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Overview of AI: Expert Systems, Neural Networks, Genetic Algorithms, & Intelligent Agents , Study notes of Introduction to Business Management

Mississippi University for Women (MUW)Introduction to Business Management
Prof. Christopher S. Tollison

An introduction to artificial intelligence (ai), focusing on various ai systems including expert systems, neural networks, genetic algorithms, and intelligent agents. Expert systems apply reasoning capabilities to reach conclusions based on domain expertise, while neural networks simulate human ability to classify things and find patterns. Genetic algorithms mimic the evolutionary process to generate better solutions, and intelligent agents assist in performing repetitive tasks. The document also discusses the challenges and advantages of each system.

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Uploaded on 08/07/2009

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Artificial Intelligence
February 19 – February 23
Introduction to Artificial Intelligence (AI):
oArtificial intelligence (AI) is the science of making machines imitate human
thinking and behavior (p. 189).
oA 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).
oCategories of AI:
Expert systems
Neural networks
Genetic algorithms
Intelligent agents
Expert Systems:
oAn expert system (a.k.a. knowledge-based system) is an AI system that applies
reasoning capabilities to reach a conclusion (p. 190).
oExpert 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).
oExpert 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.
oMost 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).
oWhen 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).
pf3
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Artificial Intelligence February 19 – February 23  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):  Intelligence phase – what’s wrong; identify problem or opportunity  Choice phase – what to do; decide what to do. o 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).

 A data-mining agent operates in a data warehouse discovering information. A data-mining agents detects trends in data (p. 202).