To provide the student with a solid theoretical and practical understanding, knowledge and skill in the application of artificial intelligence and expert systems. This new module is created to provide an appropriate BE/ME masters level code to the level 9 module in AI in ECE department. This module will be offered to the Master of Engineering in Electronic and Computer Engineering programme using module ID 3301 Artificial Intelligence

Section (i) - Introduction to Prolog and "Logic Programming" Rule-based systems and logic programming. The resolution principle, unification & backtracking. Recursion & iteration. Prolog representation of algorithms. Extra-logical features of Prolog. Section (ii) - State-Space Search Use of state-space search in A.I. programming. Representation of problems in state-space form. Prolog representation of state-spaces. Heuristics. Search strategies: depth-first, breadth-first, hillclimbing, best-first, branch & bound, Algorithm A, Algorithm A*. Admissibility, Monotonicity, Informedness. Section (iii) - Expert Systems The structure of an expert system. Knowledge Representation. The inference engine. Inference strategies. Reasoning under uncertainty. Section (iv) - Neural Networks Neural models: McCulloch & Pitts, Rosenblatt. Hebbian learning. The Adaline. Multi-layer Perceptrons & Backpropagation. Associative networks. Competitive networks.

1. Use the resolution technique to solve problems stated in terms of predicate logic. 2. Formulate a search problem in terms of an appropriate state-space representation. 3. Apply suitable search algorithms and heuristics to problem solving. 4. Apply neural network techniques to the solution of classification problems. 5. Construct problem-solving programs in a suitable A.I. language such as Lisp or Prolog. 6. Evaluate the current state of the art in artificial intelligence research and applications.

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Lectures/Labs/Tutorials, Self-directed research and project work.