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Author: Tomoko Sakiyama

Affiliation: Department of Information Systems Science, Faculty of Science and Engineering, Soka University, Tokyo 192-8577, Japan

Keyword(s): Random Walk, Super-diffusion, Memory, Resource Distribution.

Abstract: A random walker explores an unknown field and sometimes changes its movement property using new spatial information obtained by it during its exploration. An important matter is the relation between the movement property of a random walker and the use for acquired information. I recently developed a random walk model in which a walker coordinated its directional rule based on its experiences and found that this model presented an optimal random walk, which demonstrated a so-called Lévy walk with μ = 2.00. Here, I investigate the foraging efficiency for that model and verify whether a large memory capacity is required or not in order to maintain the foraging efficiency. My findings reveal that the proposed model can apply to biological processes where a random walker does not have a high memory capacity.

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Paper citation in several formats:
Sakiyama, T. (2021). A Random Walker Can Optimize the Exploration without the Large Capacity Memory. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Paradigms-Methods-Approaches; ISBN 978-989-758-490-9; ISSN 2184-4305, SciTePress, pages 209-212. DOI: 10.5220/0010369902090212

@conference{paradigms-methods-approaches21,
author={Tomoko Sakiyama.},
title={A Random Walker Can Optimize the Exploration without the Large Capacity Memory},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Paradigms-Methods-Approaches},
year={2021},
pages={209-212},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010369902090212},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - Paradigms-Methods-Approaches
TI - A Random Walker Can Optimize the Exploration without the Large Capacity Memory
SN - 978-989-758-490-9
IS - 2184-4305
AU - Sakiyama, T.
PY - 2021
SP - 209
EP - 212
DO - 10.5220/0010369902090212
PB - SciTePress

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