Artificial intelligence and expert systems / Itisha Gupta, Garima Nagpal
Material type:
TextPublication details: Estados unidos : Mercury learning, 2020Description: 409 páginas : Ilustraciones, Gráficas; 23 cmContent type: - Texto
- Sin mediación
- Volumen
- 9781683925071
- 006.33 G977 2020 23
| Item type | Current library | Collection | Call number | Materials specified | Copy number | Status | Notes | Date due | Barcode | |
|---|---|---|---|---|---|---|---|---|---|---|
Libro de Reserva
|
Biblioteca William Corredor Gómez. Sede Cosmos (Barranquilla) | Reserva | 006.33 G977 2020 (Browse shelf(Opens below)) | Ingeniería de Sistemas / Barranquilla | Ej. 1 | Available | Colección 1, Isla 1, Lado B, Módulo 3 | 301257620 |
CHAPTER 1. INTRODUCTION TO ARTIFICIAL INTELLIGENCE.
1.1 The turing test.
1.2 Intelligent agents.
1.2.1 software agents.
1.2.2 physical agents.
1.3 Approaches in artificial intelligence.
1.3.1 Acting humanly: the turing test approach.
1.3.2 Thinking humanly: the cognitive modelling approach.
1.3.3 Thinking rationally: the laws of thought approach.
1.3.4 Acting rationally: the rational agent approach.
1.4 Definitions of artificial intelligence.
1.4.1 Intelligent behavior.
1.4.2 Interpretations of artificial intelligence.
1.5 AI problems.
1.5.1 Task under artificial intelligence.
1.5.2 Tasks domains of artificial intelligence.
1.6 Features of AI programs.
1.7 Importance of AI.
1.8 What can artificial intelligence systems do?.
1.9 What can artificial intelligence systems not do yet?.
1.10 Advantages of AI.
1.11 Disadvantages of artificial intelligence.
CHAPTER 2. APPLICATIONS OF ARTIFICIAL INTELLIGENCE.
2.1 Finance.
2.2 Hospitals and medicine.
2.3 Robotics.
2.4 Expert systems.
2.5 Diagnosis.
2.6 Pattern recognition.
2.7 Natural language processing.
2.8 Game playing.
2.9 Image processing.
2.10 Data mining.
2.11 Big data mining.
CHAPTER 3. INTRODUCTION TO THE STATE SPACE SEARCH.
3.1 State space search.
3.1.1 The search problem.
3.2 search techniques.
3.2.1 Basic search algorithm
3.3 Types of searching techniques.
3.3.1 Uninformed search (blind search).
3.3.2 Avoiding repeated states.
CHAPTER 4. HEURISTIC SEARCH STRATEGIES.
4.1 Types of heuristic search techniques.
4.1.1 Generate and test.
4.1.2 Best first search.
4.1.3 Hill climbing search.
4.1.4 Simulated annealing search.
4.1.5 Algorithm.
4.1.6 AND
4.2 Properties of the heuristic search algorithm.
4.3 Adversary search.
CHAPTER 5: EXPERT SYSTEMS.
5.1 Definitions of expert systems.
5.2 Future of good expert systems.
5.3 Architecture and components of expert systems.
5.3.1 User interface.
5.3.2 Knowledge base.
5.3.3 Working storage (Database).
5.3.4 Interference engine.
5.3.5 Explanation facility.
5.3.6 Knowledge acquisition facility.
5.3.7 External interface.
5.4 Roles of the individuals who interact with the system.
5.4.1 Domain expert.
5.4.2 Knowledge engineer.
5.4.3 Programmer.
5.4.4 Project manager.
5.4.5 User.
5.5 Advantages of expert systems.
5.6 Disadvantages of expert systems.
CHAPTER 6. THE EXPERT SYSTEM DEVELOPMENT LIFE CYCLE.
6.1 Stages in the expert system development life cycle.
6.1.1 Problem selection.
6.1.2 Conceptualization.
6.1.3 Formalizatión.
6.1.4 Prototype construction.
6.1.5 Implementation.
6.1.6 Evaluation.
6.2 Sources of error in expert system development.
6.2.1 Knowledge errors.
6.2.2 Syntax errors.
6.2.3 Semantic errors.
6.2.4 Inference engine errors.
6.2.5 Inference chain errors.
CHAPTER 7. KNOWLEDGE ACQUISITION.
7.1 Knowledge basics.
7.2 Knowledge engineering.
7.2.1 Knowledge acquisition.
7.2.2 Knowledge engineer.
7.2.3 Difficulties in knowledge acquisition.
7.3 Knowledge acquisition techniques.
7.3.1 Natural techniques.
7.3.2 Contrived techniques.
7.3.3 Modelling techniques.
CHAPTER 8. KNOWLEDGE REPRESENTATION.
