chapter 12 advanced intelligent systems © 2005 prentice hall, decision support systems and...

CHAPTER 12CHAPTER 12ADVANCED INTELLIGENT ADVANCED INTELLIGENT SYSTEMSSYSTEMS

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-1

Learning Objectives

© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

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Understand second-generation intelligent systems.

Learn the basic concepts and applications of case-based systems.

Understand the uses of artificial neural networks.

Examine the advantages and disadvantages of artificial neural networks.

Learn about genetic algorithms. Examine the theories and applications of

fuzzy knowledge.

Machine Learning


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Acquisition of knowledge through historical examples

Different from the way that humans learn Implicitly induces expert knowledge from

history Implications of system success and failure

unclear Manipulates of symbols instead of

numbers

Methods


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Supervised learning Induce knowledge from known outcomes

New cases used to modify existing theories Statistical methods Rule induction Case based and inference Neural computing Genetic algorithms leading to survival of fittest

Unsupervised learning Determine knowledge from data with unknown

outcomes Clustering data into similar groups Neural computing Genetic algorithms leading to survival of fittest

Case Reasoning


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Case base used for decision-making Effective when rule-based reasoning is

not Case

Primary knowledge element Ossified Paradigmatic Stories

Case Reasoning


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Case vs. Rule Reasoning


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Process Case-Based Reasoning


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Features assigned as character indexes Indexing rules identify input features

Indexes used to retrieve similar cases from memory Episodic case memories Similarity metrics applied

Old solution adjusted to fit new case Modification rules

Solution tested If successful, assigned value and stored If failure, explain, repair, test

Alter plan to fit situation Rules for permissible alterations

Process Case-Based Reasoning


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Case Reasoning Success Factors


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Specific business objectives Knowledge should directly support end

users Appropriate design Updatable Measurable metrics User accessible Expandable across enterprise

Human Brain

50 to 150 billion neurons in brain Neurons grouped into networks

Axons send outputs to cells Received by dendrites, across synapses


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Neural Networks


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Attempts to mimic brain functions Analogy, not accurate model Artificial neurons connected in network

Organized by topologies Structure

Three or more layers Input, intermediate (one or more hidden layers),

output

Receives modifiable signals

Processing


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Processing elements are neurons Allows for parallel processing Each input is single attribute

Connection weight Adjustable mathematical value of input

Summation function Weighted sum of input elements Internal stimulation

Transfer function Relation between internal activation and output

Sigmoid/transfer function Threshold value

Outputs are problem solution

Architecture


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Feedforward-backpropogation Neurons link output in one layer to input in

next No feedback

Associative memory system Correlates input data with stored information May have incomplete inputs Detects similarities

Recurrent structure Activities go through network multiple times to

produce output

Network Learning


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Learning algorithms Supervised

Connection weights derived from known cases Pattern recognition combined with weighting changes Back error propagation

Easy implementation Multiple hidden layers Adjust learning rate and momentum Known patterns compared to output and allows for weight

adjustment Established error tolerance

Unsupervised Only stimuli shown to network Humans assign meanings and determine usefulness

Adaptive resonance theory Kohonen self-organizing feature maps

Development of Systems


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Collect data The more, the better

Separate data into training set to adjust weights Divide into test sets for network validation Select network topology

Determine input, output, and hidden nodes, and hidden layers Select learning algorithm and connection weights Iterative training until network achieves preset error level Black box testing to verify inputs produce appropriate

outputs Contains routine and problematic cases

Implementation Integration with other systems User training Monitoring and feedback

Genetic Algorithms


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Computer programs that apply processes of evolution Viability of candidate solutions

Self-organized Adaptable Fitness function

Measured by objective obtained Iterative process

Candidate solutions combine to produce generations Reproduction, crossover, mutation

Genetic Algorithms


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Establish problem Parameters

Number of initial solutions, number of offspring, number of parents and offspring for each generation, mutation level, probability distribution of crossover point occurrence

Generate initial set of solutions Compute fitness functions Total all fitness functions Compare each solution’s fitness function to total Apply crossover Apply random mutation Repeat until good enough solution or no

improvement

Genetic Algorithms


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Fuzzy Logic


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Mathematical theory of fuzzy sets Imprecise thinking Describes human perception Continuous logic Not 100% true or false, black or white Fuzzy neural networks

Fuzzification Fuzzy logic applied to input and output used to create

model Defuzzification

Model converted back to original input, output scales Output becomes input for another intelligent system

chapter 12 advanced intelligent systems © 2005 prentice hall, decision support systems and...

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