View Proposal

---

Proposer

Ron Petrick

Title

Combined machine learning and automated planning

Goal

Investigate connections between machine learning and automated planning

Description

This project will explore the use of modern machine learning techniques (e.g., deep learning, reinforcement learning, etc.) for different problems in automated planning. While automated planners are good at making goal-directed plans of action under many challenging conditions, the addition of machine learning tools to the process could lead to optimisations in terms of more efficient planning or higher quality plans. Also, some symbolic aspects of the planning problem (e.g., action specification in PDDL) could be learnt by using machine learning techniques. Applications of this task will be applied in planning scenarios such as robot control or human-machine interaction.

Resources

Background

Good knowledge of a programming language like C++, python, or Java

Url

Difficulty Level

Moderate

Ethical Approval

None

Number Of Students

2

Supervisor

Ron Petrick

Keywords

Degrees

Bachelor of Science in Computer Science
Bachelor of Science in Computer Systems
Master of Engineering in Software Engineering
Master of Science in Artificial Intelligence
Master of Science in Artificial Intelligence with SMI
Master of Science in Data Science