@conference {10.1145/3377929.3398078,
	title = {Evolutionary Stress Factors for Adaptable Robot {\textquoteright}Personalities{\textquoteright}},
	booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion},
	year = {2020},
	pages = {1387{\textendash}1388},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	abstract = {Many studies on real multi-robot systems are undertaken in laboratory environments, whereas the outside world is unstable and risky. I argue that the phenomenon of phenotypic plasticity provides a bioinspiration framework for making such systems more resilient in the field. Such plasticity can result in consistent individual behavioural differences among group members, which have been described as {\textquoteright}personalities{\textquoteright} in recent years. Personality axes such as shy-bold can be represented by single variables, and may be intuitive in the context of human-robot interaction. However, for plasticity to emerge in artificial evolution, simulated environments need to be more heterogeneous and unpredictable, as in real-world natural history. Behavioral Reaction Norms (BRNs) from developmental biology are a useful concept for the transparent mapping of relevant sensory inputs to robot behavioral change. In this position paper, I identify candidate stress factors in simulated environments for artificial evolution of adaptive BRNs. I also consider some potential benefits and costs of this approach.},
	keywords = {field robotics, personality, phenotypic plasticity, reaction norms},
	isbn = {9781450371278},
	doi = {10.1145/3377929.3398078},
	url = {https://doi.org/10.1145/3377929.3398078},
	author = {Edmund R Hunt}
}
