Conference Paper

A Study on Psychometric Assessment Data for Autonomous Dementia Detection

Chloe M. Barnes: A Study on Psychometric Assessment Data for Autonomous Dementia Detection. In: Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 383–389, Association for Computing Machinery, Corfu, Greece, 2022.

Abstract

Dementia and Alzheimer’s disease are characterised by cognitive decline, and diagnoses are predicted to rise due to an ageing population. Psychometric assessments are widely used by clinicians to inform the diagnosis of dementia, however these may not be as accurate or accessible for patients with lower levels of literacy or socioeconomic status. This study explores how machine learning models can detect dementia when trained on combinations of attributes from multi-modal datasets containing psychometric and Magnetic Resonance Imaging (MRI) data. When psychometric testing is not available, results show that the Random Forest classifier achieves a balanced accuracy, sensitivity and specificity of 84.75%, 79.10%, and 90.41% respectively before the dataset was standardised, and 84.34%, 78.27%, and 90.41% after – outperforming identical models trained on data from a single psychometric test.


BibTeX (Download)

@inproceedings{10.1145/3529190.3534726,
title = {A Study on Psychometric Assessment Data for Autonomous Dementia Detection},
author = {Chloe M. Barnes},
url = {https://doi.org/10.1145/3529190.3534726},
doi = {10.1145/3529190.3534726},
year  = {2022},
date = {2022-06-29},
urldate = {2022-01-01},
booktitle = {Proceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments},
pages = {383--389},
publisher = {Association for Computing Machinery},
address = {Corfu, Greece},
series = {PETRA '22},
abstract = {Dementia and Alzheimer’s disease are characterised by cognitive decline, and diagnoses are predicted to rise due to an ageing population. Psychometric assessments are widely used by clinicians to inform the diagnosis of dementia, however these may not be as accurate or accessible for patients with lower levels of literacy or socioeconomic status. This study explores how machine learning models can detect dementia when trained on combinations of attributes from multi-modal datasets containing psychometric and Magnetic Resonance Imaging (MRI) data. When psychometric testing is not available, results show that the Random Forest classifier achieves a balanced accuracy, sensitivity and specificity of 84.75%, 79.10%, and 90.41% respectively before the dataset was standardised, and 84.34%, 78.27%, and 90.41% after – outperforming identical models trained on data from a single psychometric test.},
keywords = {AI and Health, Dementia Classification, Machine Learning, Multi-Modal Data},
pubstate = {published},
tppubtype = {inproceedings}
}

Centralised and Decentralised Control of Video Game Agents

Sam G. Robinson, Chloe M. Barnes, Peter R. Lewis: Centralised and Decentralised Control of Video Game Agents. In: Advances in Computational Intelligence Systems. UKCI 2021, pp. 108–120, Springer, 2022.

Abstract

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 ANNs. We find that chasing Ms. Pacman and exploring the map are both harder behaviours to learn for a centralised controller than for decentralised control. Further, both centralised and decentralised approaches produce vastly different behaviours for exploring the map. Novel techniques for comparing networks are also explored.


BibTeX (Download)

@inproceedings{Robinson2021CentralisedAgents,
title = {Centralised and Decentralised Control of Video Game Agents},
author = {Sam G. Robinson and Chloe M. Barnes and Peter R. Lewis},
doi = {10.1007/978-3-030-87094-2_10},
year  = {2022},
date = {2022-09-08},
urldate = {2022-01-01},
booktitle = {Advances in Computational Intelligence Systems. UKCI 2021},
volume = {1409},
pages = {108--120},
publisher = {Springer},
series = {Advances in Intelligent Systems and Computing},
abstract = {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 ANNs. We find that chasing Ms. Pacman and exploring the map are both harder behaviours to learn for a centralised controller than for decentralised control. Further, both centralised and decentralised approaches produce vastly different behaviours for exploring the map. Novel techniques for comparing networks are also explored.},
keywords = {ANNs, Game Playing, Network Comparisons, Neuroevolution},
pubstate = {published},
tppubtype = {inproceedings}
}

Evolving Neuromodulated Controllers in Variable Environments

Chloe M. Barnes, Anikó Ekárt, Kai Olav Ellefsen, Kyrre Glette, Peter R. Lewis, Jim Tørresen: Evolving Neuromodulated Controllers in Variable Environments. In: Proceedings of the IEEE 2nd International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), pp. 164–169, IEEE, 2021.

Abstract

Modern technical systems are increasingly composed of heterogeneous components that are situated in variable environments. In nature, organisms can temporarily adapt their behaviour to novel stimuli with behavioural plasticity; this can be achieved with neuromodulation, a biological process that modulates synaptic activity in the brain. We explore how neuromodulation affects goal-achievement in evolved neural controllers for artificial agents in variable environments. As variability can arise from the actions of others, we show that the benefit of plasticity can increase with variability, as agents can temporarily change their phenotype within their lifetime. Further, we show that cooperation can emerge between plastic agents that cannot perceive one another in highly variable environments.


