Chloe M. Barnes

Chloe M. Barnes
Aston University · Department of Computer Sciences

Doctor of Philosophy

About

13
Publications
1,823
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44
Citations
Introduction
Chloe M. Barnes is a doctoral candidate at Aston University, UK, after studying for a Bachelor's Degree in Computer Science at the same institution in 2017. She is also currently working as a Research Fellow for the Think Beyond Data initiative at Aston University. Her research interests are inspired by the fields of human psychology and sociology, and are directed towards that of computational self-awareness, neuroevolution, and interference within multi-agent systems.

Publications

Publications (13)
Article
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or damaged. State-of-the-art works in the field report high accuracy results on small datasets (<1000 images)...
Chapter
In this paper, the game of partially observable Ms. Pacman is used as a sandbox to evaluate Artificial Neural Networks (ANNs) that control multiple opponents (i.e. the ghosts). Comparisons between one central ANN that controls all ghosts, and multiple distinct ANNs, each controlling one ghost, are made. The NEAT algorithm is employed to evolve the...
Article
Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as coop...
Preprint
Full-text available
Contemporary Artificial Intelligence technologies allow for the employment of Computer Vision to discern good crops from bad, providing a step in the pipeline of selecting healthy fruit from undesirable fruit, such as those which are mouldy or gangrenous. State-of-the-art works in the field report high accuracy results on small datasets (<1000 imag...
Article
Neural networks have been widely used in agent learning architectures; however, learnings for one task might nullify learnings for another. Behavioural plasticity enables humans and animals alike to respond to environmental changes without degrading learned knowledge; this can be achieved by regulating behaviour with neuromodulation—a biological pr...
Article
Full-text available
In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronav...
Conference Paper
Full-text available
Neural networks have been widely used in agent learning architectures; however, learning multiple context-dependent tasks simultaneously or sequentially is problematic when using them. Behavioural plasticity enables humans and animals alike to respond to changes in context and environmental stimuli, without degrading learnt knowledge; this can be a...
Conference Paper
Full-text available
Evolving agents to learn how to solve complex, multi-stage tasks to achieve a goal is a challenging problem. Problems such as the River Crossing Task are used to explore how these agents evolve and what they learn, but it is still often difficult to explain why agents behave in the way they do. We present the Minimal River Crossing (RC-) Task testb...
Article
Full-text available
Systems that pursue their own goals in shared environments can indirectly affect one another in unanticipated ways, such that the actions of other systems can interfere with goal-achievement. As humans have evolved to achieve goals despite interference from others in society, we thus endow socially situated agents with the capacity for social actio...
Conference Paper
Full-text available
Two systems pursuing their own goals in a shared world can interact in ways that are not so explicit-such that the presence of another system alone can interfere with how one is able to achieve its own goals. Drawing inspiration from human psychology and the theory of social action, we propose the notion of employing social action in socially situa...

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Project (1)
Archived project
These papers arose from work completed in my PhD studies from 2017-2021. In this work, I explore the consequences of unintended interactions between entities in a shared environment, and explore ways in which these consequences can be mitigated without extensive knowledge of others.