Computer Laboratory – Course material 2008–09: Artificial Intelligence I

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Artificial Intelligence I

Lecturer: Dr M. Jamnik

No. of lectures: 12

Prerequisite courses: Algorithms. In addition the course requires some mathematics, in particular some use of vectors and some calculus. Part IA Natural Sciences Mathematics or equivalent, and Discrete Mathematics, are likely to be helpful although not essential.

This course is a prerequisite for the Part II courses Artificial Intelligence II and Natural Language Processing.

Aims

The aim of this course is to provide an introduction to some basic issues and algorithms in artificial intelligence (AI). The course approaches AI from an algorithmic, computer science-centric perspective; relatively little reference is made to the complementary perspectives developed within psychology, neuroscience or elsewhere. The course aims to provide some basic tools and algorithms required to produce AI systems able to exhibit limited human-like abilities, particularly in the form of problem solving by search, representing and reasoning with knowledge, planning, and learning.

Lectures

Introduction. What is it that we're studying? Why is something that looks so easy to do actually so difficult to compute? Theories and methods: what approaches have been tried? What does this course cover, and what is left out?
Agents. A unifying view of AI systems. How could we approach the construction of such a system? How would we judge an AI system? What should such a system do and how does it interact with its environment?
Search I. How can search serve as a fundamental paradigm for intelligent problem-solving? Simple, uninformed search algorithms.
Search II. More sophisticated heuristic search algorithms.
Search III. Search in an adversarial environment. Computer game playing.
Constraint satisfaction problems.
Knowledge representation and reasoning. How can we represent and deal with commonsense knowledge and other forms of knowledge? Semantic networks, frames and rules. How can we use inference in conjunction with a knowledge representation scheme to perform reasoning about the world and thereby to solve problems? Inheritance, forward and backward chaining.
Planning. Methods for planning in advance how to solve a problem. The partial-order planning algorithm.
Learning. A brief introduction to supervised learning from examples, focusing on neural networks. [4 lectures]

Objectives

At the end of the course students should

appreciate the distinction between the popular view of the field and the actual research results
appreciate different perspectives on what the problems of artificial intelligence are and how different approaches are justified
be able to design basic problem solving methods based on AI-based search, reasoning, planning, and learning algorithms

Recommended reading

* Russell, S. & Norvig, P. (2003). Artificial intelligence: a modern approach. Prentice Hall (2nd ed.).
Cawsey, A. (1998). The essence of artificial intelligence. Prentice Hall.
Luger, G.F. & Stubblefield, W.A. (1998). Artificial intelligence: structures and strategies for complex problem solving. Addison-Wesley.
Dean, T., Allen, J. & Aloimonos, Y. (1995). Artificial intelligence: theory and practice. Benjamin/Cummings.

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