Controlling swarm dynamics is challenging and has long been an attractive research field because swarms provide a fundamental insight of locally interacting systems’ emergent behaviors. For example, a sheepdog type navigation control has been studied recently, where swarms consist of two different agents: passive sheep and active sheepdogs. In this paper, we focused on the swarm predator system with a swarm that has a number of passive agents and a single active predator agent. Recently, reservoir computing (RC) was introduced as a new way to control swarms. RC offers an easy and analyzable way to find optimal controllers. In this paper, we suggest a new way to read the swarms’ state for controlling swarm predator systems, named relatively ordered state (ROS), where the agents’ IDs are reordered at each time step by relative distances from the predator. The ROS is robust against the swarm’s initial condition’s difference, despite the simpleness and naturalness of the process of the ROS. We found that a swarm within the critical phases of order disorder phase transition like structures can bring out the swarm’s potential to be a reservoir both in open-loop and closed-loop experiments. In this closed-loop control, the predator determines its own movement via the collective dynamics of the swarm like a serpent eating its own tail in the classic “Ouroboros.”

Human intelligence emerged through the process of natural selection and evolution on Earth. We investigate what it would take to re-create this process in silico . While past work has often focused on low-level processes (such as simulating physics or chemistry), we instead take a more targeted approach, aiming to evolve agents that can accumulate open-ended culture and technologies across generations. Towards this, we present JaxLife: an artificial life simulator in which embodied agents, parameterized by deep neural networks, must learn to survive in an expressive world containing programmable systems. First, we describe the environment and show that it can facilitate meaningful turing-complete computation. We then analyze the evolved emergent agents’ behavior, such as rudimentary communication protocols, agriculture, and tool-use. Finally, we investigate how complexity scales with the amount of compute used. We believe JaxLife takes a step towards studying evolved behavior in more open-ended simulations. 1

Agent-based models are widely used in biology to study tissue-scale phenomena by simulating individual cell behaviors, offering insights into cellular biology and serving as predictive tools through computer simulations. However, their development requires effective communication between biologists and modelers, leading to delays. To address this, we propose a novel methodology using Unified Modeling Language (UML) diagrams to enhance communication and involve biologists in the model design process. These diagrams provide clarity and structure, while simulation visualization gathers qualitative feedback for validation. We also introduce a web platform allowing the creation of UML diagrams and automatic code translation for immediate simulation visualization. This article demonstrates our platform’s capability by replicating two models from literature.

This paper investigates the capability of embodied agents to perform a sequential counting task. Drawing inspiration from honeybee studies, we present a minimal numerical cognition task wherein an agent navigates a 1D world marked with landmarks to locate a previously encountered food source. We evolved embodied artificial agents controlled by dynamical recurrent neural networks to be capable of associating a food reward with encountering a number of landmarks sequentially. To eliminate the possibility of the evolved agents relying on distance to locate the target landmark, we varied the positions of the landmarks across trials. Our experiments demonstrate that embodied agents equipped with relatively small neural networks can accurately enumerate and remember up to five landmarks when encountered sequentially. Counter to the intuitive notion that numerical cognition is a complex, higher cortical function, our findings support the idea that numerical discrimination can be achieved in relatively compact neural circuits.

Spatial structure is hypothesized to be an important factor in the origin of life, wherein encapsulated chemical reaction networks came together to form systems capable adaptive complexification via Darwinian evolution. In this work, we use a computational model to investigate how different patterns of environmental connectivity influence the emergence of adaptive processes in simulated systems of self-amplifying networks of interacting chemical reactions (autocatalytic cycles, “ACs”). Specifically, we measured the propensity for adaptive dynamics to emerge in communities with nine distinct patterns of inter-AC interactions, across ten different patterns of environmental connectivity. We found that the pattern of connectivity can dramatically influence the emergence of adaptive processes; however, the effect of any particular spatial pattern varied across systems of ACs. Relative to a well-mixed (fully connected) environment, each spatial structure that we investigated amplified adaptive processes for at least one system of ACs and suppressed adaptive processes for at least one other system. Our findings suggest that there may be no single environment that universally promotes the emergence of adaptive processes in a system of interacting components (e.g., ACs). Instead, the ideal environment for amplifying (or suppressing) adaptive dynamics will depend on the particularities of the system.

