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:

1. Able to understand the basic concept of artificial intelligence architecture and agents.

2. Have clear knowledge of different search techniques used in AI applications.

3. Able to represent knowledge using different systems and languages.

4. Able to understand the basic concept of evolutionary computing and its operations.

5. 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:

1. An introduction to logic programming through Prolog - Michael Spivey

2. Artificial Intelligence - A Modern Approach - Stuart J. Russell and Peter Norvig

3. Artificial Intelligent - A new synthesis - Nils J. Nilsson

4. LOGIC, PROGRAMMING AND PROLOG - Ulf Nilsson and Jan Maluszy´nski