AEPIS: Agent-Enabled Planning at Scale

Schmid S, Gui Z, Freund M, Wehr T, Harth A (2025)

Publication Type: Conference contribution

Publication year: 2025

Publisher: ACM

Pages Range: 5-8

Conference Proceedings Title: Proceedings of the 13th Knowledge Capture Conference 2025

Event location: Dayton US

DOI: 10.1145/3731443.3771341

Abstract

We investigate the use of Web agents that use planning to achieve goals involving preconditions on the dynamic Web. We implement our approach, AEPIS, based on Semantic Web technologies and rely on environmental information evaluated exclusively at runtime to take dynamic changes into account. We evaluate AEPIS in static and dynamic environments, based on the Web of Things, and compare its performance to a rule-based and an agent using large language models. We find that AEPIS combines adaptivity with rapid planning at scale, even in a dynamic environment.

Authors with CRIS profile

Sebastian Schmid Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Zhou Gui Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Michael Freund Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Thomas Wehr Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme Andreas Harth Lehrstuhl für Wirtschaftsinformatik, insbesondere Technische Informationssysteme

How to cite

APA:

Schmid, S., Gui, Z., Freund, M., Wehr, T., & Harth, A. (2025). AEPIS: Agent-Enabled Planning at Scale. In Proceedings of the 13th Knowledge Capture Conference 2025 (pp. 5-8). Dayton, US: ACM.

MLA:

Schmid, Sebastian, et al. "AEPIS: Agent-Enabled Planning at Scale." Proceedings of the 13th Knowledge Capture Conference 202, Dayton ACM, 2025. 5-8.

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