Artificial Intelligence Logic Designer (AILD10002)
Specialization: Artificial Intelligence
Duration: 12 Months
Course Details
Syllabus,
Introduction to Artificial Intelligence
Includes all the hypothetical theories, know-hows and myths about AI.
Problem formulation
Review of tree structure • Review of graph structure • Graph implementation • State space representation • Search graph and search tree
Production system
• Production system • Inference engine • Working memory • Knowledge base • Pattern matching • Conflict resolution • Forward inference • Back inference
Ontology
What is ontology? • Semantic network • Frame • Structural knowledge • Declarative knowledge • Procedural knowledge
Propositional logic
Propositional logic • Definition of logic formula • Meaning of logic formula • Classification of logic formula • Proof based on truth table • Basic laws • Clausal form/Conjunctive canonical form • Formal proof
First order predicate logic
Predicate logic • Term and logic formula • Clausal form/Conjunctive canonical form • Standardization of logic formula • Unification and resolution • Horn clause and Prolog
Fuzzy logic
Definition of fuzzy set • Membership function • Notation of fuzzy set • Operations of fuzzy set • Fuzzy number and operations • Extension principle • Fuzzy rules • De-fuzzification • Fuzzy control
Pattern Recognition
• Concept and concept learning. • Pattern classification and recognition. • Feature vector representation of patterns. • Nearest neighbor based learning. • Discriminant function and decision boundary. • Multi-class pattern recognition. • General for
Distance-Based Neural Networks
Pattern recognition again • Feature extraction/selection • Self-organizing neural network • Winner-take-all learning strategy • Learning vector quantization • R4-rule
Multilayer Neural Networks
What is a neural network? • Model of one neuron. • Learning rules for one neuron. • Layered neural network. • Learning of multilayer neuron network.
Decision trees
Review of useful tree structures. • What is a decision tree? • Make a decision using decision tree. • Induction of decision trees. • Neural network decision tree. • Induction of neural network decision trees.
Population-based search
Genetic algorithm (GA) – Individual, population, and generation – Genotype, phenotype, and fitness – Selection, crossover, and mutation • Particle swarm optimization (PSO) – Particle and swarm – Personal factor and social factor