Machine-learned Behaviour Models for a Distributed Behaviour Repository

Alexander Jahl, Harun Baraki, Stefan Jakob, Malte Fax, Kurt Geihs

2022

Abstract

Dynamically organised multi-agent systems that consist of heterogeneous participants require cooperation to fulfil complex tasks. Such tasks are commonly subdivided into subtasks that have to be executed by individual agents. The necessary teamwork demands coordination of the involved team members. In contrast to typical approaches like agent-centric and organisation-centric views, our solution is based on the task-centric view and thus contains active task components which select agents focusing on their Skills. It enables an encapsulated description of the task flow and its requirements including team cooperation, organisation, and location-independent allocation processes. Besides agent properties that represent syntactical and semantic information, agent behaviours are considered as well. The main contributions of this paper are hyperplane-based machine-learned Behaviour Models that are generated to capture the behaviour and consider the Behaviour Implementations as black boxes. These Behaviour Models are provided by a distributed behaviour repository that enables tasks to actively select fitting Behaviour Implementations. We evaluated our approach based on agents playing chessboard-like games autonomously.

Paper Citation

in Harvard Style

Jahl A., Baraki H., Jakob S., Fax M. and Geihs K. (2022). Machine-learned Behaviour Models for a Distributed Behaviour Repository. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-547-0, pages 188-199. DOI: 10.5220/0010804000003116

in Bibtex Style

@conference{icaart22,
author={Alexander Jahl and Harun Baraki and Stefan Jakob and Malte Fax and Kurt Geihs},
title={Machine-learned Behaviour Models for a Distributed Behaviour Repository},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2022},
pages={188-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010804000003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Machine-learned Behaviour Models for a Distributed Behaviour Repository
SN - 978-989-758-547-0
AU - Jahl A.
AU - Baraki H.
AU - Jakob S.
AU - Fax M.
AU - Geihs K.
PY - 2022
SP - 188
EP - 199
DO - 10.5220/0010804000003116