Abstract
Division of labour is a key aspect of distributed systems, such as swarm robotics or multi-agent systems. Inspired by social insects known for their task allocation capabilities, most of the models rely on two assumptions: 1) each task is associated with a stimulus, and 2) the execution of this task lowers that stimulus. In short, the stimulus is a representation of the amount of work needed on a task. When these assumptions are not true, we need a mechanism to guide the agent in its decision whether to pursue or to interrupt its current task, as there is no diminishing stimulus to rely on. In this article, we propose a model based on the Response Threshold Model and a mechanism based on the agent’s intrinsic motivation and internal states, allowing to take into account tasks dissociated from stimuli. Agents use their intrinsic motivation to emulate the priority of tasks not associated with any stimuli, and to decide whether to interrupt or pursue their current task. This model has been applied to simulate the division of labour within a simplified honey bee colony, associated with the constantly adapting physiology of honey bees. Preliminary results show that the task allocation is effective, robust and in some cases improved by the interruption mechanism.
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- 1.
The source code of the implementation (java), the table of parameters used in the experiments and the scripts (python) used to conduct the statistical analysis (with JASP) can be found on GitHub: https://github.com/Kwarthys/BeeKeeper.
- 2.
Larvae continuously emit a volatile pheromone called “E-\(\beta \)-ocimene”, but recent work has shown that hungry larvae emit more of it and thus attract more workers [12]. Yet, it is still unclear if this stimulus increases the feeding of the larvae.
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Alves, T. et al. (2020). An Interruptible Task Allocation Model. In: Demazeau, Y., Holvoet, T., Corchado, J., Costantini, S. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Trustworthiness. The PAAMS Collection. PAAMS 2020. Lecture Notes in Computer Science(), vol 12092. Springer, Cham. https://doi.org/10.1007/978-3-030-49778-1_1
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