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Artificial Intelligence II
Lecturer: Dr S.B. Holden
No. of lectures and examples classes: 12 + 4
Prerequisite courses: Artificial Intelligence I, Logic
and Proof, Continuous Mathematics, Discrete Mathematics, Probability
Aims
The aim of this course is to give an introduction to the general field
of artificial intelligence (AI). The course approaches AI primarily
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 the 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, dealing with uncertainty, 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. Search in an adversarial environment. Computer game
playing.
- Knowledge Representation. How can we represent and deal
with commonsense knowledge and other forms of knowledge? The use of
formal logic, in particular first-order logic.
- 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.
- Uncertainty. How can we behave intelligently in an
uncertain world? Methods for dealing with uncertainty, in particular
probabilistic reasoning. Bayesian networks.
- Learning. Algorithms allowing an AI system to improve its
performance through experience.
- Philosophy. Is AI even possible? And other issues...
Objectives
At the end of this 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.
- Know how to model situations and actions using a variety of
knowledge representation techniques.
- Be able to design problem solving methods based on search,
reasoning, planning, and learning techniques
- Know how probability theory can be applied in practice as a means
of handling uncertainty in AI systems.
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.
Next: Computer Systems Modelling
Up: Michaelmas Term 2003: Part
Previous: Advanced Graphics
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Christine Northeast
Thu Sep 4 15:29:01 BST 2003