Journal Articles
2.
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
1.
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.
