Artificial Intelligence (AI)

Systems that exhibit the characteristics associated with intelligence in human behavior like:

Understanding language

Learning

Reasoning

Solving problems

Most Common Application of AI Concepts:

Expert Systems (ES)

These are software programs that use facts, knowledge, and reasoning techniques to solve problems that typically require human abilities.

Steps in the Creation of an Expert System:

1. Knowledge acquisition

2. Knowledge representation

3. Computational modeling

4. Model validation

Characteristics of an Expert System

1. Relies on a rich base of specific knowledge

2. Use thought processes in solving decisions

3. Explain the reasoning process and allow for uncertainty

4. Learns

Components of an Expert System

1. Knowledge database

2. Inference engine

3. User interface

Benefits of an Expert System

1. Consistency in developing solutions

2. Efficient

3. Valuable

4. Under distribution of knowledge of many experts

5. Knowledge is retained even when employed experts leave

6. Work 24 hours a day

Risk and Limitations of Expert Systems

1. There may be legal issues when reliance is placed on Expert Systems in decision-making

2. They are valuable only if they can perform as well or better than a human expert

3. They are expensive

4. They cannot think and there is a need for consensus among experts.

Neural Networks (NN)

These are software programs that solve problems by learning from experience, and using this learning for prediction purposes.

Components of a Neural Network

1. Nodes receives and passes data

2. Connections lines of communications

Characteristics of Neural Networks

1. Much historical data are available for system training

2. Data set may be incomplete, yet describes specific examples

3. Output is dependent on multiple, interacting parameters

4. The function that would determine the output is unknown

Case-Based Reasoning Systems

These are software programs that reason by analogy and for problems that require human to search through historical data to find similar problems with successful solutions.

Fuzzy Logic Systems

It deals with imprecise data and problems that have many solutions.

Genetic Algorithms

These are problem-solving methods that use evolutionary processes

Intelligent Agents

These are programs that apply a built-in or learned knowledge base to execute a specific, repetitive, and predictable task