The collective perception problem is commonly discussed in swarm robotics, with many proposed solutions. However, there has been less discussion on the impact of faulty agents on the efficacy of these decision making strategies, and few possible solutions to mitigate any negative effects. This paper introduces a decentralised, immuno-inspired ‘check, track, mark’ (CTM) routine, and tests its efficacy when used to mitigate the effect of faulty agents in the collective perception problem. The CTM routine is inspired by macrophages in the human immune system, and their use in preventing pathogens from infecting healthy cells. We test the routine using three previously established decision making strategies, and a model of a detection algorithm with saturating true-positive and false-positive rates. We find that the proposed approach improves the ability of agents to reach an accurate consensus in the presence of faulty agents across all three of the decision making strategies tested, with increases in accuracy between 15–213%. For one strategy, the CTM routine also allows for an accurate consensus to be reached in fewer timesteps, with a median decrease in time to consensus of 29%. Out of the parameters associated with the CTM routine, we find the interval between initial checks to be most significant in affecting the speed and accuracy of the group in reaching consensus.

Coordination and cooperation are crucial features of many natural and artificial systems. Among the many mechanisms that have been proposed to support their emergence, leadership can play an important role. In human and other animal groups, inter-individual differences can lead to the emergence of successful leaders, who assume their role thanks to their physical or cognitive capabilities that grant them some influence over the behavior of their peers. Hence, heterogeneity in a population appears as a key element for successful leaders. Here, we present an evolutionary game theoretic model to study the effect of leadership and heterogeneity on cooperative behavior and examine the relationships between the two. We show that the presence of a leader can promote the evolution of cooperation. Moreover, we find that, when there is the possibility for a leader to emerge in the group, heterogeneity benefits cooperation. In our model, players cooperate when they are more likely to become leaders, and defect otherwise. In other words, strong leaders do not defect, but act as exemplar of prosocial behavior that, when followed, lead to full cooperation.

Artificial Neural Networks have been crowned with tremendous successes in recent years, with ever wider and more complex ranges of applications. However, they, too often, result from a costly human design process relying as much on expertise as on trial and error. While the field of NeuroEvolution provides a complementary view point through emergent, self-designing ANNs, the “black-box” properties of the resulting networks is further magnified. Still, by once more taking inspiration from biology, we may extract meaningful information from ANNs by using similar approaches as those used for biological brains. In this work, we study the emergence and functional allocation of neurons in a light communication task. By having a robot transmit visual information, through vocal channels, we enrich the existing literature with new types of stimuli, namely those related to role (emitter/ receiver). Through Virtual functional Magnetic Resonance Imaging (VfMRI), we observe that evolution only favored specific kind of input-processing modules. Combined with a strong presence of jack-of-alltrades modules, this demonstrates the balancing act between specialization and generalization in Artificial Neural Networks with emergent topologies.

Biological systems exhibit hierarchical and intricate mechanisms that enable self-sustenance and open-ended behavior. This organizational closure is arguably one of life’s hallmarks, and it is facilitated by the widespread utilization of enzymes. Enzymes enhance improbable pathways, enabling the formation of complex structures and functions. Here, we propose a model to characterize artificial enzymes within an artificial “soup” of functions. We contend that these enzymes can emerge from elementary interactions among functions, and they should foster rapid complexity growth, owing to their ability to construct auto-catalitic networks.

