Just how much can a pheromone-enabled swarm do? Motivated by robotic construction, we set out to show that a swarm of computationally simple ants, communicating only via pheromones, can in fact perform classic compass-straightedge geometry, and thus can make many shapes and perform many nontrivial geometric tasks. The ants do not need speciallydesigned stigmergic building materials, a prepared environment, local or global direct communication facilities (such as radio or line-of-sight signaling), or any localization beyond initial starting points for drawing. We describe the proof of concept in replicable detail. We then note that its accuracy and efficiency can be greatly improved through augmentation with a simple embeddable broadcast mechanism.
Memory is an essential component of intelligence as it enables an individual to make informed decisions based on past experiences. In the context of biological systems, however, what selective conditions promote the evolution of memory? Given that reliable memory is likely to be associated with costs, how much is it actually worth in different contexts? We use a genetic algorithm to measure the evolutionary importance of memory in the context of the Iterated Prisoner's Dilemma, a game in which players receive a short-term gain for defection, but may obtain greater long-term benefits with cooperation. However, cooperation requires trust; cooperating when an opponent defects is the worst possible outcome. Memory allows a player to recall an opponent's previous actions to determine how trustworthy that opponent is. While a player can earn a high payout by defecting, it will likely lose the trust of an opponent with memory, yielding a lower long-term payout. We determined the value of memory in the Iterated Prisoner's Dilemma under various conditions. When memory is costly, players reduce their available memory and use short-term greedy strategies, such as "Always Defect". Alternatively, when memory is inexpensive, players use well-known cooperative strategies, such as "Tit-for-Tat". Our findings indicate that organisms playing against a static opponent evolve memory as expected. However, memory is much more challenging to evolve in coevolutionary scenarios where its value is uneven.
Because the kind of open-ended complexity explosion seen on Earth remains beyond the observed dynamics of current artificial life worlds, it has become critical to isolate and investigate specific factors that may contribute to open-endedness. This paper focuses on one such factor that has previously received little attention in research on open-endedness: the minimal criterion (MC) for reproduction. Originally proposed as an enhancement to novelty search, the MC is in effect a different abstraction of evolution than the more conventional competition-focused fitness-based paradigm, instead focusing on the minimal task that must be completed for an organism to be allowed to produce offspring. The MC is interesting for studying open-endedness because in principle its strictness (i.e. how hard it is to satisfy) can be varied on a continuum to observe its effects. While in many artificial life worlds the MC strictness is implicit and therefore difficult to vary systematically, in the previously-introduced Chromaria world, the MC is designed to be set explicitly by the experimenter, making possible the systematic study of different levels of MC strictness in this paper. The main result, supported by visual, quantitative, and qualitative observations, is that the strictness of the MC can profoundly affect open-ended dynamics, ultimately deciding between complete stagnation (both with extreme strictness or complete relaxation) and orderly divergence. This result offers a lesson of particular importance to worlds whose MCs are not explicit by exposing an area of sensitivity within open-ended systems that is easy to overlook because of its implicit nature.
Models of robust human-human coordination can guide the design of adaptive and responsive human-robot systems. Here we test an artificial agent that embodies low-dimensional nonlinear dynamic equations derived from human behavior while completing a two-agent herding task, where the goal is to contain reactive spheres to the center of a target region. The model was able to complete the task alongside human novices in a virtual version of the experimental setup used in Nalepka and colleagues (submitted). Not only did the model lead participants to successful performance, but also 12 out of 18 participants reported that they believed their partner was a human participant in another room. The model was therefore able to capture the complex social behavior that defined robust task success in terms of lower dimensional dynamical equations that characterizes the emergent behavioral dynamics of embedded multiagent behavior.
Written responses can provide a wealth of data in understanding student reasoning on a topic. Yet they are time- and labor-intensive to score, requiring many instructors to forego them except as limited parts of summative assessments at the end of a unit or course. Recent developments in Machine Learning (ML) have produced computational methods of scoring written responses for the presence or absence of specific concepts. Here, we compare the scores from one particular ML program - EvoGrader - to human scoring of responses to structurally- and content-similar questions that are distinct from the ones the program was trained on. We find that there is substantial inter-rater reliability between the human and ML scoring. However, sufficient systematic differences remain between the human and ML scoring that we advise only using the ML scoring for formative, rather than summative, assessment of student reasoning.
Novelty search is a powerful biologically motivated method for discovering successful behaviors especially in deceptive domains, like those in artificial life. This paper extends the biological motivation further by distributing novelty search to run in parallel in multiple islands, with periodic migration among them. In this manner, it is possible to scale novelty search to larger populations and more diverse runs, and also to harness available computing power better. A second extension is to improve novelty searchs ability to solve practical problems by biasing the migration and elitism towards higher fitness. The resulting method, DANS, is shown to find better solutions much faster than pure single-population novelty search, making it a promising candidate for solving deceptive design problems in the real world.
