Publications

State transitions unlock temporal memory in swarm-based reservoir computing

Published in PeerJ Computer Science, 2026

We show that simple state transitions can unlock substantial temporal memory in swarm-based reservoir computing, with memory capacity scaling linearly with swarm size across the tested range.

Recommended citation: Lund, T., Adams, A., Aubert-Kato, N., & Ikegami, T. (2026). State transitions unlock temporal memory in swarm-based reservoir computing. PeerJ Computer Science, 12:e3763. https://doi.org/10.7717/peerj-cs.3763 https://doi.org/10.7717/peerj-cs.3763

aRtificiaL death: learning from stories of failure

Published in ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference, 2023

Sharing stories, particularly about death, is an important part of many cultures. We explore this concept in artificial agents via Reinforcement Learning.

Recommended citation: Marcin Korecki, Cesare Carissimo, Tanner Lund; July 24–28, 2023. "aRtificiaL death: learning from stories of failure." Proceedings of the ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. ALIFE 2023: Ghost in the Machine: Proceedings of the 2023 Artificial Life Conference. Online. (pp. 41). ASME. https://doi.org/10.1162/isal_a_00633 https://direct.mit.edu/isal/proceedings/isal/35/41/116928

Automata Quest: NCAs as a Video Game Life Mechanic

Published in Alife for and from video games workshop at ALIFE2023, 2023

We investigate the viability of one popular form of automata, namely Neural Cellular Automata, as a way to more fully express life within video game settings and innovate new game mechanics or gameplay loops.

Recommended citation: Sato, H., & Lund, T., & Yoshida, T. (2023, August) Automata Quest: NCAs as a Video Game Life Mechanic. Presented at Alife for and from video games workshop at ALIFE2023. MIT Press. https://arxiv.org/abs/2309.14364

Practical data mining and analysis for system administration

Published in 2015 ASEE Annual Conference & Exposition, 2015

Modern networks are both complex and important, requiring vigilant system administration. By implementing a practical data mining infrastructure, we are able to analyze device data and logs as part of general administrative tasks.

Recommended citation: Lund, T., & Panike, H., & Moses, S., & Rowe, D. C., & Ekstrom, J. J. (2015, June), Practical Data Mining and Analysis for System Administration Paper presented at 2015 ASEE Annual Conference & Exposition, Seattle, Washington. 10.18260/p.24570 https://peer.asee.org/practical-data-mining-and-analysis-for-system-administration