Evolution of Self-Organized Task Specialization in Robot Swarms

The Emergence of Specialized Roles Within Groups

Humans routinely form groups to achieve goals that no individual can accomplish alone. Group coordination often brings to mind synchrony and alignment, where all individuals do the same thing (e.g., driving on the right side of the road, marching in lockstep, or playing musical instruments on a regular beat). Yet, effective coordination also typically involves differentiation, where specialized roles emerge for different members (e.g., prep stations in a kitchen or positions on an athletic team). Role specialization poses a challenge for computational models of group coordination, which have largely focused on achieving synchrony. Here, we present the CARMI framework, which characterizes role specialization processes in terms of five core features that we hope will help guide future model development: Communication, Adaptation to feedback, Repulsion, Multi-level planning, and Intention modeling. Although there are many paths to role formation, we suggest that roles emerge when each agent in a group dynamically allocates their behavior toward a shared goal to complement what they expect others to do. In other words, coordination concerns beliefs (who will do what) rather than simple actions. We describe three related experimental paradigms-"Group Binary Search," "Battles of the Exes," and "Find the Unicorn"-that we have used to study differentiation processes in the lab, each emphasizing different aspects of the CARMI framework.

Recent trends in robot learning and evolution for swarm robotics

Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms.

Evolution of self-organised division of labour driven by stigmergy in leaf-cutter ants

Social insects owe their widespread success to their ability to efficiently coordinate behaviour to carry out complex tasks. Several leaf-cutter ant species employ an advanced type of division of labour known as task partitioning, where the task of retrieving leaves is distributed between workers that cut and drop and those that collect the fallen leaves. It is not entirely clear how such highly coordinated behaviour can evolve, as it would seem to require the simultaneous mutations of multiple traits during the same generation. Here, we use an agent-based simulation model to show how task partitioning in leaf-cutter ants can gradually evolve by exploiting stigmergy (indirect coordination through the environment) through gravity (leaves falling from the treetop on the ground forming a cache). Our simple model allows independent variation in two core behavioural dimensions: the tendency to drop leaves and the tendency to pick up dropped leaves. Task partitioning readily evolves even under these minimal assumptions through adaptation to an arboreal environment where traveling up and down the tree is costly. Additionally, we analyse ant movement dynamics to demonstrate how the ants achieve efficient task allocation through task switching and negative feedback control.

Resource sharing is sufficient for the emergence of division of labour

Division of labour occurs in a broad range of organisms. Yet, how division of labour can emerge in the absence of pre-existing interindividual differences is poorly understood. Using a simple but realistic model, we show that in a group of initially identical individuals, division of labour emerges spontaneously if returning foragers share part of their resources with other group members. In the absence of resource sharing, individuals follow an activity schedule of alternating between foraging and other tasks. If non-foraging individuals are fed by other individuals, their alternating activity schedule becomes interrupted, leading to task specialisation and the emergence of division of labour. Furthermore, nutritional differences between individuals reinforce division of labour. Such differences can be caused by increased metabolic rates during foraging or by dominance interactions during resource sharing. Our model proposes a plausible mechanism for the self-organised emergence of division of labour in animal groups of initially identical individuals. This mechanism could also play a role for the emergence of division of labour during the major evolutionary transitions to eusociality and multicellularity.

How the History of Changing Environments Affects Traits of Evolvable Robot Populations

The environment is one of the key factors in the emergence of intelligent creatures, but it has received little attention within the Evolutionary Robotics literature. This article investigates the effects of changing environments on morphological and behavioral traits of evolvable robots. In particular, we extend a previous study by evolving robot populations under diverse changing-environment setups, varying the magnitude, frequency, duration, and dynamics of the changes. The results show that long-lasting effects of early generations occur not only when transitioning from easy to hard conditions, but also when going from hard to easy conditions. Furthermore, we demonstrate how the impact of environmental scaffolding is dependent on the nature of the environmental changes involved.

Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms

Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation. In this paper, we present an empirical study on the extent to which the reality gap impacts the most popular and advanced neuro-evolutionary methods for the off-line design of robot swarms. The results show that the neural networks produced by the methods under analysis performed well in simulation, but not in real-robot experiments. Further, the ranking that could be observed in simulation between the methods eventually disappeared. We find compelling evidence that real-robot experiments are needed to reliably assess the performance of neuro-evolutionary methods and that the robustness to the reality gap is the main issue to be addressed to advance the application of neuro-evolution to robot swarms.

Indirect genetic effects allow escape from the inefficient equilibrium in a coordination game

Social interactions involving coordination between individuals are subject to an "evolutionary trap." Once a suboptimal strategy has evolved, mutants playing an alternative strategy are counterselected because they fail to coordinate with the majority. This creates a detrimental situation from which evolution cannot escape, preventing the evolution of efficient collective behaviors. Here, we study this problem using evolutionary robotics simulations. We first confirm the existence of an evolutionary trap in a simple setting. We then, however, reveal that evolution can solve this problem in a more realistic setting where individuals need to coordinate with one another. In this setting, simulated robots evolve an ability to adapt plastically their behavior to one another, as this improves the efficiency of their interaction. This ability has an unintended evolutionary consequence: a genetic mutation affecting one individual's behavior also indirectly alters their partner's behavior because the two individuals influence one another. As a consequence of this indirect genetic effect, pairs of partners can virtually change strategy together with a single mutation, and the evolutionary barrier between alternative strategies disappears. This finding reveals a general principle that could play a role in nature to smoothen the transition to efficient collective behaviors in all games with multiple equilibriums.

Environmental influences on evolvable robots

The field of Evolutionary Robotics addresses the challenge of automatically designing robotic systems. Furthermore, the field can also support biological investigations related to evolution. In this paper, we evolve (simulated) modular robots under diverse environmental conditions and analyze the influences that these conditions have on the evolved morphologies, controllers, and behavior. To this end, we introduce a set of morphological, controller, and behavioral descriptors that together span a multi-dimensional trait space. Using these descriptors, we demonstrate how changes in environmental conditions induce different levels of differentiation in this trait space. Our main goal is to gain deeper insights into the effect of the environment on a robotic evolutionary process.

Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics

The real world is highly variable and unpredictable, and so fine-tuned robot controllers that successfully result in group-level "emergence" of swarm capabilities indoors may quickly become inadequate outside. One response to unpredictability could be greater robot complexity and cost, but this seems counter to the "swarm philosophy" of deploying (very) large numbers of simple agents. Instead, here I argue that bioinspiration in swarm robotics has considerable untapped potential in relation to the phenomenon of phenotypic plasticity: when a genotype can produce a range of distinctive changes in organismal behavior, physiology and morphology in response to different environments. This commonly arises following a natural history of variable conditions; implying the need for more diverse and hazardous simulated environments in offline, pre-deployment optimization of swarms. This will generate-indicate the need for-plasticity. Biological plasticity is sometimes irreversible; yet this characteristic remains relevant in the context of minimal swarms, where robots may become mass-producible. Plasticity can be introduced through the greater use of adaptive threshold-based behaviors; more fundamentally, it can link to emerging technologies such as smart materials, which can adapt form and function to environmental conditions. Moreover, in social animals, individual heterogeneity is increasingly recognized as functional for the group. Phenotypic plasticity can provide meaningful diversity "for free" based on early, local sensory experience, contributing toward better collective decision-making and resistance against adversarial agents, for example. Nature has already solved the challenge of resilient self-organisation in the physical realm through phenotypic plasticity: swarm engineers can follow this lead.

