Conference Paper

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

Authors

Chloe M. Barnes, Anikó Ekárt, Kai Olav Ellefsen, Kyrre Glette, Peter R. Lewis and Jim Tørresen

Conference Details

2020 IEEE 1st International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)

Washington DC, USA (Virtual).

13th-18th July 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.

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

Authors

Abida Ghouri, Chloe M. Barnes and Peter R. Lewis

Conference Details

The 2020 Conference on Artificial Life (ALife).

Montreál, Canada (Virtual).

13th-18th July 2020.


Abstract

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.

Social Action in Socially Situated Agents

Authors

Chloe M. Barnes, Anikó Ekárt and Peter R. Lewis

Conference Details

2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO).

Umeå, Sweden.

16th-20th June 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.