MITM 613 Intelligent System

1. MITM 613Intelligent System Chapter 0: Introduction

2. Contents • Introduction • Objectives • Outcomes • Chapters • Plan • Assessment • References • Conclusion and Expectations Abdul Rahim Ahmad

3. Introduction • This course emphasises on the methods and techniques that can be used to develop intelligent systems. • knowledge-based techniques • expert and rule-based system • object-oriented and frame-based systems • intelligent agents. • computational intelligence or Machine Learning techniques • neural networks and its similar tools • genetic algorithms • Fuzzy logic • a hybrid of both. Abdul Rahim Ahmad

4. Objectives • To provide understanding of intelligent systems and the various methods and tools in implementing Intelligent Systems. • To demonstrate the implementation of individual methods within the scope of Intelligent systems • To compare the pros and cons of each method of developing Intelligent Systems. • To develop the ability to implement a particular Intelligent system of choice Abdul Rahim Ahmad

5. Outcomes At the end of the course, you should be able to: • Explain the various methods of implementing Intelligent systems • Describe the issues involved in each method of implementing an Intelligent System. • Describe the tools that can be used. • Develop a particular intelligent system of choice in a class project environment. Abdul Rahim Ahmad

6. Main text • Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2nd Edition, CRC Publication (2000). • http://www.adrianhopgood.com/ Abdul Rahim Ahmad

7. Abdul Rahim Ahmad

8. Chapters from Hopgood Includes Fuzzy Logic • Chapter one: Introduction • Chapter two: Rule-based systems • Chapter three: Dealing with uncertainty • Chapter four: Object-oriented systems • Chapter five: Intelligent agents • Chapter six: Symbolic learning • Chapter seven: Optimization algorithms • Chapter eight: Neural networks • Chapter nine: Hybrid systems • Chapter ten: Tools and languages • Chapter eleven: Systems for interpretation and diagnosis • Chapter twelve: Systems for design and selection • Chapter thirteen: Systems for planning • Chapter fourteen: Systems for control • Chapter fifteen: Concluding remarks Specifically on Genetic Algorithm Additional Chapter – Support Vector Machine

9. Chapter one: Introduction 1.1 Intelligent systems 1.2 Knowledge-based systems 1.3 The knowledge base 1.4 Deduction, abduction, and induction 1.5 The inference engine 1.6 Declarative and procedural programming 1.7 Expert systems 1.8 Knowledge acquisition 1.9 Search 1.10 Computational intelligence 1.11 Integration with other software

10. Chapter two: Rule-based systems 2.1 Rules and facts 2.2 A rule-based system for boiler control 2.3 Rule examination and rule firing 2.4 Maintaining consistency 2.5 The closed-world assumption 2.6 Use of variables within rules 2.7 Forward-chaining (a data-driven strategy) 2.7.1 Single and multiple instantiation of variables 2.7.2 Rete algorithm 2.8 Conflict resolution 2.8.1 First come, first served 2.8.2 Priority values 2.8.3 Metarules 2.9 Backward-chaining (a goal-driven strategy) 2.9.1 The backward-chaining mechanism 2.9.2 Implementation of backward-chaining 2.9.3 Variations of backward-chaining 2.10 A hybrid strategy 2.11 Explanation facilities

11. Chapter three: Dealing with uncertainty 3.1 Sources of uncertainty 3.2 Bayesian updating 3.2.1 Representing uncertainty by probability 3.2.2 Direct application of Bayes’ theorem 3.2.3 Likelihood ratios 3.2.4 Using the likelihood ratios 3.2.5 Dealing with uncertain evidence 3.2.6 Combining evidence 3.2.7 Combining Bayesian rules with production rules 3.2.8 A worked example of Bayesian updating 3.2.9 Discussion of the worked example 3.2.10 Advantages and disadvantages of Bayesian updating 3.3 Certainty theory 3.3.1 Introduction 3.3.2 Making uncertain hypotheses 3.3.3 Logical combinations of evidence 3.3.4 A worked example of certainty theory 3.3.5 Discussion of the worked example 3.3.6 Relating certainty factors to probabilities 3.4 Possibility theory: fuzzy sets and fuzzy logic 3.4.1 Crisp sets and fuzzy sets 3.4.2 Fuzzy rules 3.4.3 Defuzzification 3.5 Other techniques 3.5.1 Dempster–Shafer theory of evidence 3.5.2 Inferno

