Inproceedings
2.
Sam G. Robinson; Chloe M. Barnes; Peter R. Lewis
Centralised and Decentralised Control of Video Game Agents Inproceedings
In: Advances in Computational Intelligence Systems. UKCI 2021, pp. 108–120, Springer, 2022.
Abstract | Links | BibTeX | Tags: ANNs, Game Playing, Network Comparisons, Neuroevolution
@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}
}
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.
PhD Theses
1.
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.
