectively these memory models have helped
in the construction of knowledge-based systems that use contextual
information to tackle specific problems.
2. Learning from practice. After a long lull caused by dmappoint-
ments with early experiments on learning machines some 20 years
ago, recent advances in the development of computer systems have
given rise to programs that exhibit modest yet continuous learning
from practice on the tasks that they perform, much as humans do.
These recent innovations have important implications for computer
science in that they represent key steps toward the goal of making
more intelligent machines. Multiprocessors add fuel to this promise
with the substantially greater power they possess. Machines that
could learn from practice, even at a modest scale, could relieve much
of the burden of programming all the necessary intelligence at the
outset and could help tailor generic programs to specific applications.
3. Connectionem. The last decade has witnessed progress in
the development of systems and theories involving the connections
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paradigm, which is often likened to the human nervous system. The
result of an interdisciplinary effort by neuroscientists, psychologists,
and computer scientists, connectionist work grows from the shared
conviction that the computational architecture of human cognition
is fashioned within the highly parallel dynamic architecture of the
human brain. Connections systems involve interconnected networks
of large numbers of elemental computing nodes that often simply add
up the values of their inputs and check if the sum Is above a preset
threshold. These massively parallel systems, somet~rnes referred to
as neural networks, operate by learning strategies that involve the
modification of the elemental nodes (e.g., the thresholds) in response
to what they experience.
Recent advances in this area are related to new knowledge about
what can be learned by such networks and improvements in VEST
circuits that make possible new complex architectures of many such
interconnected cells. Progress has been constrained by the learning
limitations of small experunental systems and by the absence of a
theory sufficiently developed to address how large neural networks
can become capable of substantial, predictable, and scalable learning.
Making systems more intelligent is a primary goal of AT research,
and advances toward this objective will make computers both more
useful and easier to use. The power and flexibility of today's machines
are greatly inhibited by the amount of detailed knowledge that must
be memorized by those who wish to use them effectively. Given
advances in equipment and a deeper theoretical understanding of
human cognitive processes, tomorrow's computers should have a
greatly enhanced capacity to understand what unsophisticated users
want them to do. More successful cognitive computer systems will
enhance the usefulness and ease of use of computer systems in all
areas of application.
ROBOTICS
Robotics researchers strive to understand and build machines
that are sufficiently intelligent to interact effectively with the physi-
cal world in the performance of designated tasks. Progress in robotics
will continue to be a key to enhanced productivity ~ factories, with
particular utility in the performance of repetitive and dangerous jobs
or jobs that require sustained quality control. Moreover, extensive
use of robotics and other computer technologies in design and man-
ufacturing Is expected to make possible the rapid prototyping of
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products (or even factories), permitting the cost-effective manufac-
ture of customized products at mass production costs. Autonomous
systems with mobile and perceptual capabilities will also make possi-
ble the performance of otherwme-unpossible tasks, such as planetary
exploration over a long period of time. Listed below are some of the
major promising research thrusts in robotics.
1. Sensors and perception. Sensors are the mechanisms that
provide information about the robot's relation to the environment.
Perception enables a machine to comprehend and adjust to its physi-
cal surroundings. Modes of sensing and perception include the visual
(see Chapter 7), tactile, force, torque, speed, and even olfactory
modes. Improvements in sensing and perception have the potential
to augment the usefuInem of virtually every type of machine by in-
creasing itd ability to adapt to the complexity and variability that
characterize the physical world. An important research activity in
this area is combining sensory transducers with computation for mak-
ing smarter sensors. Machine vision represents the most important
perceptual capability of future robotic systems.
2. Mechanisms. Progress in robotics depends on the design and
manufacture of mechanisms capable of the subtle, strong, and precise
motions required for useful activity. The need for precision, speed,
light weight, and strength poses serious problems that cannot easily
be met with conventional approaches. To date' most of the best
work in this area has come from the intensive efforts of design teams
relying on traditional engineering methods. We expect that extensive
computer-assisted design will play an expanded role in this area
through modeling and sunulation of complete robotic mechanisms
before construction.
3. Sensorimotor integration. To achieve smooth, flexible, em
ficient motions in robots, the sensor and motor controb must be
integrated and coordinated. Advances here call for research on vi-
sual, force, tactile, and torque feedback. A deeper understanding
of this integrative process among robot sensors and actuators wild
broaden understanding of neuroscience and biomechanics as well.
4. Planning. The effectiveness of robotics depends heavily on
a machine's ability to define necessary actions and specify their se-
quence in order to achieve a desired goal. Planning ranges from
high-level task planning (e.g., to assemble a product) to low-level
path planning (e.g., for obstacle avoidance). A difficult and impor-
tant problem in planning involves the conversion of semantic or mid
sion descriptions of a robot's goad to physical or machine-executable
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functions. Planning must also account for the inherent uncertainty
and partial knowledge that robots have of their physical environ-
ment. Some researchers believe that the best hope for progress in
planning rests with the creation of more intelligent program tenth a
deeper knowledge of the physical world.
Representative terms from entire chapter:
knowledge delivery