12. Chapter four: Object-oriented systems Skipped

13. Chapter five: Intelligent agents 5.1 Characteristics of an intelligent agent 5.2 Agents and objects 5.3 Agent architectures 5.3.1 Logic-based architectures 5.3.2 Emergent behavior architectures 5.3.3 Knowledge-level architectures 5.3.4 Layered architectures 5.4 Multiagent systems 5.4.1 Benefits of a multiagent system 5.4.2 Building a multiagent system 5.4.3 Communication between agents

14. Chapter six: Symbolic learning Skipped

15. Chapter seven: Optimization algorithms 7.1 Optimization 7.2 The search space 7.3 Searching the search space 7.4 Hill-climbing and gradient descent algorithms 7.4.1 Hill-climbing 7.4.2 Steepest gradient descent or ascent 7.4.3 Gradient-proportional descent 7.4.4 Conjugate gradient descent or ascent 7.5 Simulated annealing • 7.6 Genetic algorithms • 7.6.1 The basic GA • 7.6.2 Selection • 7.6.3 Gray code • 7.6.4 Variable length chromosomes • 7.6.5 Building block hypothesis • 7.6.6 Selecting GA parameters • 7.6.7 Monitoring evolution • 7.6.8 Lamarckian inheritance • 7.6.9 Finding multiple optima • 7.6.10 Genetic programming

16. Chapter eight: Neural networks 8.1 Introduction 8.2 Neural network applications 8.2.1 Nonlinear estimation 8.2.2 Classification 8.2.3 Clustering 8.2.4 Content-addressable memory 8.3 Nodes and interconnections 8.4 Single and multilayer perceptrons 8.4.1 Network topology 8.4.2 Perceptrons as classifiers 8.4.3 Training a perceptron 8.4.4 Hierarchical perceptrons 8.4.5 Some practical considerations 8.5 The Hopfield network 8.6 MAXNET 8.7 The Hamming network 8.8 Adaptive Resonance Theory (ART) networks 8.9 Kohonen self-organizing networks 8.10 Radial basis function networks

17. Chapter nine: Hybrid systems 9.1 Convergence of techniques 9.2 Blackboard systems 9.3 Genetic-fuzzy systems 9.4 Neuro-fuzzy systems 9.5 Genetic-neural systems 9.6 Clarifying and verifying neural networks 9.7 Learning classifier systems

18. Chapter ten: Tools and languages • 10.1 A range of intelligent systems tools • 10.2 Expert system shells • 10.3 Toolkits and libraries • 10.4 Artificial intelligence languages • 10.4.1 Lists • 10.4.2 Other data types • 10.4.3 Programming environments • 10.5 Lisp • 10.5.1 Background • 10.5.2 Lisp functions • 10.5.3 A worked example • 10.6 Prolog • 10.6.1 Background • 10.6.2 A worked example • 10.6.3 Backtracking in Prolog • 10.7 Comparison of AI languages

19. Assessment • Assignments (3 x 5)15% • Projects(best of 2 x 15)15% • Mid. Semester Examination 30% • Final Examination 40% Abdul Rahim Ahmad

20. All References • Adrian A. Hopgood, Intelligent Systems for Engineers and Scientists, 2nd Edition, CRC Publication (2000). • Vojislav Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models (Complex Adaptive Systems), MIT Press 2001 • Artificial Intelligence, Elain Rich, Kevin Knight, Shivashanker Nair, McGraw Hill, 2009 Abdul Rahim Ahmad

21. Conclusion/Expectations • Able to explain fundamental concepts. • Able to implement selected methods. • Appreciation for using intelligent methods in other field. Abdul Rahim Ahmad