MCC 1513ADVANCED ARTIFICIAL INTELLIGENCE ADVANCED ARTIFICIAL INTELLIGENCE

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Aims:To capture an in depth understanding of Soft Computing/Computational IntelligenceTo investigate some common models and their applicationsT i l t th t h i l t d blTo implement these techniques on related problems

Learning Outcomes:Understand the learning and generalization issues in Soft ComputingUnderstand the basic ideas on common learning algorithms inUnderstand the basic ideas on common learning algorithms in◦ Artificial Neural Network

Multilayer Perceptrons, Backpropagation Network, SOM, RBF & RNN◦ Data Preparation & Analysis

N-Fold Cross Validation N Fold Cross Validation Applying ANN techniques to classification and recognition problems.

◦ Particle Swarm Optimization & Genetic Algoritm◦ Granular Computing

Fuzzy SystemRough Set Theory

◦ Advances in Soft Computing Techniques

Prerequisites:Diff ti l E ti - Differential Equation

- Linear Algebra- Probability and Statistics - Strong Familiarity with a computer programming

language(C,C++) language(C,C++)

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COURSE OUTLINECOURSE OUTLINE

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MOTIVATION

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ARTIFICIAL NEURAL NETWORKARTIFICIAL NEURAL NETWORK◦ Cerrebelum Computing (PDF)◦ Brain and Attention◦ Autonomic ComputingAutonomic Computing◦ Dendritic Computing (PDF)

ARTIFICIAL IMMUNE SYSTEM (PDF)C U S S ( )Artificial Lymphocyte Cell (ALC)

ARTIFICIAL MEMBRANE SYSTEM (PDF)

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BRAIN NERVOUS SYSTEMcreating the terminology of

ARTIFICIAL NEURAL NETWORK!!

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IMMUNE SYSTEMcreating the terminology of ARTIFICIAL IMMUNE SYSTEMARTIFICIAL IMMUNE SYSTEM

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MEMBRANE SYSTEMScreating the terminology ofcreating the terminology of ARTIFICIAL MEMBRANE SYSTEMS

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(AL-QURAN : At-Tin: VERSE 4)

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BOOK CHAPTER 2008BOOK CHAPTER 2008 SPRINGERSPRINGERBOOK CHAPTER 2008 BOOK CHAPTER 2008 -- SPRINGERSPRINGER

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What is AI ?

AI is a broad field, and means different things to different people. It is concerned with getting computers to do tasks that require human intelligence which require complex and

hi ti t d i g d k l dgsophisticated reasoning processes and knowledge.

If John McCarthy, the father of AI, were to coin a new phrase for “artificial intelligence” today, he would probably use for artificial intelligence today, he would probably use “computational intelligence.” (IEEE Intelligent Systems, 2002)

HOWEVERHOWEVER,

L.A. Zadeh claimed that computational intelligence is actually Soft Computing techniques (1994)Soft Computing techniques (1994)

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By Lee SpectorArtificial Intelligence 170 (2006) 1251–1253

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ThinkUnderstandRecognizeRecognizePerceiveGeneralizeAdaptLearnMake DecisionsMake DecisionsSolve Daily Problems

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Capability to learnGathering of informationRecognizing PatternsCapability to classifyCapability to classifyMaking decisionsReasoning capabilityg p yPredicting/ForecastingCapability to survive

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Humans usually employ natural languages in reasoning and drawing conclusions.

Conventional AI research focuses on an attempt to mimic human intelligent

behavior by expressing it in language forms or symbolic rules. Conventional AI

basically manipulates symbols on the assumption that such behavior can be stored

in symbolically structured knowledge bases Perhaps the most successful in symbolically structured knowledge bases. Perhaps the most successful

conventional AI product is the knowledge-based system or expert system (ES).

Calling Soft Computing constituents parts of modern AI inevitably depends on Calling Soft Computing constituents parts of modern AI inevitably depends on personal judgment. It is true that many books on modern AI describe neural networks and perhaps other soft computing components such as:

Jones M. T. 2005. AI Application Programming. 2nd Ed. Hingham, Massachusetts: Ch l Ri M di I Charles River Media Inc. S. Russell and P. Norvig. Artificial Intelligence : A modern approach. Prentice-Hall, Upper Saddle River, NJ, 1995.

Luger, G. F. 2002. Artificial Intelligence: Structures and Strategies for Complex Problem g , f g g f pSolving. 4th ed. Harlow, England: Addison-Wesley/Pearson Education Limited.

Patrick H. Winston. Artificial Intelligence. Addison-Wesley, Reading, MA, 3rd Edition, 1992.28

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What is Soft Computing (SC) ?What is Soft Computing (SC) ?Soft Computing can be considered as a field dedicated to problem solving methods capable of SIMULTANEUOUSLYexploiting numerical data and human knowledge, using

th ti l d li d b li i t mathematical modeling and symbolic reasoning systems. It is tolerant of imprecision, uncertainty, and partial truth.

