Self-organized Task Allocation Research Articles

The Entrainment of Task Allocation and Release Cycles in Open Source Software Development

In this study we identify a process of “entrainment” around open source software (OSS) development release cycles to capture patterns of self-organized task allocation among developers. We conducted an abductive, computationally intensive study of eight OSS projects, using relational event modeling to analyze 1,169,489 actions covering 93 major software releases. The process of entrainment that we identify involves three task allocation mechanisms: (1) developer-issue inertia, (2) developer contribution frequency, and (3) issue popularity. Our analysis demonstrates that these mechanisms and the phases of the release cycle entrain each other. Before a major release, developers engage in a concentrated mobilization phase, whereby they democratize development activity and increasingly allocate community contributions to the set of issues related to the release. After a major release, the extended cleanup phase garners a greater share of development work from recently highly active developers and dilutes the activity of these developers across a wider range of issues. Our theorizing suggests that major releases constitute important events around which OSS communities self-organize and we characterize how this occurs. Our research contributes to theorizing on organizing in OSS communities by explaining how self-organizing task allocation interacts with release cycles through the mechanism of entrainment. We also contribute to the literature on entrainment by showing how it may unfold in the context of online peer production communities such as OSS.

Dynamic Response Threshold Model for Self-Organized Task Allocation in a Swarm of Foraging Robots

In swarm-robotics foraging, the purpose of task allocation is to adjust the number of active foraging robots dynamically based on the task demands and changing environment. It is a difficult challenge to generate self-organized foraging behavior in which each robot can adapt to environmental changes. To complete the foraging task efficiently, this paper presents a novel self-organized task allocation strategy known as the dynamic response threshold model (DRTM). To adjust the behavior of the active foraging robots, the proposed DRTM newly introduces the traffic flow density, which can be used to evaluate the robot density. Firstly, the traffic flow density and the amount of obstacle avoidance are used to adjust the threshold which determines the tendency of a robot to respond to a stimulus in the environment. Then, each individual robot uses the threshold and external stimulus to calculate the foraging probability that determines whether or not to go foraging. This paper completes the simulation and physical experiments, respectively, and the performance of the proposed method is evaluated using three commonly used performance indexes: the average deviation of food, the energy efficiency, and the number of obstacle avoidance events. The experimental results show that the DRTM is superior to and more efficient than the adaptive response threshold model (ARTM) in all three indexes.

Global-to-Local Design for Self-Organized Task Allocation in Swarms

Programming robot swarms is hard because system requirements are formulated at the swarm level (i.e., globally) while control rules need to be coded at the individual robot level (i.e., locally). Connecting global to local levels or vice versa through mathematical modeling to predict the system behavior is generally assumed to be the grand challenge of swarm robotics. We propose to approach this problem by programming directly at the swarm level. Key to this solution is the use of heterogeneous swarms that combine appropriate subsets of agents whose hard-coded agent behaviors have known global effects. Our novel global-to-local design methodology allows to compose heterogeneous swarms for the example application of self-organized task allocation. We define a large but finite number of local agent controllers and focus on the global dynamics of behaviorally heterogeneous swarms. The user inputs the desired global task allocation for the swarm as a stationary probability distribution of agents allocated over tasks. We provide a generic method that implements the desired swarm behavior by mathematically deriving appropriate compositions of heterogeneous swarms that approximate these global user requirements. We investigate our methodology over several task allocation scenarios and validate our results with multiagent simulations. The proposed global-to-local design methodology is not limited to task allocation problems and can pave the way to formal approaches to design other swarm behaviors.

Self-organized search-attack mission planning for UAV swarm based on wolf pack hunting behavior

Cooperative search-attack is an important application of unmanned aerial vehicle (UAV) swarm in military field. The coupling between path planning and task allocation, the heterogeneity of UAVs, and the dynamic nature of task environment greatly increase the complexity and difficulty of the UAV swarm cooperative search-attack mission planning problem. Inspired by the collaborative hunting behavior of wolf pack, a distributed self-organizing method for UAV swarm search-attack mission planning is proposed. First, to solve the multi-target search problem in unknown environments, a wolf scouting behavior-inspired cooperative search algorithm for UAV swarm is designed. Second, a distributed self-organizing task allocation algorithm for UAV swarm cooperative attacking of targets is proposed by analyzing the flexible labor division behavior of wolves. By abstracting the UAV as a simple artificial wolf agent, the flexible motion planning and group task coordinating for UAV swarm can be realized by self-organizing. The effectiveness of the proposed method is verified by a set of simulation experiments, the stability and scalability are evaluated, and the integrated solution for the coupled path planning and task allocation problems for the UAV swarm cooperative search-attack task can be well performed.

