Prerequisite courses: Artificial Intelligence I, Logic and Proof, Data Structures and Algorithms, Mathematical Methods for Computer Science, Discrete Mathematics, Probability

Aims

The aim of this course is to build on Artificial Intelligence I, first
by introducing more elaborate methods for knowledge representation and
planning within the symbolic tradition, but then by moving beyond the
purely symbolic view of AI and presenting methods developed for
dealing with the critical concept of uncertainty. The central tool
used to achieve the latter is probability theory. The course continues
to exploit the primarily algorithmic and computer science-centric
perspective that informed Artificial Intelligence I.

The course aims to provide further tools and algorithms required to
produce AI systems able to exhibit limited human-like abilities, with
an emphasis on the need to obtain richer forms of knowledge
representation, better planning algorithms, and systems able to deal
with the uncertainty inherent in the environments that most real
agents might be expected to perform within.

Lectures

Further symbolic knowledge representation. Representing
knowledge using First Order Logic (FOL). The situation calculus.
[1 lecture]

Further planning. Incorporating heuristics into
partial-order planning. Planning graphs. The GRAPHPLAN algorithm.
Planning using propositional logic. [2 lectures]

Uncertainty and Bayesian networks. Review of probability
as applied to AI. Bayesian networks. Inference in Bayesian networks
using both exact and approximate techniques. Other ways of dealing
with uncertainty. [3 lectures]

Utility and decision-making. Maximising expected utility,
decision networks, the value of information. [1 lecture]

Further supervised learning. Bayes theorem as applied to
supervised learning. The maximum likelihood and maximum a posteriori
hypotheses. Applying the Bayesian approach to neural networks.
[4 lectures]

Uncertain reasoning over time. Markov processes,
transition and sensor models. Inference in temporal models:
filtering, prediction, smoothing and finding the most likely
explanation. Hidden Markov models. [2 lectures]

Reinforcement learning. Learning from rewards and punishments.
[2 lectures]

Objectives

At the end of this course students should

have gained a deeper appreciation for the way in which computer
science has been applied to the problem of AI, and in particular for
more recent techniques concerning knowledge representation,
inference, planning and uncertainty

know how to model situations using a variety of knowledge
representation techniques

be able to design problem solving methods based on knowledge
representation, inference, planning, and learning techniques

know how probability theory can be applied in practice as a
means of handling uncertainty in AI systems

Recommended reading

* Russell, S. & Norvig, P. (2003). Artificial intelligence: a modern approach. Prentice Hall (2nd ed.).
Bishop, S. (1995). Neural networks for pattern recognition. Oxford University Press.