The SOUL of SC is to make computers as SOFT as the The SOUL of SC is to make computers as SOFT as the human brain, and is capable of carrying out both quantitative and qualitative computing.

◦ The Quantative Information: takes the form of precise numerical data◦ The Qualitative Information: assumes qualitative

t t t f k l d d i t d b statements of knowledge and experience represented by natural languages.

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According to Zadeh (1994) :

“…in contrast to traditional, hard computing, soft computing l f d l h ”

g gis tolerant of imprecision, uncertainty, and partial truth.” It is a consortium of methodologies which work synergistically and provides in one form or another flexible information processing capabilities for handling real life ambiguous processing capabilities for handling real life ambiguous situations. Its aim to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve :

tractabilityRobustnessLow cost solutionsLow cost solutionsClose resemblance to human like decision making.

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Principal Components of Soft ComputingPrincipal Components of Soft Computing

◦ Neural NetworksCerrebelum ComputingCerrebelum ComputingAutonomous Computing

◦ Granular Computing (Information Granules)Rough SetgFuzzy Set

◦ Evolutionary ComputingGenetic Algorithm

lParticle Swarm OptimizationDifferential EvolutionArtificial Immune Systems

◦ Intelligent Hybrid Systems◦ Intelligent Hybrid Systems◦ Natured Inspired Computing

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Applications of Soft Computing

Application of SC to handwriting recognitionApplication of SC to automotive systems and manufacturingApplication of SC to image processing and data compressionApplication of SC to image processing and data compressionApplication of SC to architectureApplication of SC to decision-support systemsApplication of SC to power systemspp p yApplication of SC to Pattern RecognitionApplication of SC for Web Content Filtering Application of SC to Biometrics (Soft-Biometrics)

l f f b i iApplication of SC for Web Mining Application of SC to BiotechnologyApplication of SC for Detecting Computer Network Intruders

A lot of current issues in 2008 and 2009…A lot of current issues in 2008 and 2009

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ARTIFICIAL INTELLIGENCE ADVANCEMENT

COMPUTATIONALINTELLIGENCE

GADEAIS

NATURAL COMPUTING

SOFT COMPUTING

ARTIFICIAL

BP NetworkMLP NetworkART Network

PSO

ARTIFICIAL INTELLIGENCE

ART NetworkFuzzy SetRough SetEtc….

INTELLIGENCESYSTEMS

BIO-INSPIREDCOMPUTING

S f C i b id d fi ld d di d Soft Computing can be considered as a field dedicated to problem solving methods capable of SIMULTANEUOUSLYexploiting numerical data and human knowledge, using mathematical modeling and symbolic reasoning systems. mathematical modeling and symbolic reasoning systems. Soft computing is tolerant of imprecision, uncertainty, and partial truth.

The SOUL of SC is to make computers as SOFT as the human brain, and is capable of carrying out both quantitative and qualitative computing.

A methodology involving computing that exhibits an ability to learn and/or to deal with new situations such that the system is perceived to possess one or more with new situations, such that the system is perceived to possess one or more attributes of reason, such as generalization, discovery, association and abstraction.

C t ti l i t lli i ti l d t ti t Computational intelligence comprises practical adaptation concepts, paradigms, algorithms and implementations that enable or facilitate appropriate actions (intelligent behaviour) in complex and changing

i tenvironments.

CI Main Paradigms1 Neural Networks (NN) 1. Neural Networks (NN), 2. Evolutionary Computing (EC), 3. Swarm Intelligence (SI), 4. Fuzzy Systems (FS), y y ,5. Hybrid of these, etc.

Computational systems and methods which simulate aspects of intelligent behaviour The intention is to learn from of intelligent behaviour. The intention is to learn from nature and human performance in order to build more powerful systems. The aim is to learn from cognitive science, neuroscience, biology, engineering, and linguistics for building more powerful computational system architectures

Natural Computation, also called Natural Computing, is the field that works with

computational techniques inspired in part by nature and natural systems. The aim

of is to develop computational tools for solving complex, usually conventionally-

hard problems. This often leads to the synthesis of natural patterns, behaviorshard problems. This often leads to the synthesis of natural patterns, behaviors

and organisms, and may result in the design of novel computing systems that

use natural media with which to compute.

Bi I i d C ti i th fi ld f i ti ti th t dBio Inspired Computing is the field of investigation that drawsupon metaphors or theoretical models of biological systems inorder to design computing machines that could allow the creationof new machines with promising characteristics such fault-of new machines with promising characteristics such faulttolerance, self replication, reproduction, evolution, adaptation andlearning, and growth.

BIOLOGICALLY INSPIRED COMPUTINGBIOLOGICALLY-INSPIRED COMPUTING

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