Efficient Selection of Consistent Plans Using Patterns and Constraint Satisfaction for Beliefs-Desires-Intentions Agents

Veronika Kurchyna, Ye Eun Bae, Jan Ole Berndt, Ingo J. Timm

2025

Abstract

Agent-based models can portray complex systems that emerge from the actions of individual actors. The use of many agents with complex decision-making processes in a large action space is computationally intensive and leads to slow simulations. This work proposes an alternative approach to agent deliberation by pre-computing valid action sequences and simplifying decision-making at runtime to a linear problem. The method is demonstrated with pandemic self-protection as use case for an implementation of the concept. Additionally, a step-by-step guideline for application of this approch is provided.

Paper Citation

in Harvard Style

Kurchyna V., Bae Y., Berndt J. and Timm I. (2025). Efficient Selection of Consistent Plans Using Patterns and Constraint Satisfaction for Beliefs-Desires-Intentions Agents. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 333-341. DOI: 10.5220/0013141700003890

in Bibtex Style

@conference{icaart25,
author={Veronika Kurchyna and Ye Eun Bae and Jan Berndt and Ingo Timm},
title={Efficient Selection of Consistent Plans Using Patterns and Constraint Satisfaction for Beliefs-Desires-Intentions Agents},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2025},
pages={333-341},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013141700003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Efficient Selection of Consistent Plans Using Patterns and Constraint Satisfaction for Beliefs-Desires-Intentions Agents
SN - 978-989-758-737-5
AU - Kurchyna V.
AU - Bae Y.
AU - Berndt J.
AU - Timm I.
PY - 2025
SP - 333
EP - 341
DO - 10.5220/0013141700003890
PB - SciTePress