Abdul Rahim Ahmad MITM 613 Intelligent System Chapter 9: Hybrid Systems.
Hybrid Intelligent Systems
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Transcript of Hybrid Intelligent Systems
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Hybrid Intelligent
Systems
Rajendra M Sonar, Ph.D.SJM School of Management,Indian Institute of Technology BombayMumbai-400 [email protected]
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Intelligent Systems
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How problems are solved?
Knowledge + Use and apply the knowledge
(Tools/Technique/..)
= Problem SolvingKnowledge?
- facts, ideas that
have been
acquired through
experience,
investigation and
observations
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How to make a machine intelligent?
Represent the
Knowledge
Write code to use
and apply the
knowledge
Get the
Knowledge
Intelligent
System=+
From past
examples/solved
problems etc.
Learn and Understand
Extract from
expert/s (or
documented
sources)
extract
How to get the knowledge?
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Represent the
Knowledge
Write code to use
and apply the
knowledge
Get the
Knowledge
Intelligent
System=+
From past
examples/solved
problems etc.
Retrieve, Learn and Understand
Extract from
expert/s
Extract
Knowledge
Presentation
Reasoning
Techniques
Knowledge
Elicitation
Retrieval
Structuring,
Storage,
Indexing…
Protocol Analysis
Questionnairs
Repertery Grid Analysis
Automated Tools…
Rule-based
Semantic Net
Predicate Logic
Similarity
Frames…
Backward
Forward
Case-based
…|
Expert System,
Neural Networks,
Fuzzy Systems,
Case-based Systems
…
Dealing with Incomplete Information and Uncertainty
Explanation
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Intelligent techniques differ in ways they
� Acquire and get the knowledge
� Represent and store the knowledge
� Use and apply the knowledge
� Deal with uncertainty and incomplete information
� Deal with and adapt to new problems
� Retain/Revise the knowledge
� Explain the problem solving
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Widely used intelligent techniques to develop
intelligent systems
� Rule-based Reasoning
� Neural Networks
� Case-based Reasoning
� Fuzzy Systems
� Genetic Algorithms
� Model-based Reasoning
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Comparing Intelligent Systems
(expert system, CBR and ANN)
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Foundation
Logic Logic RBR
Biological nervous system Biological nervous system ANN
Human Reasoning Human Reasoning CBR
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Knowledge Containers and Model complexity
Knowledge is explicit in the form of set of rules, rules are
easier to understand
Knowledge is explicit in the form of set of rules, rules are
easier to understand RBR
Knowledge is implicit (in the form neural network topology,
transfer function, weights etc). Models are like black-box, less
explanatory
Knowledge is implicit (in the form neural network topology,
transfer function, weights etc). Models are like black-box, less
explanatory
ANN
Implicit in the form of past examples and explicit in the form of
domain vocabulary, and domain knowledge. Domain
knowledge is: used for indexing, retrieval and adaptation.
Models are easier to understand, combination of experience
and domain knowledge
Implicit in the form of past examples and explicit in the form of
domain vocabulary, and domain knowledge. Domain
knowledge is: used for indexing, retrieval and adaptation.
Models are easier to understand, combination of experience
and domain knowledge
CBR
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Learning & Adaptability
Rules are static, do not learn, new rules needs to be explicitly
added and existing to be updated.
Rules are static, do not learn, new rules needs to be explicitly
added and existing to be updated. RBR
Learn from the examples, trial-and-error, completely
automated, no domain knowledge, needs to re-trained for new
examples. Adapts to new examples easily.
Learn from the examples, trial-and-error, completely
automated, no domain knowledge, needs to re-trained for new
examples. Adapts to new examples easily.
ANN
Do not learn but find out most matching cases, reuse and
adapt the similar ones based on similarity knowledge, lazy
learning (defer the learning as late as possible and take care
of latest experience). New cases can be easily added,
Knowledge can be updated.