Game theoretic self-organization in multi-satellite distributed task allocation

This paper addresses self-organized task allocation in multi-satellite systems by formulating it as a potential game and proposing a distributed task allocation algorithm (DT2A) via learning in games. We show that each Nash equilibrium of the game constitutes a task cover that maximizes the number of executed tasks. Moreover, we also prove that the proposed DT2A converges with probability 1 to near optimal assignments in finite time. Numerical experiments demonstrate the algorithm robustness against incidental disturbances and reveal the positive effect of the memory length on solution efficiency refinement. Comparison experiments in both small-scale and large-scale scenarios highlight the superiority of the presented methodology over existing typical methods that are distributed or centralized.

A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation

Social insects allocate their workforce in a decentralised fashion, addressing multiple tasks and responding effectively to environmental changes. This process is fundamental to their ecological success, but the mechanisms behind it are not well understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap, we propose a game theoretical model of task allocation. Our main findings are twofold: Firstly, the specialisation emerging from self-organised task allocation can be largely determined by the ecology. Weakly specialised colonies in which all individuals perform more than one task emerge when foraging is cheap; in contrast, harsher environments with high foraging costs lead to strong specialisation in which each individual fully engages in a single task. Secondly, social interactions lead to important differences in dynamic environments. Colonies whose individuals rely on their own experience are predicted to be more flexible when dealing with change than colonies relying on social information. We also find that, counter to intuition, strongly specialised colonies may perform suboptimally, whereas the group performance of weakly specialised colonies approaches optimality. Our simulation results fully agree with the predictions of the mathematical model for the regions where the latter is analytically tractable. Our results are useful in framing relevant and important empirical questions, where ecology and interactions are key elements of hypotheses and predictions.

Evolutionary dynamics in division of labor games on cycle networks

This paper theoretically investigates the evolutionary dynamics in the division of labor games performed on the cycle networks. Several typical updating processes based on the Moran process are considered here: Birth-Death (BD) updating process, Death-Birth (DB) updating process and the mixed DB-BD updating process. The exponential payoff-to-fitness mapping is adopted, and analytical results for fixation time and fixation probability under the condition of strong selection are provided. In addition, numerical values of several crucial quantities within the framework of different game scenarios, decided by the payoff matrix of the division of labor (DOL) games, are studied for comparison. The conclusion clearly shows that, even under different kinds of DOL games, the fixation times are closely associated with the specific update mechanism. This work provides a deeper look into the strategic interactions and network dynamics in the context of the DOL games based on self-organized task allocation.

Stochastic dynamics of division of labor games in finite populations

A theoretical investigation on the strategy evolution in the self-organized division of labor dilemma is performed by means of evolutionary game theory. The often-used Fermi function is employed for driving the strategy updating, based on which the fixation probability of the involved strategy (performing which task) is calculated. Results about the evolution dynamics of the division of labor for two-player games are: (1) the fixation probabilityfor any selection intensity is derived; (2) the fixation probability and fixation time under weak selection are gained and a comparison with neutral selection is performed. In this case, the conditions to facilitate cooperation in division of labor are found. Then we extend the model to a multi-player one to describe the self-organized task allocation when multiple players are involved in one game. Relevant results for weak selection to favor the coexistence state of the two strategies for the multi-player games are gained. These results help understand and design effective mechanism where self-organized collective dynamics occurs in the form of maximizing the benefit of the multi-agent system.

Task allocation in organic computing systems: networks with reconfigurable helper units

In this paper, computing systems that have no central control and consist of many partially autonomous components are studied. Two types of components are distinguished which are called workers and helpers respectively. The components are connected via a network and the helper components perform service tasks for the worker components. It is assumed that the helper components exhibit reconfigurable hardware to be able to execute different service tasks efficiently. The problem addressed in this paper is how to organise such a decentralised system in a way that requests of the workers are executed by suitable helpers and the total reconfiguration costs of the helpers are small. Several decentralised task allocation methods are proposed. One of them uses a combination of a fully decentralised dynamic clustering algorithm and a self-organised task allocation system. The clustering algorithm is used to classify the service requests that are sent through the network in order to give the helpers hints which requests are suitable to be executed by them. Simulations are done for static and dynamic scenarios to investigate the reconfiguration costs and the number of dropped requests, i.e., requests that could not be satisfied. The results show that the clustering-based system has a strong adaptive behaviour and that the decentralised clustering is able to reduce the reconfiguration costs significantly.