BibTeX (Download)

@inproceedings{Barnes2021EvolvingEnvironments,
title = {Evolving Neuromodulated Controllers in Variable Environments},
author = {Chloe M. Barnes and Anikó Ekárt and Kai Olav Ellefsen and Kyrre Glette and Peter R. Lewis and Jim Tørresen},
doi = {10.1109/ACSOS52086.2021.00037},
year  = {2021},
date = {2021-09-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the IEEE 2nd International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)},
pages = {164--169},
publisher = {IEEE},
abstract = {Modern technical systems are increasingly composed of heterogeneous components that are situated in variable environments. In nature, organisms can temporarily adapt their behaviour to novel stimuli with behavioural plasticity; this can be achieved with neuromodulation, a biological process that modulates synaptic activity in the brain. We explore how neuromodulation affects goal-achievement in evolved neural controllers for artificial agents in variable environments. As variability can arise from the actions of others, we show that the benefit of plasticity can increase with variability, as agents can temporarily change their phenotype within their lifetime. Further, we show that cooperation can emerge between plastic agents that cannot perceive one another in highly variable environments.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Cooperation, Environmental Variability, Evolutionary Algorithms, Neuroevolution, Neuromodulation, River Crossing},
pubstate = {published},
tppubtype = {inproceedings}
}

Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks

Chloe M. Barnes, Anikó Ekárt, Kai Olav Ellefsen, Kyrre Glette, Peter R. Lewis, Jim Tørresen: Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks. In: Proceedings of the IEEE 1st International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), pp. 129–138, IEEE, 2020.

Abstract

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 achieved by regulating behaviour with neuromodulation - a biological process found in the brain. We demonstrate that modulating activity-propagating signals when evolving neural networks enables agents to learn context-dependent and multi-stage tasks more easily. Further, we show that this benefit is preserved when agents occupy an environment shared with other neuromodulated agents. Additionally we show that neuromodulation helps agents that have evolved alone to adapt to changes in environmental stimuli when they continue to evolve in a shared environment.


BibTeX (Download)

@inproceedings{Barnes2020CoevolutionaryTasks,
title = {Coevolutionary Learning of Neuromodulated Controllers for Multi-Stage and Gamified Tasks},
author = {Chloe M. Barnes and Anikó Ekárt and Kai Olav Ellefsen and Kyrre Glette and Peter R. Lewis and Jim Tørresen},
url = {https://ieeexplore.ieee.org/document/9196458},
doi = {10.1109/ACSOS49614.2020.00034},
year  = {2020},
date = {2020-08-17},
urldate = {2020-01-01},
booktitle = {Proceedings of the IEEE 1st International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)},
pages = {129--138},
publisher = {IEEE},
abstract = {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 achieved by regulating behaviour with neuromodulation - a biological process found in the brain. We demonstrate that modulating activity-propagating signals when evolving neural networks enables agents to learn context-dependent and multi-stage tasks more easily. Further, we show that this benefit is preserved when agents occupy an environment shared with other neuromodulated agents. Additionally we show that neuromodulation helps agents that have evolved alone to adapt to changes in environmental stimuli when they continue to evolve in a shared environment.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Evolutionary Algorithms, Neuroevolution, Neuromodulation, River Crossing},
pubstate = {published},
tppubtype = {inproceedings}
}

A Minimal River Crossing Task to Aid the Explainability of Evolutionary Agents

Abida Ghouri, Chloe M. Barnes, Peter R. Lewis: A Minimal River Crossing Task to Aid the Explainability of Evolutionary Agents. In: ALIFE 2020: The 2020 Conference on Artificial Life, pp. 36–43, 2020.

Abstract

n
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 testbed, designed to reduce the complexity of the original River Crossing Task while keeping its essential components, such that the fundamental learning challenges it presents can be understood in more detail. Specifically to illustrate this, we demonstrate that the RC-environment can be used to investigate the effect that a cost to movement has on agent evolution and learning, and more importantly that the findings obtained as a result can be generalised back to the original River Crossing Task.


BibTeX (Download)

@inproceedings{Ghouri2020AAgents,
title = {A Minimal River Crossing Task to Aid the Explainability of Evolutionary Agents},
author = {Abida Ghouri and Chloe M. Barnes and Peter R. Lewis},
url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00347},
doi = {10.1162/isal_a_00347},
year  = {2020},
date = {2020-07-01},
urldate = {2020-01-01},
booktitle = {ALIFE 2020: The 2020 Conference on Artificial Life},
pages = {36--43},
abstract = {n
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 testbed, designed to reduce the complexity of the original River Crossing Task while keeping its essential components, such that the fundamental learning challenges it presents can be understood in more detail. Specifically to illustrate this, we demonstrate that the RC-environment can be used to investigate the effect that a cost to movement has on agent evolution and learning, and more importantly that the findings obtained as a result can be generalised back to the original River Crossing Task.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Evolutionary Algorithms, Explainability, Neuroevolution, Principled Simplification, River Crossing},
pubstate = {published},
tppubtype = {inproceedings}
}

Social Action in Socially Situated Agents

Chloe M. Barnes, Anikó Ekárt, Peter R. Lewis: Social Action in Socially Situated Agents. In: Proceedings of the IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pp. 97–106, IEEE, 2019.

Abstract

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 situated agents as a means of alleviating interference in interacting systems. Here we demonstrate that these specific issues of behavioural and evolutionary instability caused by the unintended consequences of interactions can be addressed with agents capable of a fusion of goal-rationality and traditional action, resulting in a stable society capable of achieving goals during the course of evolution.


BibTeX (Download)

@inproceedings{Barnes2019saso,
title = {Social Action in Socially Situated Agents},
author = {Chloe M. Barnes and Anikó Ekárt and Peter R. Lewis},
url = {https://ieeexplore.ieee.org/document/8780530},
doi = {10.1109/SASO.2019.00021},
year  = {2019},
date = {2019-06-19},
urldate = {2019-01-01},
booktitle = {Proceedings of the IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO)},
pages = {97--106},
publisher = {IEEE},
abstract = {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 situated agents as a means of alleviating interference in interacting systems. Here we demonstrate that these specific issues of behavioural and evolutionary instability caused by the unintended consequences of interactions can be addressed with agents capable of a fusion of goal-rationality and traditional action, resulting in a stable society capable of achieving goals during the course of evolution.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Evolutionary Algorithms, Neuroevolution, River Crossing, Social Action},
pubstate = {published},
tppubtype = {inproceedings}
}