@article {Hunt2022,
	title = {What Could Models of Superorganismal Cognition Offer to Embodied AI?},
	journal = {IOP Conference Series: Materials Science and Engineering},
	volume = {1261},
	year = {2022},
	pages = {012023},
	publisher = {IOP Publishing},
	abstract = {Superorganisms such as ant or honeybee colonies exhibit extraordinary collective intelligence, such as an ability to identify and choose the best available nest site in an uncertain world. This collective cognition is inextricably reliant on the embodiment of individual agents, specifically their movement through space. We have recently developed models of superorganismal cognition based on a compelling analogy with techniques in Bayesian statistics, which are likewise aimed at grappling with the uncertainty and incompleteness of real data sources. These models foreground some potential lessons for the design of embodied artificial intelligences, such as robot swarms. For example, the spatial distribution of independently judging agents can convey valuable information, relaxing expectations that regular inter-agent ({\textquoteleft}inter-neuronal{\textquoteright}) communication is necessary for cognition, which points to the potential of minimal field swarm robotics. Meanwhile, the importance of individual heterogeneity to effective and resilient collective cognition in biology suggests great potential in this area for engineering.},
	issn = {1757-899X},
	doi = {10.1088/1757-899X/1261/1/012023},
	url = {https://dx.doi.org/10.1088/1757-899X/1261/1/012023},
	author = {Edmund R Hunt}
}
@article {Hunt2020b,
	title = {The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems},
	journal = {Artificial Life Conference Proceedings},
	volume = {32},
	year = {2020},
	note = {doi: 10.1162/isal_a_00247},
	month = {7},
	pages = {315-323},
	publisher = {MIT Press},
	abstract = {Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such ?swarm? systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a ?multi-armed bandit? problem). House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony: we propose that this is possible via rapid, decentralized estimation of the probability that each choice is best. Viewed this way, their behavioral algorithm can be understood as a statistical method that anticipates recent advances in mathematics. Our nest finding model incorporates insights from approximate Bayesian computation as a model of colony-level behavior; and particle filtering as a model of Temnothorax ?tandem running?. Our framework suggests that the mechanisms of complex collective behavior can sometimes be explained as a spatial enactment of Bayesian inference. It facilitates the generation of quantitative hypotheses regarding individual and collective movement behaviors when collective decisions must be made. It also points to the potential for bioinspired statistical techniques. Finally, it suggests simple mechanisms for collective decision-making in engineered systems, such as robot swarms.},
	doi = {10.1162/isal_a_00247},
	url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00247},
	author = {Edmund R Hunt and Nigel R Franks and Roland J Baddeley}
}
@article {Hunt2020,
	title = {A checklist for safe robot swarms},
	journal = {Nature Machine Intelligence},
	volume = {2},
	year = {2020},
	pages = {420-422},
	abstract = {As robot swarms move from the laboratory to real-world applications, a routine checklist of questions could help ensure their safe operation.},
	issn = {2522-5839},
	doi = {10.1038/s42256-020-0213-2},
	url = {https://doi.org/10.1038/s42256-020-0213-2},
	author = {Edmund R Hunt and Sabine Hauert}
}
@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}
}
@booklet {Hunt2020,
	title = {Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics},
	howpublished = {Frontiers in Robotics and AI},
	volume = {7},
	year = {2020},
	pages = {23},
	abstract = {The real world is highly variable and unpredictable, and so fine-tuned robot controllers that successfully result in group-level {\textquotedblleft}emergence{\textquotedblright} of swarm capabilities indoors may quickly become inadequate outside. One response to unpredictability could be greater robot complexity and cost, but this seems counter to the {\textquotedblleft}swarm philosophy{\textquotedblright} of deploying (very) large numbers of simple agents. Instead, here I argue that bioinspiration in swarm robotics has considerable untapped potential in relation to the phenomenon of phenotypic plasticity: when a genotype can produce a range of distinctive changes in organismal behavior, physiology and morphology in response to different environments. This commonly arises following a natural history of variable conditions; implying the need for more diverse and hazardous simulated environments in offline, pre-deployment optimization of swarms. This will generate{\textemdash}indicate the need for{\textemdash}plasticity. Biological plasticity is sometimes irreversible; yet this characteristic remains relevant in the context of minimal swarms, where robots may become mass-producible. Plasticity can be introduced through the greater use of adaptive threshold-based behaviors; more fundamentally, it can link to emerging technologies such as smart materials, which can adapt form and function to environmental conditions. Moreover, in social animals, individual heterogeneity is increasingly recognized as functional for the group. Phenotypic plasticity can provide meaningful diversity {\textquotedblleft}for free{\textquotedblright} based on early, local sensory experience, contributing toward better collective decision-making and resistance against adversarial agents, for example. Nature has already solved the challenge of resilient self-organisation in the physical realm through phenotypic plasticity: swarm engineers can follow this lead.},
	isbn = {2296-9144},
	doi = {10.3389/frobt.2020.00023},
	url = {https://www.frontiersin.org/article/10.3389/frobt.2020.00023},
	author = {Edmund R Hunt}
}
@article {Hunt2020a,
	title = {SPIDER: a Bioinspired Swarm Algorithm for Adaptive Risk-Taking},
	journal = {Artificial Life Conference Proceedings},
	volume = {32},
	year = {2020},
	note = {doi: 10.1162/isal_a_00279},
	month = {7},
	pages = {44-51},
	publisher = {MIT Press},
	abstract = {Robot swarms can solve tasks that are impossible or too hazardous for single robots. For example, following a nuclear radiation leak, a user may wish to establish a distributed communication chain that partly extends into the most dangerous areas to gather new information. The challenge is to create long chains while maintaining chain connectivity (?connected reach?), where those at the distant end of the chain are more likely to be disconnected. Here we take the concept of dynamic ?boldness? levels from animal behavior (Stegodyphus social spiders) to explore such risky environments in a way that adapts to the size of the group. Boldness is implemented as a continuous variable associated with the risk appetite of individuals to explore regions more distant from a central base. We present a decentralized mechanism for robots, based on the frequency of their social interactions, to adaptively take on ?bold? and ?shy? behaviors. Using this new bioinspired algorithm, which we call SPIDER, swarms are shown to adapt rapidly to the loss of bold individuals by regenerating a suitable shy?bold distribution, with fewer bolder individuals in smaller groups. This allows them to dynamically trade-off the benefits and costs of long chains (information retrieval versus loss of robots) and demonstrates the particular advantage of this approach in hazardous or adversarial environments.},
	doi = {10.1162/isal_a_00279},
	url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00279},
	author = {Edmund R Hunt and George Jenkinson and Michael Wilsher and Carl P Dettmann and Sabine Hauert}
}
@article {Hunt2019,
	title = {Testing the limits of pheromone stigmergy in high-density robot swarms},
	journal = {Royal Society Open Science},
	volume = {6},
	year = {2019},
	pages = {190225},
	doi = {10.1098/rsos.190225},
	author = {Edmund R Hunt and Simon Jones and Sabine Hauert}
}