Do not learn but find out most matching cases, reuse and
adapt the similar ones based on similarity knowledge, lazy
learning (defer the learning as late as possible and take care
of latest experience). New cases can be easily added,
Knowledge can be updated.
CBR
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Reliability
Based on experts or knowledge sourcesBased on experts or knowledge sourcesRBR
Depends upon the data/examples. Depends upon the data/examples. ANN
Based on past examples/cases/data as well as domain
expertise.
Based on past examples/cases/data as well as domain
expertise. CBR
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Matching & Generalization
The rules must be completely satisfied (matched). The rules must be completely satisfied (matched). RBR
Approximate and has generalization capabilities. Approximate and has generalization capabilities. ANN
Approximate based on similarity measures and knowledge
used.
Approximate based on similarity measures and knowledge
used. CBR
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Development Efforts
Initial efforts are high, need to convert tacit knowledge into
explicit which is time consuming and costly. It is relatively
easier to build general knowledge than the specific one.
Initial efforts are high, need to convert tacit knowledge into
explicit which is time consuming and costly. It is relatively
easier to build general knowledge than the specific one.
RBR
Deals with only quantitative data, needs lots of preprocessing,
modelling is trial and error, needs understanding of various
algorithms, architectures, transfer functions etc. large and
representative examples are required
Deals with only quantitative data, needs lots of preprocessing,
modelling is trial and error, needs understanding of various
algorithms, architectures, transfer functions etc. large and
representative examples are required
ANN
Relatively easier when structured examples available, can
easily be integrated with database systems in structural CBR,
deals with qualitative as well quantitative data, flexible and not
much of formatting and preprocessing required
Relatively easier when structured examples available, can
easily be integrated with database systems in structural CBR,
deals with qualitative as well quantitative data, flexible and not
much of formatting and preprocessing required
CBR
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Maintenance
Modifying knowledge-base is difficult task especially when
knowledge-base is large or modifications that are incremental
Modifying knowledge-base is difficult task especially when
knowledge-base is large or modifications that are incremental RBR
Relatively easily to modify ANN and retrain them or remodel
again.
Relatively easily to modify ANN and retrain them or remodel
again.ANN
Easier as new cases can be added and existing cases can be
revised or removed etc. Domain knowledge is less and can be
managed. Case structure can be modified.
Easier as new cases can be added and existing cases can be
revised or removed etc. Domain knowledge is less and can be
managed. Case structure can be modified.
CBR
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Problem domains and capabilities
Work in well understood and narrow domain. Expertise needs
to be available. Good for diagnostics, monitoring, control, data
analysis, advisory applications
Work in well understood and narrow domain. Expertise needs
to be available. Good for diagnostics, monitoring, control, data
analysis, advisory applications
RBR
Can address complex problems right from classification to
clustering
Can address complex problems right from classification to
clusteringANN
Addresses wide range of problems from diagnostics,
collaborative filtering to knowledge-management.
Addresses wide range of problems from diagnostics,
collaborative filtering to knowledge-management. CBR
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� Problem solving approaches using Intelligent
Systems:
� The next slides contain illustrations are based on
very simple problem: input parameters are
customer age and income and output parameter
what product the person will buy (in case
classification problem solving which product
customer is likely to buy)
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ANN Approach: Classification
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3Decision
Boundary
Build association between input
parameters and output . Powerful
classifiers, decision boundaries are
much finer and can be complex
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ANN Approach: Clustering
In clustering problems, neurons move
into the centre of cluster. A new
example belongs to cluster based on
distance to central neuron.
