Journal Articles
8.
Chloe M. Barnes; Anikó Ekárt; Kai Olav Ellefsen; Kyrre Glette; Peter R. Lewis; Jim Tørresen
Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments But at the Cost of Volatility Journal Article
In: ACM Transactions on Autonomous Adaptive Systems, vol. 15, no. 4, 2021.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Evolutionary Algorithms, Evolutionary Volatility, Neuroevolution, Neuromodulation, River Crossing
@article{Barnes2021BehaviouralVolatility,
title = {Behavioural Plasticity Can Help Evolving Agents in Dynamic Environments But at the Cost of Volatility},
author = {Chloe M. Barnes and Anikó Ekárt and Kai Olav Ellefsen and Kyrre Glette and Peter R. Lewis and Jim Tørresen},
doi = {10.1145/3487918},
year = {2021},
date = {2021-12-20},
urldate = {2021-01-01},
journal = {ACM Transactions on Autonomous Adaptive Systems},
volume = {15},
number = {4},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {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 process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Evolutionary Algorithms, Evolutionary Volatility, Neuroevolution, Neuromodulation, River Crossing},
pubstate = {published},
tppubtype = {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 process found in the brain. We demonstrate that by modulating activity-propagating signals, neurally trained agents evolving to solve tasks in dynamic environments that are prone to change can expect a significantly higher fitness than non-modulatory agents and also achieve their goals more often. Further, we show that while behavioural plasticity can help agents to achieve goals in these variable environments, this ability to overcome environmental changes with greater success comes at the cost of highly volatile evolution.
7.
Chloe M. Barnes; Abida Ghouri; Peter R. Lewis
Explaining Evolutionary Agent-Based Models via Principled Simplification Journal Article
In: Artificial Life, vol. 27, no. 3, 2021.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Evolutionary Algorithms, Explainability, Neuroevolution, Principled Simplification, River Crossing
@article{Barnes2021ExplainingSimplification,
title = {Explaining Evolutionary Agent-Based Models via Principled Simplification},
author = {Chloe M. Barnes and Abida Ghouri and Peter R. Lewis},
doi = {10.1162/artl_a_00339},
year = {2021},
date = {2021-03-16},
urldate = {2021-01-01},
journal = {Artificial Life},
volume = {27},
number = {3},
publisher = {MIT Press},
abstract = {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 cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Evolutionary Algorithms, Explainability, Neuroevolution, Principled Simplification, River Crossing},
pubstate = {published},
tppubtype = {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 cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid.
Inproceedings
6.
Chloe M. Barnes; Anikó Ekárt; Kai Olav Ellefsen; Kyrre Glette; Peter R. Lewis; Jim Tørresen
Evolving Neuromodulated Controllers in Variable Environments Inproceedings
In: Proceedings of the IEEE 2nd International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), pp. 164–169, IEEE, 2021.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Cooperation, Environmental Variability, Evolutionary Algorithms, Neuroevolution, Neuromodulation, River Crossing
@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}
}
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.
5.
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 Inproceedings
In: Proceedings of the IEEE 1st International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), pp. 129–138, IEEE, 2020.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Evolutionary Algorithms, Neuroevolution, Neuromodulation, River Crossing
@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}
}
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.
4.
Abida Ghouri; Chloe M. Barnes; Peter R. Lewis
A Minimal River Crossing Task to Aid the Explainability of Evolutionary Agents Inproceedings
In: ALIFE 2020: The 2020 Conference on Artificial Life, pp. 36–43, 2020.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Evolutionary Algorithms, Explainability, Neuroevolution, Principled Simplification, River Crossing
@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}
}
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.
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.
3.
Chloe M. Barnes; Anikó Ekárt; Peter R. Lewis
Social Action in Socially Situated Agents Inproceedings
In: Proceedings of the IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), pp. 97–106, IEEE, 2019.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Evolutionary Algorithms, Neuroevolution, River Crossing, Social Action
@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}
}
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.
PhD Theses
2.
Chloe M. Barnes
Interference and Volatility in Evolutionary Agent-Based Systems PhD Thesis
Aston University, 2021.
