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MACHINE LEARNING: SYMBOL-BASED 9 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version Space Search 9.3 The ID3 Decision Tree Induction Algorithm 9.4 Inductive Bias and Learnability 9.5 Knowledge and Learning 9.6 Unsupervised Learning 9.7 Reinforcement Learning 9.8 Epilogue and References 9.9 Exercises Slide 9.1

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Page 1: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

MACHINE LEARNING: SYMBOL-BASED9

9.0 Introduction

9.1 A Framework for Symbol-based Learning

9.2 Version Space Search

9.3 The ID3 Decision Tree Induction Algorithm

9.4 Inductive Bias and Learnability

9.5 Knowledge and Learning

9.6 Unsupervised Learning

9.7 Reinforcement Learning

9.8 Epilogue and References

9.9 Exercises

Slide 9.1

Page 2: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.2

Figure 9.1: A general model of the learning process.

Page 3: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.3

Figure 9.2: Examples and near misses for the concept “arch.”

Page 4: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.4

Figure 9.3: Generalization of descriptions to include multiple examples.

(continued on next slide)

Page 5: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.5

Figure 9.3: Generalization of descriptions to include multiple examples.(continued from previous slide)

Page 6: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.6

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Page 7: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.7

Figure 9.5: A concept space.

Page 8: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.8

Defining specific to general search, for hypothesis set S as:

Page 9: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.9

In this algorithm, negative instances lead to the specialization of candidate concepts; the algorithm uses positive instances to eliminate overly specialized concepts.

Page 10: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.10

Figure 9.6: The role of negative examples in preventing overgeneralization.

Page 11: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.11

Figure 9.7: Specific to general search of the version space learning the concept “ball.”

Page 12: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.12

The algorithm specializes G and generalizes S until they converge on the target concept. The algorithm is defined:

Page 13: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.13

Figure 9.8: General to specific search of the version space learning the concept “ball.”

Page 14: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.14

Figure 9.9: The candidate elimination algorithm learning the concept “red ball.”

Page 15: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.15

Figure 9.10: Converging boundaries of the G and S sets in the candidate elimination algorithm.

Page 16: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.16

Figure 9.11: A portion of LEX’s hierarchy of symbols.

Page 17: Slide 9.1 9 SYMBOL-BASED MACHINE LEARNINGmhtay/ITEC480/Lecture/luger_ch09A.pdfMACHINE LEARNING: 9 SYMBOL-BASED 9.0 Introduction 9.1 A Framework for Symbol-based Learning 9.2 Version

A R T I F I C I A L I N T E L L I G E N C E: Structure and Strategies for Complex Problem Solving, 4th Edition George F. Luger © 2002 Addison Wesley

Slide 9.17

Figure 9.12: A version space for OP2, adapted from Mitchell et al. (1983).