This customer
will share the
characteristics
of this cluster
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CBR Approach: Classification
+ Case (Age, Income Group->Product)
More closely
Matching
Cases
P1
P2
P5
P4
P3
Decision
Boundary
Unlike neural network, which
gets trained (learns) and
builds association. Each time
CBR finds solution from
stored cases to find best
match: lazy learning
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CBR Approach: Clustering
CBR does not form the cluster. The
matching cases to a problem itself forms
a cluster (means shares same features
values as that of problem)
This customer shares
characteristics of
enclosed cases in this
cluster
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RBR: General Knowledge
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
This customer will
buy Product P5
Decision
Boundary
General rules to
find out which
product a
customer will buy
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RBR: More deeper knowledge
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
This customer will
buy product P4
Decision
Boundary
Rules are more specific
indicating what
customer with given
profile will buy what
product
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Features of Intelligent Systems
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Hybrid Intelligent Systems
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Hybrid Intelligent Systems
� The systems in which more than one intelligent system has been used are called as hybrid intelligent systems or intelligent hybrid systems.
� Why?
� The intelligent systems collectively have features like learningability, adaptation to changes, explanation capability and flexibility in dealing with imprecise and incomplete information, etc.
� No single intelligent system has all the features.
� The limitations and strengths of individual systems is the central driving force behind the hybrid intelligent systems.
� By integrating the systems their strengths can be increased and weaknesses can be reduced.
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Reasons for creating Hybrid Systems[Suran Goonatilake, sukhdev Khebbal, 1995]
� Technique enhancement: This is the integration of different techniques to overcome the limitations of each individual technique. Here the aim is to take a technique that has weaknessin a particular property and combine it with a technique that has strength in that same property.
� Multiplicity of application tasks: When no single technique is available to the many sub-problems of a given application then this hybrid system is used.
� Realizing multi-functionality: These hybrid systems can exhibit multiple information processing capabilities within one architecture.
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Types of Hybrids[Suran Goonatilake, sukhdev Khebbal, 1995]
� Function-Replacing hybrids – In this system, a principal function of the given technique is replaced by another intelligent processing technique. It is done for either to increase execution speed or enhance reliability. The motivation for this approach is the technique enhancement.
� Intercommunicating hybrids – These are independent, self-contained, intelligent processing modules that exchange information and perform separate functions to generate solutions. If a problem can be subdivided into distinct processing tasks, then different independent intelligent modules can be used to solve the parts of the problem, which they are best. These independent modules, which collectively solve the given task, are coordinated by a control mechanism. This approach is motivated by multiplicity of application tasks.
� Polymorphic hybrids – These systems use a single processing architecture to achieve the functionality of different intelligent processing techniques. The broad motivation for these hybrid systems is realizing multi-functionality within particular computational architectures. These systems can functionally mimicor emulate different processing techniques. This is appropriate in situations whrethe desired functionality dynamically changes, this required the ability to a switch from one style of processing to another.
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Hybrids architectures/models[Medsker and Liebowitz, 1994]
Embedded
Tight CouplingLoose Coupling
Transformational
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Hybrids architectures/models[Fu, L.M., 1994]
Parallel
Sequential
Embedded
Partially overlapped
ES
Completely overlapped
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Chain-processing
Environment
Meta-processing
Environment
Sub-processing
Environment
Environment
Co-processing
Hybrids architectures/models[Hilario, et al 1994]
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ES+ANN hybrids[Wermter and Sun, 2000]
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ES+ANN hybrids
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Database coupling: database as an integrating
and coordinating mechanism [Sonar, 1999, 2001]
Database
Intelligent
System 1
Intelligent
System 2
Intelligent
System n
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Using XML coupling [Sonar, 2004]
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An Integration Framework [Sonar, 2007]
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Example: Using Integrated Approach:
Expert System + CBR Approach
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Why integrated RBR and CBR?
� RBR has explicit knowledge: expertise is
automated
� CBR is more experience driven (by past
examples/cases/data: implied knowledge) .
Has explicit knowledge in domain vocabulary
and to match cases
� RBR+CBR is combination of knowledge +
experience (explicit +implied knowledge)
which can address large number of problems.
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Only CBR Approach
+ Case (Age, Income Group->Product)
More closely
Matching
Cases
P1
P2
P5
P4
P3
Decision
Boundary
Issues: retrieval in real-time. adaptation
for practical problems. Too many
features: problem formulation can
become complex
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Only RBR: General Knowledge
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
Age 33, Income Group 2,
will that person will buy P2
while his profile is more
closer to people who buy
P5?
