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 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. 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? Semantic
networks, frames and rules.
Reasoning. 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.
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.