Thomas Alves, Jérémy Rivière, Cédric Alaux,
Yves Le Conte, Frank Singhoff, Thierry Duval and Vincent Rodin.
An Interruptible Task Allocation Model -
Application to a Honey Bee Colony Simulation.
PAAMS 2020, 18th International Conference on Practical Applications of
Agents, Multi-Agent Systems, Aquila (Italy), 7-9 October 2020.
Published in PAAMS Collection: Advances in Practical Applications of Agents,
Multi-Agent Systems, and Trustworthiness.
Lecture Notes in Computer Science (LNCS), Springer, volume 12092,
pages 3-15, June 2020.
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.
Keywords:
Agent-based simulation, Task allocation, Self-organisation.
[doi:10.1007/978-3-030-49778-1_1]
[Alves20b.pdf]