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Welcome to the artif-intel wiki!
This project will be used to create material for the Search and Reasoning II course.
#Syllabus
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Constraint Satisfaction Problems
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Logic Programming with Constraints
2.1. Knowledge engineering
2.1.1. Meta-knowledge
2.1.2. Experience, common sense, and intuition.
2.1.3. Knowledge representation.
2.1.4. Semantic nets.
2.1.5. Uncertainty and ambiguity
2.2. Software development cycle for an expert system
2.2.1. Problem analysis
2.2.2. Knowledge acquisition and representation
2.2.3. Selection of reasoning and search strategies.
2.2.4. Design of an expert systems.
2.2.5. Methods of control.
2.2.6. Applications of expert systems.
- Artificial Neural Networks
3.1. Fundamentals of artificial neural networks.
3.1.1. Physiology of biological neural networks.
3.1.2. The artificial neuron.
3.1.3. One level artificial networks.
3.1.4. Multilevel artificial networks.
3.2. The perceptron.
3.2.1. Representation.
3.2.2. Training and limitations.
3.3. The back propagation network.
3.3.1. Introduction.
3.3.2. Training algorithms.
3.3.3. Recurrent models.
3.3.4. Applications and limitations.
3.4. Hopfield networks.
3.4.1. Recurrent networks.
3.4.2. Associative memories.
3.4.3. Boltsman machine.
#Bibliography
- Artificial Intelligence: A modern approach. Third edition. 2010 AIAMA
- Artificial Intelligence by Elaine Rich and Kevin Knight. Second Edition. 1991 AI by ER
- Clever Algorithms Nature-inspired recipes by Jason Brownlee. 2010 CANIR
- Constraint satisfaction problems.(2)
AIAMA Chapter 6. Constraint Satisfaction Problems. (by 12/Mar)
- Logic Programming with constraints (3)
AIAMA Chapter 7. Logical Agents (by 19/Mar)
AIAMA Chapter 13. Quantifying uncertainty.(by 26/Mar)
- Artificial neural networks (3)
AIAMA Chapter 18 Learning from examples. (by 29/Mar)
AI by ER Chapter 18 Connectionist models. (by 2/Apr)
CANIR Part 6, Chapter 7 Neural Algorithms.(by 9/Apr)
- Genetic Algorithms (2)
CANIR Part 6, Chapter 2. Evolutionary Algorithms. (by 16/Apr)