This unit is a core elective in the Master of Artificial Intelligence to be introduced in 2020.

Automated planning is an increasingly popular branch of AI that concerns agents expecting a set of strategies or action sequences to achieve certain goals. This leads the way too many relevant topics such as plan/goal recognition, robotics, scheduling and optimisation.

18/07/2019: Weekly tutorial hours increased from 1/week to 2/week, in order to make the tutorials interactive and incorporate team activities.

31/10/2019: Updating the prerequisites to include FIT9136. Effective 2020.

11/11/2019: Removing FIT9131, FIT9133 and FIT9136 from prerequisites, as they are already prereq to FIT5047. Effective S1, 2020.

14/10/2020: Following lessons learnt from the first run of this course we are opting to make the following changes :

Reduce lecture time to 1 hour, as it is sufficient to cover the high level material and more focus should be given to the practical, coding part.

Change tutorials to labs : The labs will be run as a first hour discussion and second hour implementation after which they will need to submit a lab report each week. Each lab report will be worth 2%. There will still be 2 big assessments, each at 25%, and the test will be 26%. The test and 2 assessments will be hurdles but the lab reports will not. We feel since this course is a practical, problem solving course, this new format will enable us to better implement key concepts learnt, while also increasing student engagement.

Upon successful completion of this unit students should be able to:

1. explain the theoretical concepts of automated planning and reasoning techniques
2. apply agent modelling techniques to analyse, design and implement a small agent-based system
3. evaluate, design, and implement automated planning and reasoning technique
4. describe strengths and weaknesses of different automated planning and reasoning approaches for software agents
5. apply automated planning and concurrent programming techniques to non-trivial distributed problems.

020119 Artificial Intelligence

This unit focuses on the foundations of automated planning and reasoning and their real-world applications. Autonomous agents are active agents that independently execute actions to achieve a certain goal or goals. These agents perceive their environment and reason and plan in order to effect their environment and achieve their goals. This is a very popular and highly researched AI approach and has many significant implications beyond the traditional area of AI (optimisation, robotics, scheduling, etc?). This course will give students the foundations to develop and design their own autonomous agents.

On-campus

Exam (2 hours): 26%, ULOs 1,2,4,5

In-semester assessment: 74% overall comprising

Assessment 1 - 25% ULOs 2,3,5. Additional hurdle of 45%.

Assessment 2 - 25% ULOs 2,3,5. Additional hurdle of 45%.

12 Weekly lab report, 2% each, for a total of 24%, ULOs 1,2,3,4,5

Minimum total expected workload equals 12 hours per week comprising:

• One hours/week lectures
• Two hour/week lab

A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision.

FIT5047