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Artificial Intelligence
October 2 – October 6

Introduction to Artificial Intelligence (AI):
o
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).
o

Categories of AI:

Expert systems

Neural networks

Genetic algorithms

Intelligent agents
Expert Systems:
o
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
Expert systems are designed to work at two of the phases of decision making (p.
190):
o

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 (pp. 192-193).
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.

A domain expert is the person who provides the domain expertise.

A knowledge engineer is the IT person who coverts the domain expertise
into an expert system.

Once the knowledge engineer has converted the domain expertise into
rules, the knowledge base is used to store the rules of the expert system.

The inference engine is the part of the expert system that takes your
answers and decides what to ask next.

The explanation module is the part of the expert system that provides the
reason why a conclusion was reached.
o
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:
o
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. 193).
o
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. 193-194).
o
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 (pp. 195-196).
o
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. 196).
o
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. 197).

Genetic Algorithms:
o
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. 198).
o
In other words, a genetic algorithm finds the combinations of inputs that give you
the best outputs (p. 198).
o
Genetic algorithms are best suited to decision-making environments in which
thousands, or perhaps millions, of solutions are possible (p. 198).

Intelligent Agents:
o
An intelligent agent is software that assists you, or acts on your behalf, in
performing repetitive computer-related tasks (p. 200).
o
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.
200).

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

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. 201):

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. 202).