Cooperation is essential for both human and artificial life societies, yet understanding how to promote it remains a complex challenge. Indirect reciprocity, where individuals cooperate to maintain a good reputation, is one mechanism to encourage cooperation. To promote stable cooperation, society needs social norms that stipulate how individuals should behave and how they should evaluate others. Previous research has identified a set of effective social norms, called the “leading eight”, for achieving evolutionarily stable cooperation. In this study, we expand on a classical framework in two significant ways. First, we include norms that update the reputations of passive receivers. Second, we introduce stochasticity to social norms. We theoretically derived the necessary and sufficient conditions for evolutionarily stable norms that result in full cooperation within this generalized model. Our findings offer a new perspective on prior research and provide a foundation for future studies in this field.

This paper presents an energy-based approach for simulating virtual creatures, advocating for a shift from traditional monolithic physics engines to a more flexible implementation approach centered on energy minimization and automatic differentiation. By integrating insights from established disciplines alongside emerging concepts such as scale-free cognition, this approach enables a comprehensive modeling of behaviors, where everything from basic physical phenomena, such as inertia and elasticity, to more complex behaviors, such as robust locomotion, can be interpreted as goal-directed behavior.

Cartesian Genetic Programming (CGP) literature repeatedly reports that crossover operators hinder CGP search compared to a 1 + λ strategy based on mutation only. Though there have been efforts in making CGP crossover operators work, the literature is relatively evasive on why the phenomenon is observed at all. This contrasts with what happens in Linear Genetic Programming (LGP), where we know that crossover works well. While both CGP and LGP individuals can be represented as directed acyclic graphs (DAGs), changing a single connection gene in a CGP individual can drastically alter the activeness of nodes in the entire graph, as opposed to LGP where crossover changes are much more beneficial. In this contribution, we demonstrate the phenomenon and show that LGP evolution produces children that are far more similar to their parents than in CGP. This lets us propose that the design of LGP, namely the inclusion of steady-state memory registers and program size regulation, serves to protect highfitness substructures from perturbation in a way that is not provided for in CGP.

Plants are complex organisms, showing collective adaptive behavior. Plant behavior is often defined by shoot growth, yet root systems exhibit equally complex, less visible, behaviors. Roots have to navigate in a particular environment, while optimizing nutrient and water uptake as well as avoiding exposure to harmful elements. In this paper, we introduce an interactive, agent-based simulation model of root growth. It supports the exploration of the interplay of different root models within different environments. To this end, we resort to Swarm Grammars (SGs) which combine the interactivity of spatial agents with the generative perspectives of LSystems. We pursue a point-based representation of the environment due to its versatility with respect to modeling and rendering possibilities. SGs and point-based environments are combined in a simulation that enables interactions between a user, agents and the environment at runtime. We validate the model by recreating and analysing several established root model configurations, and validate the benefits of the interactive simulation by an expert interview.

A common subject: Evolution through a computational lens. Two different communities: on the one hand, artificial life researchers use computational systems to understand emergent evolutionary processes and patterns such as complexity, robustness, evolvability and open-endedness; on the other hand, evolutionary bioinformatics researchers decipher patterns and processes in diverse domains of life on Earth using computational methods based on biological data. Both communities use simulations of living organisms but with different aims, objects, and methods, resulting in disjoint research corpuses. We propose Aevol 4b, an artificial life evolution simulator, and show that the data it produces can be successfully and interestingly processed using bioinformatics methods. This bridges the gap between the two fields and paves the way for fruitful exchanges between artificial life models and bioinformatic analysis methods.

Human culture relies on collective innovation: our ability to continuously explore how existing elements in our environment can be combined to create new ones. Language is hypothesized to play a key role in human culture, driving individual cognitive capacities and shaping communication. Yet the majority of models of collective innovation assign no cognitive capacities or language abilities to agents. Here, we contribute a computational study of collective innovation where agents are Large Language Models (LLMs) that play Little Alchemy 2, a creative video game originally developed for humans that, as we argue, captures useful aspects of innovation landscapes not present in previous test-beds. We, first, study an LLM in isolation and discover that it exhibits both useful skills and crucial limitations. We, then, study groups of LLMs that share information related to their behaviour and focus on the effect of social connectivity on collective performance. In agreement with previous human and computational studies, we observe that groups with dynamic connectivity out-compete fully-connected groups. Our work reveals opportunities and challenges for future studies of collective innovation that are becoming increasingly relevant as Generative Artificial Intelligence algorithms and humans innovate alongside each other.

