The growing societal impact of AI-based systems has brought with it a set of risks and concerns [1, 2].Indeed, unintended and harmful behaviours may emerge from the application of machine learning (ML) algorithms, including negative side effects, reward hacking,...
The growing societal impact of AI-based systems has brought with it a set of risks and concerns [1, 2].Indeed, unintended and harmful behaviours may emerge from the application of machine learning (ML) algorithms, including negative side effects, reward hacking,...
Recent times have witnessed a flurry of advancements in ML, enabling their widespread application in domains such healthcare, security and autonomous vehicles. However, their deployment has also come at cost, resulting in racial discrimination, and in some instances...
AI Planning is concerned with producing plans that are guaranteed to achieve a robot’s goals, assuming the pre-specified assumptions about the environment in which it operates hold. However, no matter how detailed these assumptions are or how complex the resulting...
Machine learning (ML) approaches such as encoder-decoder networks and LSTM have been successfully used for numerous tasks involving translation or prediction of information (Otter et al, 2020). However, the knowledge obtained by these techniques is learned in an...
Ontologies have become fundamental AI artifacts in providing knowledge to intelligent systems. The concepts and relationships formalised in these ontologies are frequently used to semantically annotate data, helping machines to understand their meaning as humans do,...
Autonomous systems such as robots may become another appliance found in our homes and workplaces. In order to have such systems helping humans to perform their tasks, they must be as autonomous as possible, to prevent becoming a nuisance instead of an...
This project aims to investigate, design and develop new model-driven methods for AI-based network intrusion detection systems. The emphasis is on designing an AI model that is able to verify and explain its safety decisions as well as being able to efficiently and...
Recent advances in deep reinforcement learning (DRL) have allowed computer programs to beat humans at complex games like Chess or Go years before the original projections. However, the SOTA in DRL misses out on some of the core cognitive capabilities we would like to...
Agent-based models (ABMs) are powerful methods to describe the spread of epidemics. An ABM treats each susceptible individual as an agent in a simulated world. The simulation algorithm of the model tracks the health status of each agent. ABMs can provide realistic...
Cooperative Multi-Agent Planning (MAP) is a topic in symbolic artificial intelligence (AI). In a cooperative MAP system, multiple agents collaborate to achieve a common goal. A cooperative MAP solver produces human-understandable action sequences that solve a given...
The effective development and deployment of single-robot systems is known to be increasingly problematic in a variety of application domains including search and rescue, remote exploration, de-mining, etc. These and other practical settings require expensive and...
