research-article

Combining Machine Learning With Inductive Logic Learning To Detect Deviations From Daily Routines In Ambient Intelligent Environments

Authors:

Benjamin Duppe,

Michael Meiser,

Alexander Anisimov,

André Antakli,

Ingo ZinnikusAuthors Info & Claims

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

Pages 310 - 317

https://doi.org/10.1145/3486622.3493942

Published: 13 April 2022 Publication History

Abstract

The availability of sensor technology in home and building automation offers new opportunities for AI applications. Machine learning (ML) methods can recognize device patterns and profiles based on sensor data and make energy-relevant predictions. At a higher level, the detected patterns can in turn be used to learn activity patterns. In this paper, we present a hybrid approach to augment ML-based outputs with inductive logic-based learning techniques. We present evaluation results and illustrate an application to agent-supported critical situation detection in the assisted living domain.

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Cited By

Duppe B(2024)Logic Supervised Learning for Time Series - Continual Learning for Appliance DetectionNeural-Symbolic Learning and Reasoning10.1007/978-3-031-71170-1_4(32-40)Online publication date: 10-Sep-2024
https://doi.org/10.1007/978-3-031-71170-1_4
Meiser MDuppe BZinnikus I(2023)SynTiSeD – Synthetic Time Series Data Generator2023 11th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES)10.1109/MSCPES58582.2023.10123429(1-6)Online publication date: 9-May-2023
https://doi.org/10.1109/MSCPES58582.2023.10123429

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cover image ACM Conferences

WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology

December 2021

698 pages

ISBN:9781450391153

DOI:10.1145/3486622

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 April 2022

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German Federal Ministry for Economic Affairs and Energy

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WI-IAT '21

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WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence

December 14 - 17, 2021

VIC, Melbourne, Australia

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Cited By

Duppe B(2024)Logic Supervised Learning for Time Series - Continual Learning for Appliance DetectionNeural-Symbolic Learning and Reasoning10.1007/978-3-031-71170-1_4(32-40)Online publication date: 10-Sep-2024
https://doi.org/10.1007/978-3-031-71170-1_4
Meiser MDuppe BZinnikus I(2023)SynTiSeD – Synthetic Time Series Data Generator2023 11th Workshop on Modelling and Simulation of Cyber-Physical Energy Systems (MSCPES)10.1109/MSCPES58582.2023.10123429(1-6)Online publication date: 9-May-2023
https://doi.org/10.1109/MSCPES58582.2023.10123429

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