Transcript TechGuide 4
4 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 1. 2. 3. 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 – – – – – – – – – – Interpretation Prediction Diagnosis Design Planning Monitoring Debugging Repair Instruction Control Applications, Benefits, and Limitations of Expert Systems • Benefits of Expert Systems – – – – – – – – – – • 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 – – – – – – – – 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)