CHAPTER 12 ADVANCED INTELLIGENT SYSTEMS © 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 12-2  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-3  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-4  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-5  Case base used for decision-making  Effective when rule-based reasoning is not  Case  Primary knowledge element Ossified Paradigmatic Stories

Case Reasoning © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-6

Case vs. Rule Reasoning © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-7

Process Case-Based Reasoning © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-8  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-9

Case Reasoning Success Factors © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-11

Neural Networks © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang  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 © 2005 Prentice Hall, Decision Support Systems and Intelligent Systems, 7th Edition, Turban, Aronson, and Liang 12-19

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