Unconventional computing seeks to develop new means of acting on and interpreting the world. These emerge when new tools and computational substrates are built or discovered, or when existing artifacts are deployed in novel ways. Prior work designed sheets of vibrating particles to achieve mechanical polycomputation, wherein multiple logical operations were physically executed by the same parts at the same time. This works by exploiting the vibrational superposition of particles induced by external drives acting at multiple frequencies. In this paper, we introduce an idea called refractive computation, in which a sufficiently high density of polycomputed logic gates results in parallelized computations across driving frequencies. Parallelized logic gates are split across external drive frequencies in a single simulation, and emerge in the course of polycomputing sequential logic gates.

Robots have long been proposed as a solution for jobs that are difficult for humans, but their construction from nonrenewable and pollutant-causing materials presents a problem. The field of bio-robotics was developed, in part, to address this issue. In previous bio-robotic systems, such as Xenobots, AI-generated morphologies have been used to engineer desired behaviors in individual robots. However, this approach cannot be applied to biobots that are mass-fabricated as this limits our ability to control the behaviors of individual bots. While mass fabrication could have significant implications in the development of scalable biobot technologies for use in real-world applications, developing a reliable method to control their behavior remains a significant challenge. In this paper, we use evolutionary algorithms to create biobot swarm compositions that explore environments with varying obstacles efficiently at several scales. We demonstrate here that, while we cannot control the behavior of individual biobots, carefully selected swarm compositions can lead to desired behavior outcomes. This work thus provides one potential option for realizing biotechnology at scale, where mass-produced biobots must be filtered and combined appropriately.

Endosymbiosis, symbiosis in which one symbiont lives inside another, is woven throughout the history of life and the story of its evolution. From the mitochondrion residing in almost every eukaryotic cell to the gut microbiome found in every human, endosymbiosis is a cornerstone of the biological processes that sustain life on Earth. While endosym-biosis is ubiquitous, many questions about its origins remain shrouded in mystery; one question in particular regards the general conditions and possible trajectories for its evolution. Modern science has hypothesized two possible pathways for the evolution of mutualistic endosymbiosis: one where an obligate antagonism is co-opted into an obligate mutualism (Co-Opted Antagonism Hypothesis), and one where a facultative mutualism evolves into an obligate mutualism (Black Queen Hypothesis). We investigated the viability of these pathways under different environmental conditions by expanding on the evolutionary agent-based system Symbulation. Specifically, we considered the impact of ectosymbiosis on de novo evolution of obligate mutualistic endosymbiosis. We found that introducing a facultative ectosymbiotic state allows endosym-biosis to evolve in a more diverse set of environmental conditions, while also decreasing the evolution of endosymbiosis in conditions where it can evolve independently.