Transcript Advanced Intelligent Systems

Turban, Aronson, and Liang
Decision Support Systems and Intelligent Systems,
Seventh Edition
Chapter 12
Advanced Intelligent Systems
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-1
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.
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-2
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-3
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-4
Methods
• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-5
Case Reasoning
• Inductive
• Case base used for decision-making
• Effective when rule-based reasoning
is not
• Case
– Primary knowledge element
• Ossified
• Paradigmatic
• Stories
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-6
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-7
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-8
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-9
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-10
Human Brain
• 50 to 150 billion neurons in brain
• Neurons grouped into networks
– Axons send outputs to cells
– Received by dendrites, across synapses
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-11
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-12
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-13
Architecture
• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-14
Network Learning
• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-15
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-16
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-17
Genetic Algorithms
• 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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-18
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-19
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
© 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition,
Turban, Aronson, and Liang
12-20