1. Chapter 12 Advanced Intelligent Systems
Turban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition
Chapter 12 Advanced Intelligent Systems
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

2. Understand second-generation intelligent systems.
Learning Objectives
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.
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3. Household Financial’s Vision Speeds Loan Approvals With Neural Networks Vignette
Loan product regulation varies in each state
Develop an object-oriented loan approval system
Neural network-based
Fed risk, interest rate variables, customer data
Estimates credit worthiness, potential for fraud
Pattern recognition
Integrates all loan approval phases
Uses intelligent underwriting engine
Reduced training time and administrative overhead
Decreased managed basis efficiency ratio
Upgradeable to web-based architecture
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4. Machine Learning Acquisition of knowledge through historical examples
Implicitly induces expert knowledge from history
Different from the way that humans learn
Implications of system success and failure unclear
Manipulates of symbols instead of numbers
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5. Methods Supervised learning Unsupervised 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
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6. Case Reasoning Inductive Case base used for decision-making
Effective when rule-based reasoning is not
Case
Primary knowledge element
Ossified
Paradigmatic
Stories
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8. Process 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
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10. Case Reasoning Success Factors
Specific business objectives
Knowledge should directly support end users
Appropriate design
Updatable
Measurable metrics
Acceptable ROI
User accessible
Expandable across enterprise
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11. Human Brain 50 to 150 billion neurons in brain
Neurons grouped into networks
Axons send outputs to cells
Received by dendrites, across synapses
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

12. Neural Networks 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

13. Processing 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
© Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang

14. Architecture Feedforward-backpropogation Associative memory system
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
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15. Network Learning Learning algorithms Supervised Unsupervised
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
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16. Development of Systems
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
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17. Genetic Algorithms 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
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18. Genetic Algorithms Establish problem Generate initial set of solutions
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
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20. Fuzzy Logic 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
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