TECHNOLOGY GUIDE 4: Intelligent Systems. TG 4.1 Introduction to Intelligent Systems TG 4.2 Expert Systems TG 4.3 Neural Networks TG 4.4 Fuzzy Logic TG.

TECHNOLOGY GUIDE 4:

Intelligent Systems

TG 4.1 Introduction to Intelligent Systems

TG 4.2 Expert Systems

TG 4.3 Neural Networks

TG 4.4 Fuzzy Logic

TG 4.5 Genetic Algorithms

TG 4.6 Intelligent Agents

TECHNOLOGY GUIDE 4:

INTELLIGENT SYSTEMS

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TG 4.1 Introduction to Intelligent Systems

TG 4.2 Expert Systems

TG 4.3 Neural Networks

TG 4.4 Fuzzy Logic

TG 4.5 Genetic Algorithms

TG 4.6 Intelligent Agents

INTELLIGENT SYSTEMS

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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.

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LEARNING OBJECTIVES (CONTINUED)

4. Define “fuzzy logic,” and provide examples of its use.

5. Define “genetic algorithms,” and provide examples of their use.

6. Define “intelligent agents,” and provide examples of their use.

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TG 4.1 INTRODUCTION TO INTELLIGENT SYSTEMS

• Intelligent systems: information systems that can make decisions by themselves.– Examples: Web apps and medical uses

• Major categories of intelligent systems:– expert systems– neural networks– fuzzy logic– genetic algorithms– intelligent agents

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NATURAL VS. ARTIFICIAL INTELLIGENCE

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TG 4.2 EXPERT SYSTEMS

• Click here to access the Website of IBM Watson Supercomputer

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EXPERTISE TRANSFER FROM HUMAN TO COMPUTER

1. Knowledge acquisition

2. Knowledge representation

3. Knowledge inferencing

4. Knowledge transfer

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THE COMPONENTS OF EXPERT SYSTEMS

• Knowledge base• Inference engine• User interface• Blackboard (workplace)• Explanation subsystem (justifier)

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FIGURE TG 4.1 STRUCTURE AND PROCESS OF AN EXPERT SYSTEM

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TABLE TG 4.2 TEN GENERIC CATEGORIES OF EXPERT SYSTEMS

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TABLE TG 4.3 BENEFITS OF EXPERT SYSTEMS

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DIFFICULTIES OF USING ES

• Difficulty transferring domain expertise from human experts to the expert system

• Challenge to automate certain processes• Potential liability

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TG 4.3 NEURAL NETWORKS

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TG 4.4 FUZZY LOGIC

• Fuzzy logic is a branch of mathematics that deals with uncertainties by simulating the processes of human reasoning.

• Examples:– Financial analysis (loan application)– Accounting (goodwill)– Internet searches (search queries)

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TG 4.5 GENETIC ALGORITHMS

• Genetic algorithms have three functional characteristics:– Selection (survival of the fittest): Giving preference to better and

better outcomes.– Crossover: Combining portions of good outcomes in the hope of

creating an even better outcome.– Mutation: Randomly trying combinations and evaluating the

success (or failure) of an outcome.

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TG 4.6 INTELLIGENT AGENTS

• Three types of Intelligent Agents (also called bots):– Information Agents– Monitoring-and-Surveillance Agents– User Agents

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INTELLIGENT AGENTS CONTINUED

• Information agents search for information and display it to users.

• Monitoring-and-surveillance agents, also called predictive agents, constantly observe and report on some item of interest.

• User agents, also called personal agents, take action on your behalf.

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TECHNOLOGY GUIDE CLOSING

1. There are a number of characteristics that differentiate artificial and human intelligence.

2. Expert systems are computer systems that attempt to mimic human experts by applying expertise in a specific domain.

3. A neural network is a system of programs and data structures that simulate the underlying concepts of the human brain.

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TECHNOLOGY GUIDE CLOSING (CONTINUED)

4. Fuzzy logic is a branch of mathematics that deals with uncertainties by simulating the processes of human reasoning.

5. A genetic algorithm is an intelligent system that mimics the evolutionary, “survival-of-the-fittest” process to generate increasingly better solutions to a problem.

6. An intelligent agent is a software program that assists you, or acts on your behalf, in performing repetitive, computer-related tasks.

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