Prerequisite courses: Data Structures and Algorithms. In addition
the course requires some mathematics, in particular some use of
vectors and some calculus. For Part II (General) and Diploma students
therefore Mathematics for Computation Theory is desirable. For CST
students Part IA Natural Sciences Mathematics or equivalent, and Discrete
Mathematics is likely to be helpful although not
This course is a prerequisite for Artificial Intelligence II (Part II).
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.
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? [2 lectures]
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
Search I. How can search serve as a fundamental paradigm
for intelligent problem-solving? Simple, uninformed search
Search II: More sophisticated heuristic search
Search III: Search in an adversarial environment. Computer
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, focussing on neural networks. [3 lectures]
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
Be able to design basic problem solving methods based on AI-based
search, reasoning, planning, and learning algorithms.
* 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.