We introduce Monte Carlo Physarum Machine: a dynamic computational model designed for reconstructing complex transport networks. MCPM extends existing work on agent-based modeling of Physarum polycephalum with a probabilistic formulation, making it suitable for 3D reconstruction and visualization problems. Our motivation is estimating the distribution of the intergalactic medium—the cosmic web , which has so far eluded full spatial mapping. MCPM proves capable of this task, opening up a way towards answering a number of open astrophysical and cosmological questions.

In this work, we propose a framework that derives the configuration of an artificial, compartmentalized, cell-like structure in order to maximize the yield of a desired output reactant given a formal description of the chemistry. The configuration of the structure is then used to compile G-code for 3D printing of a microfluidic platform able to manufacture the aforementioned structure. Furthermore, the compiler output includes a set of pressure profiles to actuate the valves at the input of the microfluidic platform. The work includes an outline of the steps involved in the compilation process and a discussion of the algorithms needed for each step. Finally, we provide formal, declarative languages for the input and output interfaces of each of these steps.

In social species, individuals who form social bonds have been found to live longer, healthier lives. One hypothesised reason for this effect is that social support, mediated by oxytocin, “buffers” responses to stress in a number of ways, and is considered an important process of adaptation that facilitates long-term wellbeing in changing, stressful conditions. Using an artificial life model, we have investigated the role of one hypothesised stress-reducing effect of social support on the survival and social interactions of agents in a small society. We have investigated this effect using different types of social bonds and bond partner combinations across environmentally-challenging conditions. Our results have found that stress reduction through social support benefits the survival of agents with social bonds, and that this effect often extends to the wider society. We have also found that this effect is significantly affected by environmental and social contexts. Our findings suggest that these “social buffering” effects may not be universal, but dependent upon the degree of environmental challenges, the quality of affective relationships and the wider social context.

Robotic swarms rely on local communication between agents to exhibit cooperative emergent behaviours. Local communication is typically implemented with technologies that require dedicated electronics, that can be expensive and difficult to miniaturise or mass-produce. Computational resources are then needed to transform this information into a robot action following a set of rules, further limiting swarm lifetime (battery) and scalability. In this paper, we propose an alternative approach by using the concept of morphological computation (computation through morphology) for local communication in swarms. In such a swarm, local communication is implemented as simple mass-spring-damper systems between agents, instead of electronics. We test this approach in a simple scenario where a swarm has to squeeze through a narrow gap while floating on water. We tested different types of swarms (with different levels of control) and measured their average performance and energy efficiency. We found that by offloading the majority of communication and information processing to the morphology, swarms can exhibit interesting, emergent, cooperative behaviour to solve the given task.

The dynamics of urban systems can be understood from an evolutionary perspective, in some sense extending biological and cultural evolution. Models for systems of cities implementing elementary evolutionary processes remain however to be investigated. We propose here such a model for urban dynamics at the macroscopic scale, in which the diffusion of innovations between cities captures transformation processes (mutations) and transmission processes (diffusion), using two coupled spatial interaction models. Explorations of the model on synthetic systems of cities show the role of spatial interaction and innovation diffusion ranges on measures of diversity and utility, and the existence of intermediate ranges yielding an optimal utility. Multi-objective optimization shows how the model produces a compromize between utility and diversity. This model paves the way towards more elaborated formalizations of urban evolution.

Natural living systems on Earth (i) process information, (ii) metabolize, (iii) self-reproduce and (iv) evolve. These functional properties of life are traditionally associated with the presence of a boundary, metabolism and information-carrying polymers. How can (i)-(iv) be integrated in a chemical system? Our 1-pot chemical system avoids bio-chemistry and is completely artificial. We present progress in this area resulting from experiments on autonomous system boot-up generated during the chemically controlled non-equilibrium assembly of active vesicles. We follow their dynamical evolution with membrane growth and metabolism working in concert and under autonomous chemical control. This is achieved by implementing a PISA (Polymerization Induced Self-Assembly) polymerization and encapsulation scenario, which solves the concentration problem and generates an all-important free-energy gradient. As chemicals (“fuels”) are consumed in the polymerization reaction, energy is dissipated and entropy changes result in morphological changes and joint physicochemical evolution. We monitor the consequences of the copolymer synthesis going on as it proceeds and the resulting evolution of the molecular self-assembly. We find that this transient (or dissipative) self-assembly process leads to vesicles with diameters between 0.5 and 10's of microns. They exhibit several autonomous emergent, life-like, properties including periodic growth and partial collapse, system self-replication, together with homeostasis, competition and phototaxis. We also discuss the extension of the above by running the PISA process with oscillatory chemical reactions which are actually able to compute as a completely autonomous chemical Turing machine and control the generation and time evolution of their entrapping and replicating vesicles. Taken together these results offer insights into chemistry-based artificial life, as well as into prebiotic membrane formation en route to proto-cells and proto-life and the first living systems on the early Earth.

This paper applies reinforcement learning to train a predator to hunt multiple prey, which are able to reproduce, in a 2D simulation. It is shown that, using methods of curriculum learning, long-term reward discounting and stacked observations, a reinforcement-learning-based predator can achieve an economic strategy: Only hunt when there is still prey left to reproduce in order to maintain the population. Hence, purely selfish goals are sufficient to motivate a reinforcement learning agent for long-term planning and keeping a certain balance with its environment by not depleting its resources. While a comparably simple reinforcement learning algorithm achieves such behavior in the present scenario, providing a suitable amount of past and predictive information turns out to be crucial for the training success.

