Module Overview

Computers have become commonplace in many areas of our lives and are able to accomplish many things that humans would find difficult, if not impossible, to do by their own unaided efforts. Whilst computers can perform many calculations in a very short time they generally do not possess the ability to learn or to reason about novel situations or to process incomplete or uncertain data. They will need knowledge of the environment in which they operate so that they can understand what their sensors are monitoring and so that they can behave rationally. This module demonstrates the basic principles and methods of Artificial Intelligence (AI) and provides the basis for understanding and later choosing the correct tools for building such systems. Applications that motivate the development of Artificial Intelligence technology include intelligent robots, automated navigation for autonomous vehicles, object recognition and tracking, medical diagnosis, language communications and many others. Any application that requires human-like intelligence is an application for Artificial Intelligence.

This module aims to demonstrate a variety of techniques for capturing human knowledge and represent it in a computer in a way that enables the machine to reason over the data represented and mimic the human ability to deal with incomplete or uncertain data. This module introduces the range of artificial intelligence elements that future robots or intelligent machines must possess as embedded implementations if they are to behave intelligently.

Knowledge Representation and Uncertainty Reasoning   o      Bayesian Network   o      Dempster-Shafer Theory   Learning   o      Introduction to Learning   o      Nearest Mean   o      kNN   o      Clustering   o      Decision Tree   o      Neural Networks   o      Support-Vector Machines   o      Genetic Algorithms   Visual Perception   o      Feature Extraction   o      Region Detection and Segmentation   o      Classification and Pattern Recognition   Natural Language Understanding   o      Syntax, Semantics and Context Analysis   o      Probabilistic Language Processing   o      Speech Processing and Hidden Markov Model

Recommended   Russell, S. J. and Norvig, P., (2010), Artificial Intelligence, A Modern Approach, third edition, Prentice Hall, Pearson Education International, ISBN-13: 978-0-13-207148-2.   Callan, R., (2003), Artificial Intelligence, Palgrave MacMillan, ISBN: 0333801369.   Efford, N., (2000), Digital Image Processing, A Practical Introduction using Java, Addison Wesley, ISBN 0201596237.   Negnevitsky, M., (2004), Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), Addison Wesley, ISBN: 0321204662.   background   Winston, P.H, (1992), Artificial Intelligence (3rd Edition), Addison Wesley, ISBN: 0201533774.