Abstract | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Cooperation, Environmental Variability, Evolutionary Algorithms, Evolutionary Volatility, Network Comparisons, Neuroevolution, Neuromodulation, River Crossing
@phdthesis{Barnes2021thesis,
title = {Interference and Volatility in Evolutionary Agent-Based Systems},
author = {Chloe M. Barnes},
year = {2021},
date = {2021-09-20},
urldate = {2021-01-01},
school = {Aston University},
abstract = {Agents that exist and pursue individual goals in shared environments can indirectly affect one another in unanticipated ways, such that the actions of others in the environment can interfere with the ability to achieve goals. Despite this, the impact that these unintended interactions and interference can have on agents is not currently well understood. This is problematic as these goal-oriented agents are increasingly situated in complex sociotechnical systems, that are composed of many actors that are heterogeneous in nature. The primary aim of this thesis is to explore the effect that indirect interference from others has on evolution and goal-achieving behaviour in agent-based systems. More specifically, this is investigated in the context of agents that do not possess the ability to perceive or learn about others within the environment, as information about others may not be readily available at runtime, or there may be a distinct lack of capacity to obtain such information. By conducting three experimental studies, it is established that evolutionary volatility is a consequence of indirect interactions between goal-oriented agents in a shared environment, and that these consequences can be mitigated by designing more socially-sensitive agents. Specifically, agents that employ social action are demonstrated to reduce the evolutionary volatility experienced by goal-oriented agents, without aecting the tness received. Additionally, behavioural plasticity achieved via neuromodulation is shown to allow coexisting agents to achieve their goals more often with less evolutionary volatility in highly variable environments. While sufficient approaches to mitigate interference include learning about or modelling others, or for agents to be explicitly designed to identify interference to mitigate its consequences, this thesis demonstrates that these are not necessary. Instead, more socially-sensitive agents are shown to be capable of achieving their goals and mitigating interference without this knowledge of others, simply by shifting the focus from goal-oriented actions to more socially-oriented behaviour.},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Cooperation, Environmental Variability, Evolutionary Algorithms, Evolutionary Volatility, Network Comparisons, Neuroevolution, Neuromodulation, River Crossing},
pubstate = {published},
tppubtype = {phdthesis}
}
Agents that exist and pursue individual goals in shared environments can indirectly affect one another in unanticipated ways, such that the actions of others in the environment can interfere with the ability to achieve goals. Despite this, the impact that these unintended interactions and interference can have on agents is not currently well understood. This is problematic as these goal-oriented agents are increasingly situated in complex sociotechnical systems, that are composed of many actors that are heterogeneous in nature. The primary aim of this thesis is to explore the effect that indirect interference from others has on evolution and goal-achieving behaviour in agent-based systems. More specifically, this is investigated in the context of agents that do not possess the ability to perceive or learn about others within the environment, as information about others may not be readily available at runtime, or there may be a distinct lack of capacity to obtain such information. By conducting three experimental studies, it is established that evolutionary volatility is a consequence of indirect interactions between goal-oriented agents in a shared environment, and that these consequences can be mitigated by designing more socially-sensitive agents. Specifically, agents that employ social action are demonstrated to reduce the evolutionary volatility experienced by goal-oriented agents, without aecting the tness received. Additionally, behavioural plasticity achieved via neuromodulation is shown to allow coexisting agents to achieve their goals more often with less evolutionary volatility in highly variable environments. While sufficient approaches to mitigate interference include learning about or modelling others, or for agents to be explicitly designed to identify interference to mitigate its consequences, this thesis demonstrates that these are not necessary. Instead, more socially-sensitive agents are shown to be capable of achieving their goals and mitigating interference without this knowledge of others, simply by shifting the focus from goal-oriented actions to more socially-oriented behaviour.
Presentations
1.
Chloe M. Barnes; Anikó Ekárt; Kai Olav Ellefsen; Kyrre Glette; Peter R. Lewis; Jim Tørresen
Overcoming Dynamicity with Plasticity: Neuromodulation for Lifelike Systems Presentation
LIFELIKE Systems Workshop, 2022, 20.07.2022.
Abstract | Links | BibTeX | Tags: Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Environmental Variability, Evolutionary Algorithms, Evolutionary Volatility, Neuroevolution, Neuromodulation, River Crossing
@misc{Barnes2022OvercomingSystems,
title = {Overcoming Dynamicity with Plasticity: Neuromodulation for Lifelike Systems},
author = {Chloe M. Barnes and Anikó Ekárt and Kai Olav Ellefsen and Kyrre Glette and Peter R. Lewis and Jim Tørresen},
url = {https://www.organic-computing.de/wp-content/uploads/2022/08/LIFELIKE2022_Barnes-et-al_Neuromodulation.pdf},
year = {2022},
date = {2022-07-20},
urldate = {2022-01-01},
abstract = {Natural beings are often situated in dynamic and unpredictable environments, and have evolved to use mechanisms such as neuromodulation -- the ability to change behaviour via changes to synaptic activity in the brain -- to adapt their behaviour over time to survive. The ability to change behaviour in this way is referred to as behavioural plasticity'. In this extended abstract, we summarise the findings from an exploration of how plasticity can affect how artificial agents evolve when solving tasks of different complexity, and when evolving in dynamic and unpredictable environments.},
howpublished = {LIFELIKE Systems Workshop, 2022},
keywords = {Agent-Based Systems, ANNs, Artificial Life, Behavioural Plasticity, Environmental Variability, Evolutionary Algorithms, Evolutionary Volatility, Neuroevolution, Neuromodulation, River Crossing},
pubstate = {published},
tppubtype = {presentation}
}
Natural beings are often situated in dynamic and unpredictable environments, and have evolved to use mechanisms such as neuromodulation -- the ability to change behaviour via changes to synaptic activity in the brain -- to adapt their behaviour over time to survive. The ability to change behaviour in this way is referred to as behavioural plasticity'. In this extended abstract, we summarise the findings from an exploration of how plasticity can affect how artificial agents evolve when solving tasks of different complexity, and when evolving in dynamic and unpredictable environments.