We present an interactive, agent-based, multi-scale 3D model of a colony of E. coli bacteria. We simulate chemical diffusion on an agar plate which is inhabited by a colony of bacterial cells. The cells interact with a discrete grid that models diffusion of attractants and repellents, to which the cells react. For each bacterium, we simulate its chemotactic behaviour, making a cell either follow a gradient or tumble. Cell propulsion is determined by the spinning direction of the motors that drive its flagella. In an agent-based model, we have implemented the molecular elements that comprise the two key chemotactic pathways of excitation and adaptation, which, in turn, regulate the motors and influence a cells movement through the agar medium. We show four interconnected model layers that capture the biological processes from the colony layer down to the level of interacting molecules.
Living and life-like systems vary in viability. They are alive or dead, healthy or unhealthy, getting better or worse, or dying. Despite the ease of applying these descriptions informally, there do not yet exist general methods for richly quantifying viability or health in such systems —even when every aspect of the system is available for experimental variation and measurement. Nevertheless, for a given system of interest, it is sometimes possible to distinguish between states where the system will persist for the foreseeable future (there are termed viable states) and those where it will not. This is perhaps the most basic, binary classification of states in terms of viability and it can be used to identify different regions in ‘viability space’ (see Figure 1 and Barandiaran and Egbert, 2013). An improved measure would make it possible to not just categorize systems but to compare the relative viability of two states that are in the same category, i. e. that are both expected to persist or both expected to die. This type of measure would make it possible to identify whether a system is becoming more or less viable, or to evaluate the influence of a given external perturbation upon viability, thereby enhancing our ability to understand and influence the viability of complex life-like systems.
Due to the replacement of natural flora and fauna with urban environments, a significant part of the earth's organisms that function as primary consumers have been dispelled. To compensate for the reduction in the amount of primary consumers, robotic systems that mimic plant-like organisms are interesting to mimic for their potential functional and aesthetic value in urban environments. To investigate how to utilize plant developmental strategies in order to engender urban artificial plants, we built a simple evolutionary model that applies an L-System based grammar as an abstraction of plant development. In the presented experiments, phytomorphologies (plant morphologies) are iteratively constructed using a context sensitive L-System. The genomic representation of the L-System is subject to mutation by an evolutionary algorithm. These mutations thus alter the developmental rules of these phytomorphologies. We compare the differences between the light absorption of evolving virtual plants that remain static during their life and virtual plants that possess the possibility to move joints that link the separate parts of the virtual plants. Our results show that our evolutionary algorithm did not exploit potential beneficial joint actuation, instead, mostly static structures evolved. The results of our evolving L-System show that it is able to create various phytomorphologies, albeit that the results are preliminary and will be more thoroughly investigated in the future.
A population-based simulation framework is presented that allows a principled approach for exploring gender inequalities in professional hierarchies such as universities or businesses, and how they might emerge, evolve and be rectified. Results from a representative range of cases involving gender-based discrimination and intrinsic gender-based ability differences are presented to demonstrate the power of the approach. Such artificial life simulations will hopefully inspire and facilitate better approaches for dealing with these issues in real life.
Measurements of coordinated motion in flocks are necessary to evaluate their performance. In this work, a set of quantitative metrics to evaluate the performance of the spatial features exhibited by flocks are introduced and applied to the well-known boids of Reynolds. Our metrics are based on quantitative indicators that have been used to evaluate fish schools. These indicators are revisited and extended as a set of three new metrics that can be used to evaluate and design flocks.
Endothelial cells (ECs) exhibit dramatic plasticity of form at the single- and collective-cell level during new vessel growth, adult vascular homeostasis, and pathology. Understanding how, when, and why individual ECs coordinate decisions to change shape, in relation to the myriad of dynamic environmental signals, is key to understanding normal and pathological blood vessel behavior. However, this is a complex spatial and temporal problem. In this review we show that the multidisciplinary field of Adaptive Systems offers a refreshing perspective, common biological language, and straightforward toolkit that cell biologists can use to untangle the complexity of dynamic, morphogenetic systems.
Artificial life is concerned with understanding the dynamics of human societies. A defining feature of any society is its institutions. However, defining exactly what an institution is has proven difficult, with authors often talking past each other. This article presents a dynamic model of institutions, which views them as political game forms that generate the rules of a group's economic interactions. Unlike most prior work, the framework presented here allows for the construction of explicit models of the evolution of institutional rules. It takes account of the fact that group members are likely to try to create rules that benefit themselves. Following from this, it allows us to determine the conditions under which self interested individuals will create institutional rules that support cooperation for example, that prevent a tragedy of the commons. The article finishes with an example of how a model of the evolution of institutional rewards and punishments for promoting cooperation can be created. It is intended that this framework will allow artificial life researchers to examine how human groups can themselves create conditions for cooperation. This will help provide a better understanding of historical human social evolution, and facilitate the resolution of pressing societal social dilemmas.
