Artificial Intelligence
September 28 – October 4

Introduction to Artificial Intelligence (AI):
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Artificial intelligence (AI) is the science of making machines imitate human thinking and
behavior (p. 189).
o
A robot is a mechanical device equipped with simulated human senses and the capability
of taking action on its own (in contrast to a mechanical device that requires direction
from a person) (p. 189).
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
Categories of AI:
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Expert systems
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Neural networks

Genetic algorithms

Intelligent agents
Expert Systems:
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An expert system (a.k.a. knowledge-based system) is an AI system that applies reasoning
capabilities to reach a conclusion (p. 190).
o
Expert systems are based on the know-how of experts in the field. This expertise is built
into the system and thus does not require as much knowledge to use as a typical DSS (p.
191).
o
o
Expert systems are designed to work at two of the phases of decision making (p. 190):

Intelligence phase – what’s wrong; identify problem or opportunity

Choice phase – what to do; decide what to do.
Most expert systems are built on the concepts of questions and rules. The expert system
asks a question. If it is answered “yes”, another question appears. If it is answered “no”,
a different question appears. Based on the answer to this question, another question is
asked. This process of question and answer continues until a decision is reached (p. 192).
o
When developing an expert system, there are several terms to which you must be aware.

An expert system is usually built for a specific application called a domain (p.
190).

Domain expertise is the core of the expert system because it contains the steps to
reach a decision (p. 194).

A domain expert is the person who provides the domain expertise (p. 194).

A knowledge engineer is the IT person who coverts the domain expertise into an
expert system (p. 194).

Once the knowledge engineer has converted the domain expertise into rules, the
knowledge base is used to store the rules of the expert system (p. 194).

The inference engine is the part of the expert system that takes your answers and
decides what to ask next (p. 194).

The explanation module is the part of the expert system that provides the reason
why a conclusion was reached (p. 195).
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
Problems with Expert Systems:

Converting the domain expertise into a knowledge base may be too difficult.

The expertise may be too complex to be used in an expert system.

The expert system has no common sense.
Neural Networks:
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A neural network simulates the human ability to classify things without taking prescribed
steps leading to the solution. A neural network is an AI system that is capable of finding
and differentiating patterns (p. 196).
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Neural networks are most useful for identification, classification, and prediction when a
vast amount of information is available. By examining many, many examples, it
determines important relationships and patterns in the information (p. 196).
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In an expert system, you input hundreds, or thousands, of examples into a neural network.
The neural network examines this input in many different ways until it finds an “average”
solution (p. 198).
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The difference between an expert system and a neural network is that an expert system is
rigid and unchanging and a neural network can learn and change “on the fly” (p. 198).
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The big problem with neural networks is that so much of their processing takes place
behind the scenes, it is hard to relate how the solutions are found (p. 199).

Genetic Algorithms:
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A genetic algorithm is an artificial intelligence system that mimics the evolutionary,
survival-of-the-fittest process to generate increasingly better solutions to a problem (p.
199).
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In other words, a genetic algorithm finds the combinations of inputs that give you the best
outputs (p. 199).
o
Genetic algorithms are best suited to decision-making environments in which thousands,
or perhaps millions, of solutions are possible (p. 200).

Intelligent Agents:
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An intelligent agent is software that assists you, or acts on your behalf, in performing
repetitive computer-related tasks (p. 202).
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Types of intelligent agents:

A buyer agent (a.k.a. shopping bot) is an intelligent agent on a Web site that
helps you, the customer, find the products and services you want (p. 202).

Shopping bots make money by selling, advertising, conducting special
promotions in cooperation with merchants, or charging click-through
fees.

A user agent (a.k.a. personal agent) are intelligent agents that take action on
your behalf. Examples of tasks performed by user agents include (p. 203):


Check your e-mail

Assemble customized news reports for you
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Find information on a subject of your choice
Mining and surveillance agents (predictive agents) are intelligent agents that
observe and report on equipment. Examples include (p. 204):


Observing and reporting on equipment

Tracking computer networks

Watch competition and bring back price changes made by competitors
A data-mining agent operates in a data warehouse discovering information. A
data-mining agents detects trends in data (p. 205).