The evolution of social institutions (e.g. institutions of political decision making or joint resource administration) is an important question in the context of understanding of how societies develop and evolve. In principle, social institutions can be conceptualized as abstract games with multiple players and rules about individual decision making and individual and joint outcomes. Here we propose a formal approach for the composition of games (e.g. Prisoner's Dilemma – PD) to model the evolution of social institutions. Following a generalized description of the approach, we describe two examples of application for the composition of PD games. We assess the impact of the composed games on the level of cooperation. We discuss the implications of the proposed approach and how it may help to develop effective models of social institution evolution.

We study creating and breaking symmetry in digitally generated artificial-life-based visual art. Therefore, an artificial swarm-based pattern-making system is used as a test bed. The patterns are generated algorithmically by emulating the collective feeding behavior of sand-bubbler crabs. Our focus is on analyzing concepts and templates for incorporating symmetry and broken symmetry into the creation process of bioinspired art. All four types of two-dimensional symmetry defined by isometric maps are used to create images. Apart from treating geometric symmetry, we also consider color as an object of symmetric transformations. Color symmetry is realized as a color permutation consistent with the isometric maps. Therefore, color permutation groups have been designed which utilize mappings on a color wheel.

Enaction's claim of continuity between life and mind is a bold one. We investigate one aspect of this claim using a glider in the Game of Life as a toy model. Specifically, we study the relationship between theories of glider constitution and glider interaction, demonstrating how a glider's constitution completely determines its interaction graph, but not the particular life that it enacts, which also requires knowledge of the dynamics of its environment.

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.

A standard problem in complex systems science has been to understand how infectious diseases, information, or any other contagion can spread within a system. Simple models of contagions tend to assume random mixing of elements, but real interactions are not random pairwise encounters: they occur within clearly defined higher-order structures. These higher-level structures could represent communities in social systems, cells in organisms or modules in neural networks. For a broader understanding of contagion dynamics in complex networks, we need to embrace higher-order structure, which can itself take many forms such as simplicial complexes or hypergraphs. To accurately describe spreading processes on these higher-order networks and correctly account for the heterogeneity of the underlying structure, we use a set of approximate master equations. This general framework allows us to unveil and characterize important properties of these systems. Here we focus on three of them: the localization of contagions within certain substructures, the bistability of the stationary state and a crossover of the optimal seeding strategies to maximize early spread.

Genetic programming uses biologically-inspired processes of variation and selection to synthesize computer programs that solve problems. Here we investigate the sensitivity of genetic programming to changes in the probability that particular instructions and constants will be chosen for inclusion in randomly generated programs or for introduction by mutation. We find, contrary to conventional wisdom within the field, that genetic programming can be highly sensitive to changes in this source of new genetic material. Additionally, we find that genetic sources can be tuned to significantly improve adaptation across sets of related problems. We study the evolution of solutions to software synthesis problems using untuned genetic sources and sources that have been tuned on the basis of problem statements, human intuition, or prevalence in prior solution programs. We find significant differences in performance across these approaches, and use these lessons to develop a method for tuning genetic sources on the basis of evolved solutions to related problems. This “transfer learning” approach tunes genetic sources nearly as well as humans do, but by means of a fully automated process that can be applied to previously unsolved problems.

Top-down engineering of biomolecular circuits to perform specific computational tasks is notoriously hard and time-consuming. Current circuits have limited complexity and are brittle and application-specific. Here we propose an alternative: we design and test a bottom-up constructed Reservoir Computer (RC) that uses random chemical circuits inspired by DNA strand displacement reactions. This RC has the potential to be implemented easily and trained for various tasks. We describe and simulate it by means of a Chemical Reaction Network (CRN) and evaluate its performance on three computational tasks: the Hamming distance and a short- as well as a long-term memory. Compared with the deoxyribozyme oscillator RC model simulated by Yahiro et al. , our random chemical RC performs 75.5% better for the short-term and 67.2% better for the long-term memory task. Our model requires an 88.5% larger variety of chemical species, but it relies on random chemical circuits, which can be more easily realized and scaled up. Thus, our novel random chemical RC has the potential to simplify the way we build adaptive biomolecular circuits.

The behavioural diversity seen in biological systems is, at the most basic level, driven by interactions between physical materials and their environment. In this context, we investigate the a-life properties of falling paper systems, in which different paper shapes are dropped into free fall and their behaviours observed. These systems have a simple embodiment but highly complex interactions with the environment. Using a synthetic methodology, i.e. understanding by building, we explore how morphology can be used to program certain interactions into the dynamics of a free-falling V-shaped paper. We demonstrate that morphology can encode a stochastic hierarchy of possible behaviours into the system. This hierarchy can be described by a set of conditional switching probabilities and represented in a morphological ‘state machine’. We draw a parallel with developmental processes, showing how these can emerge from interaction with the environment. Next, we demonstrate how Bayesian optimisation can be used to optimise morphology in response to a fitness function, in this case minimizing falling speed. Bayesian optimisation allows us to capture the system stochasticity with minimal sampling. By manipulating non-living raw materials such as paper, we are able to analyse how morphology can be used to control and program interactions with the environment. With this bottom-up approach we ultimately aim to demonstrate principles that turn materials into agents that show non-trivial behaviours comparable to those of living organisms.