INTELLIGENT SYSTEMSArtificial 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• Car or house scale model

• Analog models• Organizational charts, blueprints

• Mathematical (quantitative) models• Linear programs, statistical models

• Mental models – most abstract• 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• For example, a bird classification expert system

• Natural language processing (NLP)• NLP involves the largest knowledge base and most complex inference

processes

• How could intelligent systems be used in:• Accounting?• Marketing?• Manufacturing?• Computer network management?