8.1 Definitions of knowledge representation.
8.3 Basics of knowledge representation.
8.4 Properties of the symbolic representation of knowledge.
8.5 Properties for the good knowledge representation systems.
8.6 Categories of knowledge representation schemes.
8.7 Types of knowledge representational schemes.
8.7.1 Formal logic.
8.7.2 Semantic net.
8.7.3 Frames.
8.7.4 Scripts.
8.7.5 Conceptual dependency (CD).
CHAPTER 9. NEURAL NETWORKS.
9.1 Neural networks vs. conventional computers.
9.2 Neural networks.
9.2.1 Neurons.
9.2.2 Types of neural networks.
9.2.3 Historical background.
9.3 Biological neural networks.
9.3.1 Biological neurons.
9.4 Artificial neural networks.
9.5 Differences between biological and artificial neural networks.
9.6 Architecture of a neural network.
9.6.1 Single layer feed-forward networks.
9.6.2 Multilayer feed-forward network.
9.6.3 Recurrent networks.
9.6.4 Feedback networks.
9.6.4 Network layers.
CHAPTER 10. THE LEARNING PROCESS.
10.1 Types of learning in a neural network.
10.1.1 Supervised learning.
10.1.2 Unsupervised learning.
10.1.3 Reinforcement learning.
10.2 Perceptron.
10.2.1 The representational power of a perceptron.
10.3 Backpropagation networks.
10.4 Advantages of neural networks.
10.5 Limitations of neural networks.
10.6 Applications of neural networks.
CHAPTER 11. FUZZY LOGIC.
11.1 Introduction to fuzzy logic.
11.1.1 Definition of fuzzy logic.
11.1.2 Features of fuzzy logic.
11.1.3 Advantages of fuzzy logic.
11.1.4 Disadvantages of fuzzy logic.
11.2 Crisp set (Classical set).
11.3 Fuzzy set.
11.3.1 Linguistic variables in a fuzzy set.
11.4 Membership function of crisp logic.
11.5 Membership function of the fuzzy set.
11.6 Fuzzy set operations.
11.6.1 Union.
11.6.2 Intersection.
11.6.3 Complement.
11.6.4 Equality of two fuzzy sets.
11.6.5 Containment.
11.6.6 Normal fuzzy set.
11.6.7 Support of a fuzzy set.
11.6.8 a-cut or a-level set.
11.6.9 Disjunctive sum (Exclusive OR).
11.6.10 Disjoint sum.
11.6.11 Difference.
11.6.12 The bounded difference.
11.7 Properties of a fuzzy set.
11.8 Differences between a fuzzy set and crip set.
11.9 Differences between Boolean logic and fuzzy logic.
CHAPTER 12. FUZZY SYSTEM.
12.1 Fuzzy rule.
12.1.1 Fuzzy rules as relations.
12.1.2 Interpretation of fuzzy rules.
12.2 Fuzzy reasoning.
CHAPTER 13. FUZZY EXPERT SYSTEMS.
13.1 The need for fuzzy expert system.
13.2 Operations on a fuzzy expert system.
13.2.1 Fuzzification (fuzzy input).
13.2.2 Fuzzy operator.
13.2.3 Fuzzy interferencing (Implication).
13.2.4 Aggregate all output.
13.2.5 Defuzzification.
13.3 Fuzzy inference systems.
13.3.1 Mamdani fuzzy inference method.
13.3.2 Sugeno inference method (TSK fuzzy model of takagi, sugeno, and kang).
13.3.3 Choosing the inference method.
13.4 The fuzzy inference process in a fuzzy expert system.
13.1.1 Monotonic inference.
13.4.2 Non-monotonic inference.
13.4.3 Downward monotonic inference.
13.5 Types of fuzzy expert systems.
13.5.1 Fuzzy control.
13.5.2 Fuzzy reasoning.
13.6 Fuzzy controller.
13.6.1 Components of a fuzzy controller.
13.6.2 application areas of fuzzy controller.
CHAPTER 14.LOGIC PROGRAMMING.
14.1 Introduction.
14.2 Difference between C/C++ and prolog.
14.3 How does prolog work?.
14.4 A little history.
14.5 Converting English to prolog.
14.6 Goals.
14.6.1 How prolog satisfies goals.
14.7 Queries.
14.8 Clauses.
14.8.1 Facts.
14.8.2 Rules.
14.9 Notation in prolog for building blocks.
14.9.1 atoms.
14.9.2 Variables.
14.9.3 Data types and structures.
14.10 Arithmetic operations.
14.11 Strings.
CHAPTER 15. ADVANCED PROLOG.
15.1 Input and output predicates.
15.1.1 Terms and character I/O.
15.1.2 File I/O.
15.2 Backtracking.
15.2.1 Problems with backtracking.
15.3 Cut.
15.4 Fail.
15.4.1 Cut and fail combination.
15.5 Recursion.
15.6 Prolog data structure.
15.6.1 Terms.
15.6.2 Unification.
15.7 Dynamic database.
15.8 Programs in prolog.
15.9 Problems with prolog.
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