Teaching GuideTerm
Faculty of Computer Science
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Grao en Enxeñaría Informática
 Subjects
  Intelligent Systems
   Contents
Topic Sub-topic
1. Introduction 1.1. An historical perspective
1.2. Preliminary aspects
1.3. General considerations
2. Problem-Solving 2.1. Introduction to solving problems in AI
2.2. The state space concept. Searching
2.3. General characteristics of searching processes
2.4. Uninformed search strategies
2.5. Informed search strategies. Heuristic functions
2.6. Local search
3. Structured Knowledge Representation 3.1. Introduction
3.2. Declarative methods
3.3. Procedural methods
3.4. Examples and a practical case
4. Production Systems 4.1 Architecture: Knowledge base, active memory, inference engine
4.2. Dynamics of rule production systems
4.3. Basic cycle of a production system
5. A Brief Introduction to Reasoning in AI 5.1. Introduction
5.2. Categorical model
5.3. Bayesian reasoning fundamentals
6. Connectionist Systems: Origin and Context; Biological Fundamentals 6.1 Historical Evolution and Precursors.
6.2 Birth of Connectionist Systems.
6.3. Biological Basis of the Adaptive Systems
6.4. Adquisition and organization of the knowledge in Adaptive Systems.
7. Architecture, Feeding and Learning in Connectionist Systems 7.1 Processing element in Connectionist Systems.
7.2 Comparison between the biological element and the formal one.
7.3 Feeding and architecture of the Connectionist Systems.
7.4 Learning in Connectionist Systems.
8. Feed-Forward Connectionist Systems 8.1. Adaline
8.2. Perceptron
8.3. Aplications
9. Other Models of Connectionist Systems 9.1 Self-organizing networks
9.2. Other self-organizing models: Growing neural networks
9.3. Hopfield network.
10. New approaches in Sub-Symbolic Artificial Inteligence 10.1 Evolutionary Computation.
10.2 Artificial Life.
10.3 NBIC Technologies.
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