Do ants paint trucks better than chickens? Markets versus response thresholds for distributed dynamic scheduling

Kittithreerapronchai, Oran; Anderson, Carl W.

doi:10.1109/cec.2003.1299839

Cited by 18 publications

(17 citation statements)

References 10 publications

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“…The problem we investigate involves a single task, and the team of agents solving this problem must self-organize such that an appropriate number of them are working and not working on the task at any given time. Both the biological [3], [4], [5] and multi-agent [6], [7], [8], [9], [10] literatures provide examples of systems where behavioral differences among the individuals of a team help the team as a whole reach its objective. We hypothesize that inter-agent variation is not only beneficial but also essential to a distributed team's ability to self-organize.…”

Section: Introductionmentioning

confidence: 99%

On the Impact of Variation on Self-Organizing Systems

Campbell

Riggs

2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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Abstract-The goal of this work is to gain a better understanding of the role that inter-agent variation plays in selforganizing systems. We develop both continuous and discrete models of a multi-agent coordination procedure based on response thresholds and use these models to analyze the average behavior of a system as well as examine the dynamics of single instances of the problem. Results indicate that variation in the behaviors of agents can lead to increased stability and more effective self-organization in cooperative multi-agent systems.

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Section: Introductionmentioning

confidence: 99%

On the Impact of Variation on Self-Organizing Systems

Campbell

Riggs

2011

2011 IEEE Fifth International Conference on Self-Adaptive and Self-Organizing Systems

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“…Every algorithm was optimised for each setting it was tested in (high and low load, static and dynamic environments). Note that PSO provided no improvement for the MB algorithm over the standard settings, due to its relative insensitivity to parameter choice [19]. Table 1 illustrates the efficiency of the various algorithms for t c = 2.…”

Section: Performancementioning

confidence: 99%

“…In order to do so, we apply existing threshold-and market-based algorithms to a problem of distributed task allocation based on a mail retrieval problem in which agents travel to distinct locations (cities) at which they must choose between various tasks (types of mail to process). It has been used in various comparative studies of algorithms for distributed task allocation, presented either as a mail processing [25,26,12,13], or truck painting problem [4,19]. It is a flexible problem which allows for a comprehensive investigation of the behaviour of candidate algorithms.…”

Section: Introductionmentioning

confidence: 99%

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The effect of load on agent-based algorithms for distributed task allocation

Goldingay

Mourik

2013

Information Sciences

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Multi-agent algorithms inspired by the division of labour in social insects and by markets, are applied to a constrained problem of distributed task allocation. The efficiency (average number of tasks performed), the flexibility (ability to react to changes in the environment), and the sensitivity to load (ability to cope with differing demands) are investigated in both static and dynamic environments. A hybrid algorithm combining both approaches, is shown to exhibit improved efficiency and robustness. We employ nature inspired particle swarm optimisation to obtain optimised parameters for all algorithms in a range of representative environments. Although results are obtained for large population sizes to avoid finite size effects, the influence of population size on the performance is also analysed. From a theoretical point of view, we analyse the causes of efficiency loss, derive theoretical upper bounds for the efficiency, and compare these with the experimental results.

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“…The stimulus (S) represents the number of time steps an EC is in the WfMS, in this study we use S=1 [14]. The parameter tuning for the learning mechanism can be based on a genetic algorithm [25], a simple hand tuning technique [26], or a sensitivity analysis that first sets the most important parameter followed by the parameter with the second highest impact, and so on [27]. Based on the latter approach, we derived the threshold values as well as the learning importance α and the task duration component β.…”

Section: Simulation Approachmentioning

confidence: 99%

Workflow Management Systems + Swarm Intelligence = Dynamic Task Assignment for Emergency Management Applications

Reijers

Jansen-Vullers

Appl

Lecture Notes in Computer Science

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Abstract. The assignment of tasks to human performers is a critical component in people-centric business process management systems. Workflow management systems typically assign work items using strategies that only consider qualified resources. There are, however, situations, where this approach falls short. For instance, in emergency response situations, tasks need to be carried out by resources that are available immediately, even if they do not match all skill requirements. This paper compares the performance of a set of six task assignment mechanisms for workflow applications using a scenario from the emergency management domain. In particular, we develop and simulate assignment strategies inspired by stimulus/response models derived from swarm intelligence, and benchmark these strategies against conventional task assignment strategies. Our findings show that swarm intelligence-based approaches outperform the traditional assignment of tasks in ad-hoc organizations, and that workflow-based emergency management systems could benefit significantly from these novel task assignment strategies.

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Do ants paint trucks better than chickens? Markets versus response thresholds for distributed dynamic scheduling

Cited by 18 publications

References 10 publications

On the Impact of Variation on Self-Organizing Systems

On the Impact of Variation on Self-Organizing Systems

The effect of load on agent-based algorithms for distributed task allocation

Workflow Management Systems + Swarm Intelligence = Dynamic Task Assignment for Emergency Management Applications

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