Artificial Intelligence and Evolutionary Computing
Subject Code: IT/T/414 A
Credit Hours/Class:
2 Periods/Lecture & 2 Lectures/Week
- Theory Class
Marks Distribution: Two Class Test of 30 marks each in one hour duration.
Final exam of 100 marks for 3 hours written exam
Course Outcomes:
- Able to understand the basic concept of artificial intelligence architecture and agents.
- Have clear knowledge of different search techniques used in AI applications.
- Able to represent knowledge using different systems and languages.
- Able to understand the basic concept of evolutionary computing and its operations.
- Have basic knowledge of ANN.
Syllabus Outline:
Part I:
Introduction to Production Systems.
Search, Heuristic Search, A* Algorithm, AND/OR Graph, AO* Algorithm.
Knowledge Representation using Predicate Calculus;
Resolution and Theorem Proving.
Part II:
Introduction to Logic Programming Language.
Forward and Backward Search.
Part III:
Genetic Algorithms (Gas), Evolution Strategies (Ess), Evolutionary Programming (EP),
Genetic Programming (GP); Selection, Crossover, Mutation; Schema Analysis;
Analysis of Selection Algorithms; Convergence;
Part IV:
Markov and other stochastic models; Classifier Systems;
Constraint Handling; Multi-objective and Multi-modal Optimization.
Part V:
Feed Forward and Feedback (recurrent) Networks and Hybrid Learning Algorithms;
Multi Layer Perceptron and Back Propagation Learning Algorithm.
Reference Books:
- An introduction to logic programming through Prolog - Michael Spivey
- Artificial Intelligence - A Modern Approach - Stuart J. Russell and Peter Norvig
- Artificial Intelligent - A new synthesis - Nils J. Nilsson
- LOGIC, PROGRAMMING AND PROLOG - Ulf Nilsson and Jan Maluszy´nski