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Transcript
INTELLIGENT SYSTEMS
ARTIFICIAL INTELLIGENCE APPLICATIONS IN BUSINESS
LEARNING OBJECTIVES
 Describe decision models and the benefits of computer supported decision
making and experimentation
 Describe artificial intelligence (AI)
 Compare capabilities for natural (human) intelligence versus artificial intelligence
 Define an expert system and identify its components
 Discuss intelligent system examples that illustrate various forms of problem
representation and reasoning
 Identify intelligent systems applications in business functional areas
DECISION MODELS

Data + models = decisions

Models are representations of problems that vary by degree of abstraction

Examples include:

Iconic (scale) models – least abstract


Analog models


Linear programs, statistical models
Mental models – most abstract


Organizational charts, blueprints
Mathematical (quantitative) models


Car or house scale model
Consumer behavior models
Other examples include visualization methods, geographic information systems, and virtual reality
BENEFITS OF COMPUTER SUPPORTED DECISION
SYSTEMS
 Cost of virtual experimentation is lower
 Compresses time
 Manipulations are easier
 Cost of mistakes is lower
 Can evaluate risk and uncertainty
 Can compare a large number of alternatives
 Can be used for training
INTELLIGENT SYSTEMS
 Intelligent systems is a term that best describes the various commercial
applications of artificial intelligence
 Artificial intelligence (AI) is a subfield of computer science that is concerned
with studying the thought processes of humans and re-creating the effects of
those processes via machines, such as computers and robots
 AI’s ultimate goal is to build machines that will mimic human intelligence
 An interesting test to determine whether a computer exhibits intelligent
behavior was designed by Alan Turing (the Turing test)
COMPARISON OF THE CAPABILITIES OF
NATURAL VERSUS ARTIFICIAL INTELLIGENCE
Capabilities
Natural Intelligence
Artificial Intelligence
Preservation of knowledge
Perishable
Permanent
Duplication and sharing of
knowledge
Difficult, expensive, takes time
Easy, fast, and cheap when in
the right format
Total cost of knowledge
Can be erratic and inconsistent
Consistent and thorough
Documentability of knowledge
Difficult, expensive
Fairly easy, inexpensive
Creativity
Can be very high
Low, uninspired
Use of sensory experiences
Direct and rich in possibilities
Limited
Recognizing patterns and
relationships
Fast, easy to explain
Getting better, but not as good
as humans
Reasoning
Makes use of a wide range of
experiences
Good only in narrow, focused,
stable domains
EXPERT SYSTEMS
 When an organization has a complex decision to make or problem to solve, it
often turns to experts for advice
 Expert systems (ESs) are computer systems that attempt to mimic human
experts by applying expertise in a specific domain
 The transfer of expertise from an expert to a computer and then to the user
involves four activities:

Knowledge acquisition

Knowledge representation

Knowledge inferencing

Knowledge transfer
EXPERT SYSTEM STRUCTURE
QUESTION?
 What makes a system “intelligent”?
ANSWER
 Intelligent systems include one, or more, of the following capabilities:


Reasoning

Deductive

Inductive

Analogical (extremely difficult to implement in AI systems)
Rationality


Efficient search for answers
Learning

Incorporate knowledge learned from past experience to improve decision making over time
INTELLIGENT SYSTEM EXAMPLES

Machine (concept) learning

Case-based reasoning

Decision trees

Other examples:

Rule-based expert systems


Natural language processing (NLP)


For example, a bird classification expert system
NLP involves the largest knowledge base and most complex inference processes
How could intelligent systems be used in:

Accounting?

Marketing?

Manufacturing?

Computer network management?