Honours and Minor Thesis projects

Propositional satisfiability (SAT) is a well-known example of NP-complete problems. Although NP-completeness may be perceived as a drawback, it allows one to solve all the other problems in NP by reducing them to SAT and relying on the power of modern SAT solvers. This is confirmed by a wealth of successful examples of use cases for modern SAT solving, including generalisations and extensions of SAT as well as a wide variety of practical applications in artificial intelligence (AI).

Mini-CP https://www.info.ucl.ac.be/~pschaus/minicp.html is a minimal form of constraint programming solver, designed to allow for easy experimentation and learning. 

One of the most efficient approaches to discrete optimisation solving is using lazy clause generation, which is a hybrid SAT/CP approach to solving problems.  But MiniCP does not currently support this. 

Given a knowledge base describing the existing background constraints and assumptions about what is possible in the world as well as the prior experience of an autonomous agent on the one hand and probabilistic perception of the current state of the world of the autonomous agent, on the other hand, it is essential to devise and efficiently enumerate the most consistent world models that are likely to be valid under the prior knowledge in order to refine the agent’s up-to-date perception and take the most suitable actions.

Building a robust and trustworthy (semi-)autonomous agent requires us to build a consistent picture of the state of the world based on the data received from some perception module.

Please note this advert is for a Internship. It is not currently an advertisement for an honours or masters thesis project.

Please note you can ONLY apply for this internship via the  internship application form. 

Internship Project:

In this project, you will build an autonomous agent in the MineRL environment for playing Minecraft or an agent for Animal-AI.  Herein, you will learn how to incorporate symbolic prior knowledge for improving the performance of an agent trained by using deep reinforcement learning (RL) technique, which is the core technique to build AlphaGo.

Note: this is advertising for summer 2022 internship project (not an Honours Project)

The energy industry is evolving, and transiting to a new era with renewable energy being at the forefront. Making Australia aware of the lessons from the past and the predictions for the future is essential for us to start to understand how the country is changing for the better and what still needs to be done to ensure a more sustainable energy future for the population. 

Please note this advert is for a Summer Internship as part of a collaboration between FIT, Arts and MADA. It is not an advertisement for an honours or masters thesis project at present.  Please note you can ONLY apply for the internship via the Monash internship page.

(This is *not* a minor thesis or honours project, but a summer scholarship project advert only available to existing Monash taught students).

This project provides an opportunity to build on an existing funded project that focussed on document annotation using a web platform. The idea of this project is to build systems that can help humans add labels to documents more rapidly. 

Android is a mobile operating system that occupies 72.11% market share globally. As the most popular mobile operating system, the android mobile app industry has been active for over a decade, generating billions of dollars in revenue for Google and thousands of mobile app developers. Several third-party Android app stores in China are estimated to generate over $8 billion in yearly revenue. Meanwhile, the number of bugs and vulnerabilities in mobile apps is growing. In 2016, 24.7% of mobile apps contained at least one high-risk security flaw.