Many lifeforms are found in patches that other lifeforms forage for and consume. Here we explore how the patchiness of the former and cognition of the latter may emerge through mutual interaction in an agent-based model. We use a simple 2D grid world consisting of two types of agents—plants (prey) and animals (predators). Across three experiments, we investigate how cognition of animals influences patchiness of plants and evolves in response to it. Here, cognition is a probabilistic model with two parameters, one for distance of perception and the other for determinacy versus stochasticity of movement. We found that plant patchiness emerged alongside the evolution of animal cognition. In addition, greater distance of perception reduced patchiness, while greater determinacy of movement increased patchiness. Conversely, greater patchiness of plants led animals to evolve perception across greater distances but also led to evolution of less deterministic foraging. Environmental patchiness and foraging cognition thus appeared to mutually create a stable dynamic interaction leading to a self-regulating system.

Quality-Diversity optimisation algorithms enable the evolution of collections of both high-performing and diverse solutions. These collections offer the possibility to quickly adapt and switch from one solution to another in case it is not working as expected. It therefore finds many applications in real-world domain problems such as robotic control. However, QD algorithms, like most optimisation algorithms, are very sensitive to uncertainty on the fitness function, but also on the behavioural descriptors. Yet, such uncertainties are frequent in real-world applications. Few works have explored this issue in the specific case of QD algorithms, and inspired by the literature in Evolutionary Computation, mainly focus on using sampling to approximate the ”true” value of the performances of a solution. However, sampling approaches require a high number of evaluations, which in many applications such as robotics, can quickly become impractical. In this work, we propose Deep-Grid MAP-Elites, a variant of the MAP-Elites algorithm that uses an archive of similar previously encountered solutions to approximate the performance of a solution. We compare our approach to previously explored ones on three noisy tasks: a standard optimisation task, the control of a redundant arm and a simulated Hexapod robot. The experimental results show that this simple approach is significantly more resilient to noise on the behavioural descriptors, while achieving competitive performances in terms of fitness optimisation, and being more sample-efficient than other existing approaches.

This paper addresses the problem of learning cooperative strategies in swarm robotics. We are interested in heterogeneous swarms, in which each robot optimizes its individual gain. For some tasks, the problem is that the optimal strategy requires to cooperate and may be counter-selected in favor of a more stable but less efficient selfish strategy. To solve this problem, we introduce a mechanism of partner choice, which conditions of operation are learned. This mechanism proves surprisingly efficient, when the swarm size is large, and the duration of interactions is long. Beyond evolutionary swarm robotics, the results we present are relevant for other distributed on-line learning methods for robotics, and as a possible extension of existing evolutionary biology and social learning models.

We report experimental extensions of Lenia, a continuous cellular automata family capable of producing lifelike self-organizing autonomous patterns. The rule of Lenia was generalized into higher dimensions, multiple kernels, and multiple channels. The final architecture approaches what can be seen as a recurrent convolutional neural network. Using semiautomatic search e.g. genetic algorithm, we discovered new phenomena like polyhedral symmetries, individuality, self-replication, emission, growth by ingestion, and saw the emergence of “virtual eukaryotes” that possess internal division of labor and type differentiation. We discuss the results in the contexts of biology, artificial life, and artificial intelligence.

Acoustic ecologist Bernie Krause hypothesized that rich soundscapes in mature ecosystems are generated by sound communication between different species with differentiating acoustic niches. This hypothesis, called the acoustic niche hypothesis, proposes that in a mature ecosystem, the singing of a species occupies a unique bandwidth in frequency and shifts in time to avoid competition, thus making the communication efficient. We hypothesize that selective pressure on communication complexity is required for differentiating and filling acoustic niches by a limited number of species, in addition to selective pressures on communication efficiency. To test this hypothesis, we built an evolutionary model where agents can emit complex sounds. Our simulations with the model demonstrate that selective pressure on communication efficiency and complexity leads to an evolution in spectral differentiation with a limited number of species filling the acoustic niche. This is the first demonstration of acoustic niche differentiation using an artificial life model with complex-sounding agents. We also propose multi-timescale complexity measurement, extending the Jensen–Shannon complexity using multi-scale permutation entropy. We analyze the evolved soundscape in the simulations using this measure. The result shows that multi-timescale complexity in soundscape evolved, suggesting that evolving niche differentiation leads to ecological complexity. We implement the extended model in real space and demonstrate that the system can adaptively generate sounds, differentiating acoustic niches with environmental sounds.

A challenge in evolutionary robotics is the in parallel adaptation of morphologies and controllers. Here, we considered encoding methods for morphogenesis of 2D virtual creatures that can be created from directed trees. Using an evolutionary algorithm, we optimized locomotion in these virtual creatures and compared a direct encoding, an L-System, and two types of encodings that produce neural networks—a Compositional Pattern Producing Network (CPPN) and a Cellular Encoding (CE). We evaluated these encodings based on performance and diversification, and we introduced an OpenAI gym environment as a computationally inexpensive benchmark for exploring morphological evolution. The direct encoding and L-System generated more fit solutions compared to the network strategies. Considering morphological diversity, the direct encoding finds solutions more locally in the morphological search space, the L-System made larger jumps across this search space, and both network approaches also make larger jumps though find fewer solutions in this space. With these results we show how encodings exhibit different characteristics as developmental approaches. Since the genotype-phenotype mapping plays a major role in evolutionary robotics, further modifications using more complex tasks and environments can lead to a better understanding of morphogenesis and thereby improve how morphologies and controllers of robots are evolved.

