Displaying 1 - 10 of 214 honours projects.
As intelligent agents make decisions, any project aiming to realize human-like AGI should model decision-making. As we have been pursuing the WBA approach to create AGI by learning from the architecture of the entire brain, we request you to model the decision-making of the mammalian brain.
Markov Decision Processes (MDPs) are frameworks used to model decision-making in situations where outcomes are partly random and partly under the control of a decision maker. While small MDPs are inherently interpretable for people, MDPs with thousands of states are difficult to understand by humans. The K-MDP problem is the problem of finding the best MDP with, at most, K states by leveraging state abstraction approaches to aggregate states into sub-groups. The aim of this project is to measure and improve the interpretability of K-MDP approaches using state-of-the-art XAI approaches.
This is a Winter Student Research Internship ONLY not an honours or minor thesis project at this time.
Please apply here if you are interested in the role before the deadline:
https://www.monash.edu/study/fees-scholarships/scholarships/summer-winter
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Goal recognition is defined as the problem of determining an agent’s intent from observations of its behaviour. Current research in goal recognition has focused on observing agents that are trying to achieve their goals in a rational manner. Other research has focused on observing agents that are deliberately trying to trick an observer into believing they are pursuing alternative goals to the ones they are actually pursuing. However there is also a need to recognise when a behaviour is suspicious, regardless of the goal that is being tried to be achieved.
Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.
Classical propositional logic (CPL) captures our basic understanding of the linguistic connectives “and”, “or” and “not”. It also provides a very good basis for digital circuits. But it does not account for more sophisticated linguistic notions such as “always”, “possibly”, “believed” or “knows”. Philosophers therefore invented many different non-classical logics which extend CPL with further operators for these notions.
Is the user paying attention? Is the content engaging enough?
The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a
machine is referred to as ‘user engagement’. Engagement is a positive psychological state characterized by active behavioral participation, positive emotional experiences, and intense cognitive focus. Being able to detect engagement and/or attention has wide applications in consumer commerce, smart cars, augmented reality etc.
Is the user paying attention? Is the content engaging enough?
The degree of concentration, enthusiasm, optimism, and passion displayed by individual(s) while interacting with a
machine is referred to as ‘user engagement’. Engagement is a positive psychological state characterized by active behavioral participation, positive emotional experiences, and intense cognitive focus. Being able to detect engagement and/or attention has wide applications in consumer commerce, smart cars, augmented reality etc.
Deepfakes detection deals with machine learning methods, which detect if an image/video/audio sample is manipulated with a generative AI software. In recent years, deepfakes have been increasingly used for malicious purposes, including financial fraud, misinformation campaigns, identity theft, and cyber harassment. The ability to generate highly realistic synthetic content poses a serious threat to digital security, privacy, and trust in media. This project will develop methods for detecting deepfakes.
Thanks to the widespread deployment of smart meters, high volumes of residential load data have been collected and made available to both consumers and utility companies. Smart meter data open up tremendous opportunities, and various analytical techniques have been developed to analyse smart meter data using machine learning. This project will provide a new angle toward energy data analytics and aims to discover the consumption patterns, lifestyle, and behavioural changes of consumers.