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Intelligent Systems II:  2008-2009

Prerequisites

Lecturer

Degrees

Schedule B2Computer Science

Schedule B2Mathematics and Computer Science

ECS Part IIMEng Engineering and Computing Science

Schedule BMSc in Advanced Computer Science

MSc by Research

Term

Overview

To build on the introductory material from Intelligent Systems I and look at the issues involved in practical intelligent systems. In particular the course considers problems such as uncertain information, timeliness, and inexact control that are found when dealing with embodied agents (e.g., robots).

Synopsis

  • Learning. Supervised learning with examples (e.g., decision trees and neural networks). Reinforcement learning, parameter estimation. [4]
  • General Bayesian networks. [2]
  • Theorem proving in propositional and predicate logic. Resolution and unification. Logic programming and Prolog. [5]
  • Negation in logic programming and nonmonotonic reasoning. Answer set programming. Common sense reasoning and Belief revision. Notions of situation calculus and dynamic logic.[5]

Syllabus

Learning in intelligent systems.

General inference in Bayesian networks.

Logic, knowledge and belief representation and reasoning.

Logic programming and notions of Prolog.

Non-monotonic reasoning, common-sense reasoning and belief revision.

Examples.

 

Reading list

  • I. Bratko, Prolog Programming for Artificial Intelligence (3rd ed), Addison-Wesley, 2000.
  • S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (2nd ed), Prentice-Hall, 2001.
Plus notes on Robotics.

Taking our courses

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Other matriculated University of Oxford students who are interested in taking this, or other, courses in the Department of Computer Science, must complete this online form by 17.00 on Friday of 0th week of term in which the course is taught. Late requests, and requests sent by email, will not be considered. All requests must be approved by the relevant Computer Science departmental committee and can only be submitted using this form.