Transcript NATURAL VS. ARTIFICIAL INTELLIGENCE

TECHNOLOGY
GUIDE 4:
Intelligent Systems
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
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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
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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:
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–
–
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.
2.
3.
4.
Knowledge acquisition
Knowledge representation
Knowledge inferencing
Knowledge transfer
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THE COMPONENTS OF EXPERT
SYSTEMS
•
•
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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
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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
•
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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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Copyright
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