Sophisticated collective foraging with minimalist agents: a swarm robotics test

How groups of cooperative foragers can achieve efficient and robust collective foraging is of interest both to biologists studying social insects and engineers designing swarm robotics systems. Of particular interest are distance-quality trade-offs and swarm-size-dependent foraging strategies. Here, we present a collective foraging system based on virtual pheromones, tested in simulation and in swarms of up to 200 physical robots. Our individual agent controllers are highly simplified, as they are based on binary pheromone sensors. Despite being simple, our individual controllers are able to reproduce classical foraging experiments conducted with more capable real ants that sense pheromone concentration and follow its gradient. One key feature of our controllers is a control parameter which balances the trade-off between distance selectivity and quality selectivity of individual foragers. We construct an optimal foraging theory model that accounts for distance and quality of resources, as well as overcrowding, and predicts a swarm-size-dependent strategy. We test swarms implementing our controllers against our optimality model and find that, for moderate swarm sizes, they can be parameterised to approximate the optimal foraging strategy. This study demonstrates the sufficiency of simple individual agent rules to generate sophisticated collective foraging behaviour.

Automatic Off-Line Design of Robot Swarms: A Manifesto

Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms. We define the off-line design problem and illustrate it via a possible practical realization, highlight the core research questions, raise a number of issues regarding the existing literature that is relevant to the automatic off-line design, and provide guidelines that we deem necessary for a healthy development of the domain and for ensuring its relevance to potential real-world applications.

Iterated Local Search for Time-extended Multi-robot Task Allocation with Spatio-temporal and Capacity Constraints

We propose a method for task allocation to multiple physical agents that works when tasks have temporal and spatial constraints and agents have different capacities. Assuming that the problem is over-constrained, we need to find allocations that maximize the number of tasks that can be done without violating any of the constraints. The contribution of this work is the study of a new multi-robot task allocation problem and the design and the experimental evaluation of our approach, an iterated local search that is suitable for time critical applications. We created test instances on which we experimentally show that our approach outperforms a state-of-the-art approach to a related problem. Our approach improves the baseline’s score on average by 2.35% and up to 10.53%, while responding in times shorter than the baseline’s, on average, 1.6 s and up to 5.5 s shorter. Furthermore, our approach is robust to run replication and is not very sensitive to parameters tuning.

Regulatory mechanism predates the evolution of self-organizing capacity in simulated ant-like robots

The evolution of complexity is one of the prime features of life on Earth. Although well accepted as the product of adaptation, the dynamics underlying the evolutionary build-up of complex adaptive systems remains poorly resolved. Using simulated robot swarms that exhibit ant-like group foraging with trail pheromones, we show that their self-organizing capacity paradoxically involves regulatory behavior that arises in advance. We focus on a traffic rule on their foraging trail as a regulatory trait. We allow the simulated robot swarms to evolve pheromone responsiveness and traffic rules simultaneously. In most cases, the traffic rule, initially arising as selectively neutral component behaviors, assists the group foraging system to bypass a fitness valley caused by overcrowding on the trail. Our study reveals a hitherto underappreciated role of regulatory mechanisms in the origin of complex adaptive systems, as well as highlights the importance of embodiment in the study of their evolution.

Control algorithms for the emergence of self-organized behaviours in swarms of differential-traction wheeled mobile robots

This article proposes three control algorithms for the emergence of self-organized behaviours, including aggregation, flocking and rendezvous, in swarm robotics systems. The proposed control algorithms are based on a local polar coordinates’ control law available in the literature for posture regulation; this law is adapted to work in a self-organized robotic swarm using distance and bearing as coupling information. Therefore, the robots only need to know the radial distance and orientation to the goal; additionally, the three algorithms are based on self-organization, eliminating the need for a preset coupling topology among the robots. In particular, the flocking algorithm has a first stage for topology creation, while the rendezvous and aggregation algorithms change the topology on every iteration depending on the local interactions of the robots. The effectiveness of the algorithms was evaluated through numerical simulations of swarms of up to 100 differential traction wheeled mobile robots.

