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CSC3107: INTELLIGENT SYSTEMS
Core/Elective:Elective Semester: 1 Credits: 3
COURSE DESCRIPTION

The field of artificial intelligence (AI) is concerned with the design and analysis of autonomous agents. These are software systems and/or physical machines, with sensors and actuators, embodied; for example with in a robot or an autonomous spacecraft. An intelligent system has to perceive its environment, to act rationally towards its assigned tasks, to interact with other agents and with human beings. These capabilities are covered by topics such as computer vision, planning and acting, robotics, multiagent systems, speech recognition, and natural language understanding. They rely on a broad set of general and specialized knowledge representations and reasoning mechanisms, on problem solving and search algorithms, and on machine learning techniques.

COURSE OBJECTIVES

Explain the basic knowledge representation, problem solving, and learning methods of Artificial Intelligence
Assess the applicability, strengths, and weaknesses of the basic knowledge representation, problem solving, and learning methods in solving particular engineering problems
Develop intelligent systems by assembling solutions to concrete computational problems
Understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering

COURSE CONTENT

1. Overview of AI – AI problems, techniques – Characteristics of AI applications – General problem solving – Production systems – Control Strategies: Forward and backward chaining – Exhaustive searches: Depth first, breadth first search

2. Heuristic Techniques – Hill Climbing – Branch and bound techniques – AND/OR graphs – Problem reduction & AO* algorithm – Constant satisfaction problems

3. Knowledge representation – First order predicate calculus – Resolution principle and unification – Inference mechanism – Horn’s clauses – Semantic networks – Frame systems and value inheritance – Conceptual dependency

4. Natural Language Processing – Parsing techniques – Context free grammar – Recursive transition nets – Augmented transition nets – Case and logic grammars – Semantic analysis

5. Introduction to Neural Networks - Neural networks concepts – Learning process – Network architectures – The perceptron – Multilayer perceptrons- Back propagation algorithm – Training modes

REFERNCES

1. Artificial Intelligence: A Modern Approach (3rd Ed): Stuart Russell and Peter Norvig, PHI (2009).
2. Neural Network Learning (1st Ed): Martin Anthony, Peter L. Bartlett, Cambridge University Press (2009)
3. Artificial Intelligence: A Systems Approach (1st Ed): M. Tim Jones, Jones and Bartlett Publishers (2008)


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