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This guide provides an in-depth introduction to intelligent systems, focusing on key concepts such as expert systems, neural networks, fuzzy logic, genetic algorithms, and intelligent agents. Readers will learn to differentiate artificial intelligence from human intelligence and gain insights into the functioning and applications of various intelligent systems. Each section offers definitions and practical examples to enhance understanding, making it suitable for learners and practitioners interested in the field of artificial intelligence and its subdomains.
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TECHNOLOGY GUIDE FOUR Intelligent Systems
TECHNOLOGY GUIDE OUTLINE TG4.1 Introduction to Intelligent Systems TG4.2 Expert Systems TG4.3 Neural Networks TG4.4 Fuzzy Logic TG4.5 Genetic Algorithms TG4.6 Intelligent Agents
LEARNING OBJECTIVES 1. Differentiate between artificial intelligence and human intelligence. 2. Define expert systems, and provide examples of their use. 3. Define neural networks, and provide examples of their use. 4. Define fuzzy logic, and provide examples of its use.
LEARNING OBJECTIVES (continued) 5. Define genetic algorithms, and provide examples of their use. 6. Define intelligent agents, and provide examples of their use.
TG4.1 Introduction to Intelligent Systems Intelligent systems Artificial intelligence (AI)
TG 4.2 Expert Systems Expertise Expert systems (ESs)
Expertise Transfer from Human to Computer • Knowledge acquisition • Knowledge representation • Knowledge inferencing • Knowledge transfer
The Components of Expert Systems • Knowledge base • Inference engine • User interface • Blackboard • Explanation subsystem
TG 4.5 Genetic Algorithms Genetic algorithms have three functional characteristics: • • Selection • • Crossover: • • Mutation:
TG 4.6 Intelligent Agents • Information Agents • Monitoring-and-Surveillance Agents • User Agents