Evolutionary Acquisition of Autonomous Specialization in a Path-Formation Task of a Robotic Swarm

Task allocation is an important concept not only in biological systems but also in artificial systems. This paper reports a case study of autonomous task allocation behavior in an evolutionary robotic swarm. We address a path-formation task that is a fundamental task in the field of swarm robotics. This task aims to generate the collective path that connects two different locations by using many simple robots. Each robot has a limited sensing ability with distance sensors, a ground sensor, and a coarse-grained omnidirectional camera to perceive its local environment and the limited actuators composed of two colored LEDs and two-wheeled motors. Our objective is to develop a robotic swarm with autonomous specialization behavior from scratch, by exclusively implementing a homogeneous evolving artificial neural network controller for the robots to discuss the importance of embodiment that is the source of congestion. Computer simulations demonstrate the adaptive collective behavior that emerged in a robotic swarm with various swarm sizes and confirm the feasibility of autonomous task allocation for managing congestion in larger swarm sizes.

Information Exchange Design Patterns for Robot Swarm Foraging and Their Application in Robot Control Algorithms

In swarm robotics, a design pattern provides high-level guidelines for the implementation of a particular robot behaviour and describes its impact on swarm performance. In this paper, we explore information exchange design patterns for robot swarm foraging. First, a method for the specification of design patterns for robot swarms is proposed that builds on previous work in this field and emphasises modular behaviour design, as well as information-centric micro-macro link analysis. Next, design pattern application rules that can facilitate the pattern usage in robot control algorithms are given. A catalogue of six design patterns is then presented. The patterns are derived from an extensive list of experiments reported in the swarm robotics literature, demonstrating the capability of the proposed method to identify distinguishing features of robot behaviour and their impact on swarm performance in a wide range of swarm implementations and experimental scenarios. Each pattern features a detailed description of robot behaviour and its associated parameters, facilitated by the usage of a multi-agent modeling language, BDRML, and an account of feedback loops and forces that affect the pattern’s applicability. Scenarios in which the pattern has been used are described. The consequences of each design pattern on overall swarm performance are characterised within the Information-Cost-Reward framework, that makes it possible to formally relate the way in which robots acquire, share and utilise information. Finally, the patterns are validated by demonstrating how they improved the performance of foraging e-puck swarms and how they could guide algorithm design in other scenarios.

Vitellogenin-like A-associated shifts in social cue responsiveness regulate behavioral task specialization in an ant

Division of labor and task specialization explain the success of human and insect societies. Social insect colonies are characterized by division of labor, with workers specializing in brood care early and foraging later in life. Theory posits that this task switching requires shifts in responsiveness to task-related cues, yet experimental evidence is weak. Here, we show that a Vitellogenin (Vg) ortholog identified in an RNAseq study on the ant T. longispinosus is involved in this process: using phylogenetic analyses of Vg and Vg-like genes, we firstly show that this candidate gene does not cluster with the intensively studied honey bee Vg but falls into a separate Vg-like A cluster. Secondly, an experimental knockdown of Vg-like A in the fat body caused a reduction in brood care and an increase in nestmate care in young ant workers. Nestmate care is normally exhibited by older workers. We demonstrate experimentally that this task switch is at least partly based on Vg-like A-associated shifts in responsiveness from brood to worker cues. We thus reveal a novel mechanism leading to early behavioral maturation via changes in social cue responsiveness mediated by Vg-like A and associated pathways, which proximately play a role in regulating division of labor.

Embodied Evolution in Collective Robotics: A Review

This article provides an overview of evolutionary robotics techniques applied to online distributed evolution for robot collectives, namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. This article also presents a comprehensive summary of research published in the field since its inception around the year 2000, providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an online distributed learning method for designing collective behaviors in swarm-like collectives. This article concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.

Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

Research on distributed task planning model for multi-autonomous underwater vehicle (MAUV). A scroll time domain quantum artificial bee colony (STDQABC) optimization algorithm is proposed to solve the multi-AUV optimal task planning scheme. In the uncertain marine environment, the rolling time domain control technique is used to realize a numerical optimization in a narrowed time range. Rolling time domain control is one of the better task planning techniques, which can greatly reduce the computational workload and realize the tradeoff between AUV dynamics, environment and cost. Finally, a simulation experiment was performed to evaluate the distributed task planning performance of the scroll time domain quantum bee colony optimization algorithm. The simulation results demonstrate that the STDQABC algorithm converges faster than the QABC and ABC algorithms in terms of both iterations and running time. The STDQABC algorithm can effectively improve MAUV distributed tasking planning performance, complete the task goal and get the approximate optimal solution.

Handling interference effects on foraging with bucket brigades

Many kinds of bio-inspired tasks have been tested with swarm robotics and task partitioning is one of the challenging subjects. In nature, it is well known that some colonies of social insects such as honeybees, termites, and ants use task partitioning strategies for their survival. In this paper, we demonstrate an effect of the task partitioning strategy called bucket brigade, which uses the direct transfer of materials or food between a pair of workers. We propose a task partitioning strategy based on the moving speeds of agents for the foraging task. We test various environmental conditions and compare the performance between task partitioning groups and non-partitioning groups. The experimental results show that task partitioning may not always be the best solution for foraging performance. However, when there exists a transfer bottleneck at a central location such as the entrance of the nest, task partitioning can be an effective strategy for reducing the traffic jam and improving the overall foraging performance of a group. The bucket brigade sequenced from the slowest agents (near the food source) to the fastest agents (near the nest) can particularly improve performance significantly in the region with traffic congestion near the nest. Generally, many social insect colonies consist of a number of members, and the entrances of colony nests always suffer from heavy traffic congestion. Our experimental results support the hypothesis that several social insects use one of the task partitioning strategies based on bucket brigades in their foraging tasks.

Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.

Symbiotic Cell Differentiation and Cooperative Growth in Multicellular Aggregates

As cells grow and divide under a given environment, they become crowded and resources are limited, as seen in bacterial biofilms and multicellular aggregates. These cells often show strong interactions through exchanging chemicals, as evident in quorum sensing, to achieve mutualism and division of labor. Here, to achieve stable division of labor, three characteristics are required. First, isogenous cells differentiate into several types. Second, this aggregate of distinct cell types shows better growth than that of isolated cells without interaction and differentiation, by achieving division of labor. Third, this cell aggregate is robust with respect to the number distribution of differentiated cell types. Indeed, theoretical studies have thus far considered how such cooperation is achieved when the ability of cell differentiation is presumed. Here, we address how cells acquire the ability of cell differentiation and division of labor simultaneously, which is also connected with the robustness of a cell society. For this purpose, we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics. The reactions convert external nutrients into internal components for cellular growth, and the divided cells interact through chemical diffusion. We found that cells sharing an identical catalytic network spontaneously differentiate via induction from cell-cell interactions, and then achieve division of labor, enabling a higher growth rate than that in the unicellular case. This symbiotic differentiation emerged for a class of reaction networks under the condition of nutrient limitation and strong cell-cell interactions. Then, robustness in the cell type distribution was achieved, while instability of collective growth could emerge even among the cooperative cells when the internal reserves of products were dominant. The present mechanism is simple and general as a natural consequence of interacting cells with limited resources, and is consistent with the observed behaviors and forms of several aggregates of unicellular organisms.

Unicellular organisms, when aggregated under limited resources, often exhibit behaviors akin to multicellular organisms, possibly without advanced regulation mechanisms, as observed in biofilms and bacterial colonies. Cells in an aggregate have to differentiate into several types that are specialized for different tasks, so that the growth rate should be enhanced by the division of labor among these cell types. To consider how a cell aggregate can acquire these properties, most theoretical studies have thus far assumed the fitness of an aggregate of cells and the ability of cell differentiation a priori. In contrast, we developed a dynamical-systems model consisting of cells without assuming predefined fitness. The model consists of catalytic-reaction networks for cellular growth. By extensive simulations and theoretical analysis of the model, we showed that cells growing under the condition of nutrient limitation and strong cell-cell interactions can differentiate with distinct chemical compositions. They achieve cooperative division of labor by exchanging the produced chemicals to attain a higher growth rate. The conditions for spontaneous cell differentiation and collective growth of cells are presented. The uncovered symbiotic differentiation and collective growth are akin to economic theory on division of labor and comparative advantage.