This research presents computational models to represent visual navigation mechanisms which guide pigeons (Columba Livia) during flight. A 3D graphics computer simulator was developed to model autonomous flight in virtual pigeons. The aim was to investigate the role of (i) visual landmarks (ii) flocking with other pigeons and (iii) image familiarity in pigeon navigation. A recursive processing algorithm enabled landmarks and other pigeons to be located, identified and counted. Image processing could form a feasible mechanism for autonomous visual navigation by identification of familiar route headings. This could be used in autonomous flying drones or flight simulators.
In both social systems and ecosystems there is a need to resolve potential conflicts between the interests of individuals and the collective interest of the community. The collective interests need to survive the turbulent dynamics of social and ecological interactions. To see how different systems with different sets of interactions have varying degrees of robustness, we need to look at their different contingent histories. We analyse abstract Artificial Life models of such systems, and note that some prominent examples rely on explicitly a-historical frameworks; we point out where analyses that ignore a contingent historical context can be fatally flawed. Real life studies highlight the role of history, and Artificial Life studies should do likewise.
Software-based artficial life will increase the robustness, and enable vastly increased size, of computing systems. To enhance human potential and protect individual liberty in future society-scale systems, the boundary between 'private' and 'public' digital spaces-known in telephone networks as a demarcation point or "demarc"-should be set so that a significant amount of physical computing machinery can be counted as fundamentally personal, for assigning rights and responsibilities. To that end, this note offers a principle called the carried network demarc: The machines that you routinely carry under your own power, and their contents and interactions, should be considered part of your body as a matter of law and social norm. Such machines today may be as prosaic as a watch, pacemaker, or cellphone, but in the future you may regularly carry machines inhabited by multitudes of beneficial alife creatures-akin to the bacterial microbiomes that surround and perfuse our biological bodies-that would likewise be considered you and yours in both their physical and computational aspects. The author solicits input from others with expertise bearing on this topic.
With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with.
Inspired by the self-organization of growing embryos and coordinated movement of multicellular assemblies such as the slime mold Dictyostelium, where each cell is controlled by the same controller (a DNA-encoded gene regulatory network), we evolve distributed gait control mechanisms for soft-bodied animats. The animats are made of compressible material, with each body region capable of independent actuation, controlled by a cell at its center. Each animat consists of hundreds of cells uniformly distributed throughout the body, each sharing the same artificial gene regulatory network and aware of the state of their local neighborhood. We found that one of the most common actuation patterns that emerged relied on cells synchronizing their oscillations in order to produce a rotating, spiral wave spanning throughout the body. We found this type of mechanism to emerge for a wide range of animat morphologies as well as in very different types of initial conditions. We investigate how the evolved controllers produce the pattern through local feedbacks and evaluate spiral stability when imperfect, noisy cells are used.
Microscopic crowd simulation usually uses ad-hoc models. While these have been proven to be useful, they are difficult to calibrate and do not always reflect real behaviour. For this reason we propose a machine learning approach using neural networks. The main contribution of the project is a first exploration of prediction of agent trajectories using two specific types of neural networks, Support Vector Machine (SVM) and Spiking Neural Networks (SNN).
Niche construction is a process in which organisms modify the selection pressure on themselves and others through their ecological activities. Various evolutionary models of effects of niche construction on evolution have revealed that they bring about unexpected evolutionary scenarios. However, little is still known about how niche-constructing behaviors of complex physical structures (such as nest-building) can emerge through the course of evolution, even though it is one of the most ubiquitous and significant niche-constructing behaviors. Our purpose is to obtain knowledge of the emergence and evolution of physically-grounded niche construction and the effect of its ecological inheritance on evolution. We construct an evolutionary model in which a virtual organism has to arrive at a goal by constructing a physical niche composed of objects in a physically simulated environment. In particular, we focus on effects of the degree of ecological inheritance, which is represented as a weathering probability of ecologically inherited objects from a parent to its offspring. We show that it has a nonlinear effect on the adaptivity of the population. In the case of no ecological inheritance, adaptive niche-constructing behaviors such as valley-filling or ramp-placing strategies emerged, which created complex structures composed of multiple objects. It also turned out that the stable ecological inheritance of constructed structures could increase the adaptivity of the population by allowing an organism to maintain the inherited and adaptive structures while the unstable ecological inheritance rather decreases the adaptivity of the population by making previously adaptive structures maladaptive obstacles.