Self-Organized Task Allocation between Reinforcement Learning Robots and a Human Partner

Self-Organized Task Allocation between Reinforcement Learning Robots and a Human Partner

Self-organized task allocation to sequentially interdependent tasks in swarm robotics

In this article we present a self-organized method for allocating the individuals of a robot swarm to tasks that are sequentially interdependent. Tasks that are sequentially interdependent are common in natural and artificial systems. The proposed method does neither rely on global knowledge nor centralized components. Moreover, it does not require the robots to communicate. The method is based on the delay experienced by the robots working on one subtask when waiting for input from another subtask. We explore the capabilities of the method in different simulated environments. Additionally, we evaluate the method in a proof-of-concept experiment using real robots. We show that the method allows a swarm to reach a near-optimal allocation in the studied environments, can easily be transferred to a real robot setting, and is adaptive to changes in the properties of the tasks such as their duration. Finally, we show that the ideal setting of the parameters of the method does not depend on the properties of the environment.

Sensory allometry, foraging task specialization and resource exploitation in honeybees

Insect societies are important models for evolutionary biology and sociobiology. The complexity of some eusocial insect societies appears to arise from self-organized task allocation and group cohesion. One of the best-supported models explaining self-organized task allocation in social insects is the response threshold model, which predicts specialization due to inter-individual variability in sensitivity to task-associated stimuli. The model explains foraging task specialization among honeybee workers, but the factors underlying the differences in individual sensitivity remain elusive. Here, we propose that in honeybees, sensory sensitivity correlates with individual differences in the number of sensory structures, as it does in solitary species. Examining European and Africanized honeybees, we introduce and test the hypothesis that body size and/or sensory allometry is associated with foraging task preferences and resource exploitation. We focus on common morphological measures and on the size and number of structures associated with olfactory sensitivity. We show that the number of olfactory sensilla is greater in pollen and water foragers, which are known to exhibit higher sensory sensitivity, compared to nectar foragers. These differences are independent of the distribution of size within a colony. Our data also suggest that body mass and number of olfactory sensilla correlate with the concentration of nectar gathered by workers, and with the size of pollen loads they carry. We conclude that sensory allometry, but not necessarily body size, is associated with resource exploitation in honeybees and that the differences in number of sensilla may underlie the observed differences in sensitivity between bees specialized on water, pollen and nectar collection.

Self-organizing method for collaboration in multi-robot system on basis of balance principle

By analyzing the operation characteristics of two subtasks that have resource dependency on each other, this paper demonstrates the impact of progress relation between the two subtasks on the whole task’s progress, and then puts forward a self-organizing principle called balance principle that keeps the individual profit between robots equal. Furthermore, an algorithm is designed for adjusting subtask selection on the basis of this principle. Simulation shows the validity of the algorithm on self-organizing task allocation in a multi-robot system.

Self-Organized Task Allocation for Service Tasks in Computing Systems with Reconfigurable Components

A self-organized scheme for the allocation service tasks in adaptive or organic computing systems is proposed. Such computing systems are highly self-organized and the components ideally adapt to the needs of users or the environment. Typically, the components of such systems need some service from time to time in order perform their work efficiently. Since the type of service tasks will often change in this systems it is attractive to use reconfigurable hardware to perform the service tasks. The studied system consists of normal worker components and the helper components which have reconfigurable hardware and can perform different service tasks. The speed with which a service task is executed by a helper depends on its actual configuration. Different strategies for the helpers to decide about service task acceptance and reconfiguration are proposed. These task acceptance strategies are inspired by stimulus-threshold models that are used to explain task allocation in social insects. Analytical results for a system with two reconfigurable helpers are presented together with simulation results for larger systems.

The move-to-partner rule for self-organizing task allocation on a linear array

The well-known Move-to-Front rule for self-organizing lists is modified to a so-called Move-to-Partner rule for dynamic task allocation on a chain of processors. We enable an analysis of the sequence of expected communication costs by reducing their computation to that of three-dimensional arrays of certain probabilities, for which a recursion formula and an asymptotic expansion can be given. The costs depend on the underlying communication profile; two types of such profiles are investigated in more detail, namely a two-dimensional version of Zipf's law, and profiles based on a class structure of tasks. In the considered cases, the Move-to-Partner rule effects fast convergence to a stationary state, but comparably high expected stationary costs.

The call of duty: Self-organised task allocation in a population of up to twelve mobile robots

Teams with up to 12 real robots were given the mission to maintain the energy stocked in their nest by collecting food-items. To achieve this mission efficiently, we implemented a simple and decentralised task allocation mechanism based on individual activation-thresholds, i.e. the energy level of the nest under which a given robot decides to go collect food-items. The experiments show that such a mechanism — already studied among social insects — results in an efficient dynamical task allocation even under the noisy conditions prevailing in real experiments. Experiments with different team sizes were carried out to investigate the effect of team size on performance and risk of mission failure.