The dynamics of an artificial tumor-immune – ecosystem after simulated radiation therapy (RT) was investigated. The system is represented by a model for a tumor – host-tissue system including repopulation, mutation, competition and interaction with antibodies and a perceptron used for antigen pattern recognition. The perceptron response governs the generation of antibodies. The system exhibit interesting dynamic aspects: A special focus of the presented work lies on the observed separation of the perceptron weights for tumor – and host tissue, After RT application, the weights for host tissue can evolve into negative values whereas tumor-related perceptron weights remain positive. The negative perceptron weights indicate an immune-suppressive effect after RT which is related to the host tissue. The applicability of the presented system to clinical treatment optimization is not possible and may remain strongly limited when refined. The matching with a real-world tumor-immune-ecosystem (in patient) is questionable and the chosen approach may be too simplistic. However, the idea of an immune system considered as a trainable perceptron offers new hypothesis for novel approaches to anti-cancer treatments, treatments of infectious diseases or even vaccination.

In this paper we present a Minimal Cognitive Agent model of a joint action task. Pairs of agents realized as Continuous Time Recurrent Neural Networks are submitted to artificial evolution in the context of a task taken from psychological literature. In this task the agents are required to coordinate their complementary actions in order to jointly control the movement of a tracker and successfully follow a continuously moving target. It has been suggested that such a task requires a more complex type of cognitive mechanism than the types of processes postulated by the proponents of Embodied Embedded Cognition approach. Specifically, it might possibly require that the agents “co-represent” each other's contributions to the common behavior. Our results show that simple agents with no such built-in co-representation mechanism are able to evolve a solution to the task. However, we also find emergent neural activity patterns that are consistent with it. In what sense these patterns can be said to be truly representational requires further study.

An explanatory model for the emergence of evolvable units must display emerging structures that (1) preserve themselves in time (2) self-reproduce and (3) tolerate a certain amount of variation when reproducing. To tackle this challenge, here we introduce Combinatory Chemistry, an Algorithmic Artificial Chemistry based on a minimalistic computational paradigm named Combinatory Logic. The dynamics of this system comprise very few rules, it is initialized with an elementary tabula rasa state, and features conservation laws replicating natural resource constraints. Our experiments show that a single run of this dynamical system with no external intervention discovers a wide range of emergent patterns. All these structures rely on acquiring basic constituents from the environment and decomposing them in a process that is remarkably similar to biological metabolisms. These patterns include autopoietic structures that maintain their organisation, recursive ones that grow in linear chains or binary-branching trees, and most notably, patterns able to reproduce themselves, duplicating their number at each generation.

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.

We describe reaction-diffusion rules for template-driven polymerization of nucleic acid sequences. Simulations of the model reproduce experimentally determined RNA structure and demonstrate self-replication. A C++ implementation is available at https://github.com/evoldoers/carnaval.

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.

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.

Current success of Artificial Intelligence (particularly in the application of Deep Learning techniques) is bringing some of its methods closer to Artificial Life and re-opening old questions, social fears and envisioned applications. The concept of autonomy has long guided research and progress in Artificial Life. We explore how this concept can contribute to evaluate the autonomy of contemporary AI systems.

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.

Central to the origin of life is the question how a chemical system transitioned from interacting molecules to an entity with the capacity for self-replication, diversification and adaptive evolution. Here, we study a chemical system that is comprised of macrocycles that have been shown to spontaneously give rise to self-replicating entities. By combining experimental and theoretical approaches, we strive to understand the evolutionary potential of this system. In particular, we apply eco-evolutionary reasoning to investigate whether and when this system of chemical replicators can diversify. Here, we report first results of a simplified stochastic chemical reaction model that is parameterized on the basis of experimental data. The model considers the competition of two replicators that do not interact directly but need similar building blocks for their growth and reproduction. Interestingly, the replicator that emerges first is being overtaken by the later one. By means of stochastic simulations, we will explore how the competitive ability of a replicator is determined by its chemical characteristics, and under which conditions replicators can coexist. The results will subsequently inform the design of future experiments.

In light of conceptual difficulties with past and current definitions of life, we present a novel characterisation of the living state based on four pillars: thermodynamic dissipation, autocatalysis, homeostasis and learning. We clarify forms of life by introducing the term ‘lyfe’ to describe any system that performs all four fundamental processes, while ‘life' refers only to living systems as we know them on Earth. We note that many non-living structures exhibit subsets of these properties, and we refer to such systems as ‘sublyfe’. Finally, we review exotic lyfeforms that satisfy the four pillars but differ from lifeforms in distinct ways. We suggest a possible form of lyfe that transduces kinetic energy into its metabolism, a so-called mechanotroph.