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Intelligent Systems
1. Explain the potential value and the potential
limitations of artificial intelligence.
2. Provide examples of the benefits,
applications, and limitations of expert
systems.
3. Provide examples of the use of neural
networks.
4. Provide examples of the use of fuzzy logic.
5. Describe the situations in which genetic
algorithms would be most useful.
6. Describe the use case for several major types
of intelligent agents.
1. Introduction to Intelligent Systems
2. Expert Systems
3. Neural Networks
4. Fuzzy Logic
5. Genetic Algorithms
6. Intelligent Agents
TG Introduction to Intelligent
Agents
4.1 • Artificial Intelligence (AI)
– Behavior by a machine that, if performed
by a human being, would be considered
intelligent.
• Turing Test
• Artificial Intelligence versus
Natural (Human) Intelligence
TG Expert Systems
4.2 • Four Activities Involved in the
Transfer of Expertise
Expert  Computer  User
• The Components of Expert
Systems
• Applications, Benefits, and
Limitations of Expert Systems
Four Activities Involved in the Transfer
of Expertise
Expert  Computer  User
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4.
Knowledge Acquisition
Knowledge Representation
Knowledge Inferencing
Knowledge Transfer
The Components of Expert
Systems
• Knowledge Base
– Facts
– Rules
• Inference Engine
Applications, Benefits, and
Limitations of Expert Systems
• Expert Systems Are Especially Useful in
the Following Categories
• Benefits of Expert Systems
• Difficulties of Using Expert Systems
Applications, Benefits, and
Limitations of Expert Systems
• Expert Systems Are Especially Useful in the Following
Categories
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Interpretation
Prediction
Diagnosis
Design
Planning
Monitoring
Debugging
Repair
Instruction
Control
Applications, Benefits, and
Limitations of Expert Systems
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Benefits of Expert Systems
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Increased Output and Productivity
Increased Quality
Capture and Dissemination of Scarce Resources
Accessibility to Knowledge and Help Desks
Reliability
Ability to Work with Incomplete or Uncertain Information
Provision of Training
Enhancement of Decision-making and Problem-solving Capabilities
Decreased Decision-Making Time
Reduced Downtime
Difficulties of Using Expert Systems
– Transferring domain expertise from human experts to the expert system can be difficult
because people cannot always explain what they know
– Even if the doman experts can explain their entire reasoning process, automating that process
may not be possible
– In some contexts, there is a potential liability from the use of expert systems.
Applications, Benefits, and
Limitations of Expert Systems
• Benefits of Expert Systems
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Increased Output and Productivity
Increased Quality
Capture and Dissemination of Scarce Resources
Accessibility to Knowledge and Help Desks
Reliability
Ability to Work with Incomplete or Uncertain Information
Provision of Training
Enhancement of Decision-making and Problem-solving
Capabilities
– Decreased Decision-Making Time
– Reduced Downtime
Applications, Benefits, and
Limitations of Expert Systems
• Difficulties of Using Expert Systems
– Transferring domain expertise from human experts to
the expert system can be difficult because people
cannot always explain what they know
– Even if the domain experts can explain their entire
reasoning process, automating that process may not be
possible
– In some contexts, there is a potential liability from the
use of expert systems.
TG Neural Networks
4.3 • A system of programs and data
structures that simulates the
underlying functions of the
biological brain
– Examples of the Use of Neural Networks
• Bruce Nuclear Facility (Ontario, Canada)
• Research into Diseases (Alzheimer’s,
Parkinson’s, Epilepsy, etc.)
• Banking System Fraud Detection
TG Fuzzy Logic
4.4 • A branch of mathematics that
deals with uncertainties by
simulating the processes of
human reasoning.
TG Genetic Algorithms
4.5 • Mimics the evolutionary,
“survival-of-the-fittest” process to
generate increasingly better
solutions to a problem.
• Three Functional Characteristics
of Genetic Algorithms
• Examples of Genetic Algorithms
in the Field
Three Functional Characteristics
of Genetic Algorithms
• Selection
• Crossover
• Mutation
Examples of Genetic
Algorithms in the Field
• Boeing’s Aircraft – Part Design
• Marks and Spencer – Managing
Inventories
• Air Liquide – Optimal Production
Schedules and Distribution Points in
Supply Chain
TG Intelligent Agents
4.6 • A software program that assists, you,
or acts on your behalf, in
performing repetitive computerrelated tasks.
• Information Agents
• Monitoring-and-Surveillance
Agents
• User Agents (or Personal Agents)