Decision
Boundary
Only general knowledge
is available: solution is
approximate and may
not be correct
(or very generic in nature)
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Only RBR: More deeper knowledge
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
Age 43, Income
Group 3, what this
person will buy?
Decision
Boundary
Too many rules: in worst case one for
each product/case. May not get solution
for boundary/specific/exceptional cases
as rules are matched exactly
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RBR (Indexing) + CBR
+ Case (Age, Income Group->Product)
More closely
Matching
Cases
P1
P2
P5
P4
P3
Expert system helps in
retrieving cases quickly by
narrowing down search space.
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RBR (Path/Guide in Feature Selection) + CBR
+ Case (Age, Income Group->Product)
More closely
Matching
Cases
P1
P2
P5
P4
P3
Expert system guides
user/decision maker in
selecting only appropriate
input features that are
required in current problem
context.
S1
S2
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More closely
Matching
Cases
CBR+RBR (Rule-based Feature Matching)
Rules can be written to
match features values using
complex logic rather than
predefined matching
functions.
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RBR (Clear Cases) + CBR (Specific/Boundary Cases)
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
Age 43, Income
Group 3, this
person is likely to
buy P4.
Clear cases handled by
expert system while specific
or boundary cases by CBR.
+ Case (Age, Income Group->Product)
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Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
RBR is used to solve new problem. CBR
can be used to validate solution of RBR
by CBR matching cases.
RBR + CBR (Validation)
+ Case (Age, Income Group->Product)
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CBR+RBR (Adaptation)
Age 43, Income
Group 3, this
person is likely to
buy P3 or P4?
Adaptation
Rules
Based on complexity, the
problem is solved using
more than one matching
cases, based on adaptation
logic etc. Rule-based
systems can help in this.
Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
+ Case (Age, Income Group->Product)
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Prod P1
Prod P2
Prod P5
Prod P4
Prod P3
A problem can be
addressed by both RBR
and CBR, results can be
combined for effectiveness
RBR || CBR
Will buy Product
P4 (CBR)
Will buy
Product
P4 (RBR)
+ Case (Age, Income Group->Product)
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References� 1. Larry Medsker, Hybrid Intelligent Systems. Kluwer Academic Publishers, Boston,
1995.
� 2. Melanie Hilario,, Christian Pellegrini & Frederic Alexandre. Modular integration of connectionist and symbolic processing in knowledge-based systems. International Symposium on Integrating Knowledge and Neural Hueristics, pages 123-132,Pensacola, Florida, 1994.
� 3. Sonar, R.M, “An Enterprise Intelligent System Development and Solution Framework”, International Journal Of Applied Science, Engineering And TechnologyVolume 4 Number 1 2007 ISSN 1307-4318
� 4. Sonar, R.M., “A Web-based Hybrid Intelligent System Framework”, Intelligent Systems and Control, ACTA Press, 2004, pp. 254-259.
� 5. Sonar, R.M. and A. Saha, “An integration framework to develop modular hybrid intelligent systems”, Frontiers in Artificial Intelligence and Applications, 69, IOS Press, 2001, pp.1499-1506.
� 6. Sonar, R.M. “Integrating intelligent systems using an SQL-database”. Expert Systems with Applications, Vol.17 (1), July, 1999, 45-49.
� 7. Suran Goonatilake and Phillip Treleaven, editors. Intelligent Systems for Finance and Business, John Wiley and Sons, 1995.
� 8. Suran Goonatilake and Sukhdev Khebbal, editors. Intelligent Hybrid Systems, John Wiley and Sons, 1995.
� 9. Wermter Stefan and Ron Sun. Overview of Hybrid Neural Systems. In Hybrid Neural Systems,1:13, Springer, Heidelberg, New York, January 2000.