Download Some major differences between expert systems and other systems:

MSE614 – Intelligent Manufacturing
Spring 2008 – Ileana Costea, Ph.D.
Session #1 - Lecture
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Introduction to course
Division in groups (4-5 students per group)
Introduction to Artificial Intelligence (AI)
o Lecture
(no transparencies, lecture written on the b oard; some material from the
lecture is given below; the rest was taken by students as notes in class)
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Class Exercises
Introduction to Artificial Intelligence (AI)
(Read and study Preface and Chapter 1 in textbook Luger)
 What is AI, what are Expert Systems (ES), examples of ES and AI applications
 What is intelligence (natural vs. artificial)
Natural intelligence does have several advantages over AI:
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Natural intelligence is creative, whereas AI is rather uninspired. The ability to acquire
knowledge is inherent in human beings, but with AI, tailored knowledge must be built
into a carefully constructed system.
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Natural intelligence enables people to benefit from and use sensory experience directly,
whereas most AI systems must work with symbolic inp ut.
Perhaps most important, human reasoning is able to make use at all times of a wide context of
experience and bring that to bear on individual problems; in contrast, AI systems typically gain
their power by having a very narrow focus.
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The advantages of natural intelligence over AI result in the many limitations of expert systems, and
will be discussed later in the course.
Computers can be used to collect information about objects, events, or processes; and, of course,
computers can process large amounts of information more efficiently than people can. People, however,
instinctively do some things that have been very difficult to program into a computer: they
recognize relationships between things; they sense qualities; and they spot patterns that
explain how various items relate to each other.
Newspaper photographs are nothing more than collections of minute dots, yet without any conscious
effort, people discover the patterns that reveal faces and other objects in those photos. Similarly, one of
the ways that humans make sense of the world is by recognizing the relationships and patterns
that help give meaning to the objects and events that they encounter.
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Some major differences between expert systems and other systems:
AI
mainly symbolic processing
non-algorithmic emphasis
heuristics are a key element
emphasis is on knowledge
Conventional Processing
numeric processing
algorithmic
heuristics are seldom used
emphasis is on models and data
AI can be documented. Decisions made by a computer can b e easily documented by tracing the
activities of the system. Natural intelligence is P( difficult to reproduce; for example , a person
may reach a conclusion but at some later date may be unable to re-create the reasoning process that led to
that conclusion or to even recall the assumptions that were a part of the decision.
If computers are to become more intelligent, they must be able to make the same kinds of associations
among the qualities of objects, events, and processes that come so naturally to people.
K n ow le d ge in A I - Definitions
In the field of information systems it is c ustomary to distinguish between
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Data T he t er m d a t a r e f e r s to n u mer ic (o r alp h a n u me ric) s tri n g s t ha t b y
themselves do not have a mean ing. They can be facts or figures to be pro cessed.
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Information Information is data organized so that it is meaningful to the person receiving it.
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Knowledge (K) has several definitions. According to the Webster’s New World Dictionary of the
American language, knowledge is:
 A clear and certain perception of something
 Understanding
 Learning
 All that has been perceived or grasped by the mind
 Practical experience, skill
 Acquaintance or familiarity
 Cognizance; recognition
 Organized information applicable to problem solving
Knowledge is also information that has been organized and analyzed to make it understandable
and applicable to problem solving or decision making. The collection of knowledge related to a
problem (or an opportunity) to be used in an AI system is called a knowledge base (KB). Most
knowledge bases are limited in that they typically focus on some specific subject or domain.
Once a knowledge base is built, AI techniques are used to give the computer inference capability.
The computer will then be able to make inferences and judgments based on facts and relationships
contained in the KB.
Key Terms
Algorithm
Artificial intelligence
Common Sense
Conventional Programming
Data
Expert system
Inferencing
Information
Information overload
Intelligent-computer-aided
instruction
Intuition
Knowledge (K)
Knowledge Base (KB)
Machine learning
Natural language
Natural language processing
Numeric processing
Pattern matching
Programming language
Robotics
Search
Sensory system
Speech recognition
Speech understanding
State Space Representation
Symbol
Symbol structure
Symbolic processing
Turing Test
Visual recognition
Vision Systems