Supervised Learning for Untangling Braids

Alexei Lisitsa; Mateo Salles; Alexei Vernitski

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Paper

Supervised Learning for Untangling Braids

Topics: Deep Learning; Machine Learning; Neural Networks

In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, 784-789, 2023 , Lisbon, Portugal

Authors: Alexei Lisitsa ¹ ; Mateo Salles ² and Alexei Vernitski ²

Affiliations: ¹ University of Liverpool, U.K. ; ² University of Essex, U.K.

Keyword(s): Deep Learning, Supervised Learning, Behavioral Cloning, Braid.

Abstract: Untangling a braid is a typical multi-step process, and reinforcement learning can be used to train an agent to untangle braids. Here we present another approach. Starting from the untangled braid, we produce a dataset of braids using breadth-first search and then apply behavioral cloning to train an agent on the output of this search. As a result, the (inverses of) steps predicted by the agent turn out to be an unexpectedly good method of untangling braids, including those braids which did not feature in the dataset.

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Paper citation in several formats:

Lisitsa, A.; Salles, M. and Vernitski, A. (2023). Supervised Learning for Untangling Braids. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 784-789. DOI: 10.5220/0011775900003393

@conference{icaart23,
author={Alexei Lisitsa. and Mateo Salles. and Alexei Vernitski.},
title={Supervised Learning for Untangling Braids},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={784-789},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011775900003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Supervised Learning for Untangling Braids
SN - 978-989-758-623-1
IS - 2184-433X
AU - Lisitsa, A.
AU - Salles, M.
AU - Vernitski, A.
PY - 2023
SP - 784
EP - 789
DO - 10.5220/0011775900003393
PB - SciTePress