Abstract
For describing the complete state of complex environments with multiple mobile and autonomous agents, spatial layer models (SLM) are popular data structures. These models consist of several planes describing the spatial structure of selected features. Those SLM can directly be used for deep reinforcement learning tasks. However, detecting anomalies in such SLM poses two major challenges: the state space explosion in such settings and the spatial relations between the features. In this paper, we present a method for anomaly detection in SLM which solves both challenges by first extracting significant sub-patterns from training data and storing them in a dictionary. Afterwards, the entries of this dictionary are used for reconstructing SLM, which have to be validated. The resulting covering rate is an indicator for the (ab)normality of the given SLM. We show the applicability of our approach for a simple multi-agent scenario, and more complex smart factory scenarios with autonomous agents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, C., Su, H., Huang, Q., Zhang, L., Guibas, L.: Pathlet learning for compressing and planning trajectories. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 392–395. ACM (2013)
Gupta, J.K., Egorov, M., Kochenderfer, M.: Cooperative multi-agent control using deep reinforcement learning. In: Sukthankar, G., Rodriguez-Aguilar, J.A. (eds.) AAMAS 2017. LNCS (LNAI), vol. 10642, pp. 66–83. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71682-4_5
Kiermeier, M., Sauer, H., Wieghardt, J.: Monitoring self-organizing industrial systems using sub-trajectory dictionaries. In: IEEE 15th International Conference on Industrial Informatics (INDIN), pp. 665–670. IEEE (2017)
Kiermeier, M., Werner, M., Linnhoff-Popien, C., Sauer, H., Wieghardt, J.: Anomaly detection in self-organizing industrial systems using pathlets. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1226–1231. IEEE (2017)
Mnih, V., et al.: Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
Phan, T., Belzner, L., Gabor, T., Schmid, K.: Leveraging statistical multi-agent online planning with emergent value function approximation. In: Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, pp. 730–738 (2018)
Pimentel, M.A., Clifton, D.A., Clifton, L., Tarassenko, L.: A review of novelty detection. Signal Process. 99, 215–249 (2014)
Silver, D., et al.: Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Simon, H.D.: Partitioning of unstructured problems for parallel processing. Comput. Syst. Eng. 2(2–3), 135–148 (1991)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Kiermeier, M., Feld, S., Phan, T., Linnhoff-Popien, C. (2018). Anomaly Detection in Spatial Layer Models of Autonomous Agents. In: Yin, H., Camacho, D., Novais, P., Tallón-Ballesteros, A. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2018. IDEAL 2018. Lecture Notes in Computer Science(), vol 11314. Springer, Cham. https://doi.org/10.1007/978-3-030-03493-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-03493-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03492-4
Online ISBN: 978-3-030-03493-1
eBook Packages: Computer ScienceComputer Science (R0)