Teaching

Courses

COMP90054 - AI Planning for Autonomy

Semester 1 & 2

Level: Graduate Coursework

Overview

The key focus of this subject is the foundations of autonomous agents that reason about action, applying techniques such as automated planning, reinforcement learning, game theory, and their real-world applications. Autonomous agents are active entities that perceive their environment, reason, plan and execute appropriate actions to achieve their goals. The subject focuses on the foundations that enable agents to reason autonomously about goals & rewards, perception, actions, strategy, and the knowledge of other agents during collaborative task execution.

Topics Covered

  • Search Algorithms and Heuristic Functions
  • Classical (AI) Planning
  • Markov Decision Processes
  • Reinforcement Learning

Learning Outcomes

  • Apply theoretical concepts of reasoning about actions to single and multi-agent problems
  • Analyse, design, and implement automated planning, reinforcement learning, and game theoretic techniques
  • Critically evaluate the strengths, weaknesses, and ethical consequences of different approaches
  • Justify and choose appropriate techniques for different problems
  • Communicate technical solutions about automated planning and reinforcement learning
AI Planning Search Algorithms Reinforcement Learning Python

COMP20003 - Algorithms and Data Structures

Semester 2

Level: Undergraduate (Year 2)

Overview

An introduction to the most frequently used data structures and their associated algorithms. The emphasis is on justification of algorithm correctness, analysis of algorithm performance, and choosing the right data structure for the problem at hand. Quality implementation of algorithms and data structures is emphasized, leading up to an exam with a programming component.

Topics Covered

  • Justification of Algorithm Correctness
  • Asymptotic and Empirical Analysis
  • Sorting and Searching Algorithms
  • Fundamental Data Structures (Trees, Hash Tables)
  • Graph Algorithms
  • Algorithm Implementation and Design

Learning Outcomes

  • Present arguments for correctness/incorrectness of algorithms
  • Evaluate efficiency behaviour of algorithms
  • Choose appropriate data structures and algorithms
  • Implement chosen data structures and algorithms
Data Structures Algorithms Complexity Analysis Programming

Student Supervision

I supervise student projects and research in AI planning, search algorithms, and autonomous systems. I am particularly interested in students working on:

  • Automated Planning Algorithms
  • Epistemic Planning Systems
  • Game AI Development
  • Multi-Agent Systems
  • Optimization Techniques

If you are a student interested in working on cutting-edge AI research, please get in touch to discuss potential opportunities.