Extended inclusive fitness theory: synergy and assortment drives the evolutionary dynamics in biology and economics

W.D. Hamilton's Inclusive Fitness Theory explains the conditions that favor the emergence and maintenance of social cooperation. Today we know that these include direct and indirect benefits an agent obtains by its actions, and through interactions with kin and with genetically unrelated individuals. That is, in addition to kin-selection, assortation or homophily, and social synergies drive the evolution of cooperation. An Extended Inclusive Fitness Theory (EIFT) synthesizes the natural selection forces acting on biological evolution and on human economic interactions by assuming that natural selection driven by inclusive fitness produces agents with utility functions that exploit assortation and synergistic opportunities. This formulation allows to estimate sustainable cost/benefit threshold ratios of cooperation among organisms and/or economic agents, using existent analytical tools, illuminating our understanding of the dynamic nature of society, the evolution of cooperation among kin and non-kin, inter-specific cooperation, co-evolution, symbioses, division of labor and social synergies. EIFT helps to promote an interdisciplinary cross fertilization of the understanding of synergy by, for example, allowing to describe the role for division of labor in the emergence of social synergies, providing an integrated framework for the study of both, biological evolution of social behavior and economic market dynamics. Another example is a bio-economic understanding of the motivations of terrorists, which identifies different forms of terrorism.

To Cooperate or Not to Cooperate: Why Behavioural Mechanisms Matter

Mutualistic cooperation often requires multiple individuals to behave in a coordinated fashion. Hence, while the evolutionary stability of mutualistic cooperation poses no particular theoretical difficulty, its evolutionary emergence faces a chicken and egg problem: an individual cannot benefit from cooperating unless other individuals already do so. Here, we use evolutionary robotic simulations to study the consequences of this problem for the evolution of cooperation. In contrast with standard game-theoretic results, we find that the transition from solitary to cooperative strategies is very unlikely, whether interacting individuals are genetically related (cooperation evolves in 20% of all simulations) or unrelated (only 3% of all simulations). We also observe that successful cooperation between individuals requires the evolution of a specific and rather complex behaviour. This behavioural complexity creates a large fitness valley between solitary and cooperative strategies, making the evolutionary transition difficult. These results reveal the need for research on biological mechanisms which may facilitate this transition.

Ant workers exhibit specialization and memory during raft formation

By working together, social insects achieve tasks that are beyond the reach of single individuals. A striking example of collective behaviour is self-assembly, a process in which individuals link their bodies together to form structures such as chains, ladders, walls or rafts. To get insight into how individual behavioural variation affects the formation of self-assemblages, we investigated the presence of task specialization and the role of past experience in the construction of ant rafts. We subjected groups of Formica selysi workers to two consecutive floods and monitored the position of individuals in rafts. Workers showed specialization in their positions when rafting, with the same individuals consistently occupying the top, middle, base or side position in the raft. The presence of brood modified workers' position and raft shape. Surprisingly, workers' experience in the first rafting trial with brood influenced their behaviour and raft shape in the subsequent trial without brood. Overall, this study sheds light on the importance of workers' specialization and memory in the formation of self-assemblages.

A computational functional genomics based self-limiting self-concentration mechanism of cell specialization as a biological role of jumping genes

LINE-1 retrotransposition may result in silencing of genes. This is more likely with genes not carrying active LINE-1 as those are about 10 times more frequent in the given set of genes. Over time this leads to self-specialization of the cell toward processes associated with gene carrying active LINE-1, which then functionally prevail in the chronified situation.