This is a graduate course that covers advanced topics in Artificial Intelligence. Topics vary from one offering to the next and  are likely to be drawn from the following list:  planning & scheduling, games, constraint-based reasoning, knowledge compilation, model-based reasoning, decision-making under uncertainty, reinforcement learning.

2011

In 2011, the Advanced AI course will focus on reasoning about "discrete event systems". Discrete event models capture the dynamics of systems that evolve by discrete events. Examples of such
systems include digital control systems, as found in embedded and autonomous systems (including robots), telecommunications, etc., but also protocols/workflows, games, and even models of evolution. System models can be used for many purposes:

• Monitoring and diagnosis: Observing (as far as it can be observed) the

behaviour of the system over time, the model is used to infer the  state of the system,
check if it is functioning correctly, and if not, to determine what faults may
have occurred.

• Planning and control: Using the model of system behaviour, devise a plan of control

actions to drive/guide the system to a desired goal, state or keep it in desired states.

• Verification: Analyse the model to determine if it meets desired

criteria.

The course will cover some material related to all these uses, as well as the art of modelling practical systems indiscrete-event  formalisms.

On completing this course students will have a deep understanding of the taught topics:

1. Planning is concerned with finding a sequence of actions that achieve a predefined goal. Students will learn key planning algorithms as well as the main applications of planning.
2. Games: Game playing algorithms have been developed for most important games. The quality of their play varies quite a lot though. While the world chess master is a computer, Go programs have no chance even against mediocre players. The student will learn state-of-the-art techniques for selected games.
3. Search: The solution of many AI problems, in particular planning, scheduling, and game problems, is based on extensive search in solution space. Students will learn sophisticated AI search algorithms, in particularly exact and heuristic methods for pruning the search space.
4. Scheduling is concerned minimizing the time required to complete a set of interrelated jobs, each of which consisting of a sequence of actions, where each action has a given duration and might require some resources. The student will learn representative algorithms and apply them to representative problems like time-tabling.
5. Spatial and Temporal Reasoning is concerned with different ways of representing spatial and temporal knowledge and with techniques for reasoning about this knowledge and tools and methods for analysing reasoning algorithms.
6. Knowledge representation: Knowledge-based systems have to internally represent knowledge about the world. The choice of representation is influenced by the required expressivity and the required efficiency of doing inference. The student will learn how to represent knowledge for complex real-world problems.
7. Decision-making under uncertainty is concerned with planning for noisy or uncertain problems.
8. Reinforcement learning is concerned with how an agent ought to take actions in an environment so as to maximize its long-term reward. It deals with the problem of sequential decision-making in unknown environments. Classical applications are algorithms that learn to play games like checkers, Backgammon, and recently Go purely by self-play.
9. Agents: Most AI problems can be phrased as one or multiple agents interacting with each other by sending and receiving signals. The student will learn how to classify and design multi-agent systems and study their dynamics.
10. Foundations: The grand goal of AI is concerned with building systems exhibiting generic intelligence, rather than solving specific problems like chess. This raises the questions of what intelligence is and whether machines can ever surpass human intelligence. Students will learn the necessary philosophical, statistical, and computational concepts that allow to address these difficult questions.

2011

1.  Gain both a wide and a deep knowledge of the topic(s) taught in the current instance of the course.

2.  Improve their skills at navigating through, and critically examining, the scientific literature on the taught topic(s).

2011

Assignments (100%)

Enrolment in Master of Computing7705 and COMP6320 or COMP3620

Parts of

Marcus Hutter (2005) Universal Artificial Intelligence, EATCS, Springer.
http://www.springer.com/computer/ai/book/978-3-540-22139-5

and

Shane Legg (2008) Machine Super Intelligence, Lulu, PhD thesis
http://www.lulu.com/content/2043514

http://cs.anu.edu.au/courses/COMP8620/