In this study, we investigated the extent to which Vision Language Models (VLMs) possess sensibilities similar to those of humans by focusing on color impressions, which have a significant impact on the sensory aspects of vision, and sound symbolism, which constitutes linguistic and auditory sensibilities. For the experiments, we newly constructed an evolving image generation system based on the CONRAD algorithm, which evolves images based on human evaluations. Our system can also reflect the evaluations of VLMs in addition to humans. Using this system, we analyzed the sensibilities of VLMs. The experimental results suggested similarities between human and VLM sensibilities in both color impressions and sound symbolism. In sound symbolism, VLMs demonstrated sound-symbolic sensibilities similar to those of humans, even for the pseudo-words we newly generated, yielding intriguing results. These findings suggest that VLM evaluations and feedback may have a certain level of effectiveness in tasks that have previously required human evaluations or annotations related to sensibility.

ALife is primed to address the biggest challenges in astrobiology by simulating systems which capture the most general and fundamental features of living systems. One such challenge is how to detect life outside of the solar system— especially without making strong assumptions about how life would manifest and interact with its planetary environment. Here we explore an ALife model meant to overcome this problem, by focusing on what life may do, rather than what life may be: life can spread between planetary systems (panspermia) and can modify planetary characteristics (terraformation). Our model shows that as life propagates across the galaxy, correlations emerge between planetary characteristics and location, and these correlations can function as a biosignature. This biosignature is agnostic because it is independent of strong assumptions about any particular instantiation of life or planetary characteristic. We demonstrate (and evaluate) a way to prioritize specific planets for further observation—based on their potential for containing life. We consider obstacles that must be overcome to practically implement our approach, including identifying specific ways in which better understanding astrophysical and planetary processes would improve our ability to detect life.

The idea that the Earth system self-regulates itself in a habitable state was proposed in the 1970s by James Lovelock, later on formalized as the Daisyworld model using a two-species system interacting with their environment. The potential for testing this conceptual framework in an experimental way is limited by its scale. To fill this gap, here we propose an explicit test tube-scale implementation for a microbial synthetic Daisyworld using an engineered community where pH as the external, abiotic control parameter. The computational modelling of this system shows robust self-regulation within a broad range of conditions, limited by tipping points, This synthetic Daisyworld allows exploring multiple scenarios of self-regulation that include the role of parasites, fluctuations or biodiversity and can help developing an experimental path to Earth Systems Science.

This paper introduces Alter3, a humanoid robot that demonstrates spontaneous motion generation through the integration of GPT-4, Large Language Model (LLM). This overcomes challenges in applying language models to direct robot control. By translating linguistic descriptions into actions, Alter3 can autonomously perform various tasks. The key aspect of humanoid robots is their ability to mimic human movement and emotions, allowing them to leverage human knowledge from language models. This raises the question of whether Alter3+GPT-4 can develop a “minimal self” with a sense of agency and ownership. This paper introduces mirror self-recognition and rubber hand illusion tests to assess Alter3’s potential for a sense of self. The research suggests that even disembodied language models can develop agency when coupled with a physical robotic platform.

In many situations, communication between agents is a critical component of cooperative multi-agent systems, however, it can be difficult to learn or evolve. In this paper, we investigate a simple way in which the emergence of communication may be facilitated. Namely, we explore the effects of when agents can mimic preexisting, externally generated useful signals. The key idea here is that these signals incentivise listeners to develop positive responses, that can then also be invoked by speakers mimicking those signals. This investigation starts with formalising this problem, and demonstrating that this form of mimicry changes optimisation dynamics and may provide the opportunity to escape non-communicative local optima. We then explore the problem empirically with a simulation in which spatially situated agents must communicate to collect resources. Our results show that both evolutionary optimisation and reinforcement learning may benefit from this intervention.