Artificial Intelligence I

Lecturer: Dr S.B. Holden

No. of lectures: 12

Prerequisite courses: Data Structures and Algorithms, Continuous Mathematics, Discrete Mathematics or Mathematics for Computation Theory

This course is a prerequisite for Artificial Intelligence II (Part II).

Aims

The aim of this course is to provide an introduction to some basic issues and algorithms in artificial intelligence (AI). The coverage concentrates on presenting the general background to the field from a computer science perspective--relatively little reference is made to the complementary perspectives developed within psychology, neuroscience or elsewhere--and on some classical techniques from AI. 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. How can search serve as a fundamental paradigm for intelligent problem-solving? Simple, uninformed search algorithms and more sophisticated heuristic search algorithms. Constraint satisfaction problems. Search in an adversarial environment. Computer game playing.

• Knowledge Representation. How can we represent and deal with commonsense knowledge and other forms of knowledge?

• Reasoning. How can we use inference in conjunction with a knowledge representation scheme to perform reasoning about the world and thereby to solve problems?

• Planning. Methods for planning in advance how to solve a problem.

• Learning. A brief introduction to neural networks.

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 books

* Russell, S. & Norvig, P. (2003). Artificial intelligence: a modern approach. Prentice-Hall (2nd ed.).
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.