Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems-Reference-Cited by-同舟云学术

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Learning Process Steps as Dynamical Systems for a Sub-Symbolic Approach of Process Planning in Cyber-Physical Production Systems

Published:2024 Issue: Volume: Page:332-345
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Ehrhardt Jonas^ORCID,Heesch René^ORCID,Niggemann Oliver^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-50485-3_34

Reference27 articles.

1. Amado, L., Pereira, R.F., Meneguzzi, F.: Robust neuro-symbolic goal and plan recognition. Proc. AAAI Conf. Artif. Intell. 37(10), 11937–11944 (2023)

2. Ardizzone, L., Kruse, J., Rother, C., Köthe, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations (2019)

3. Asai, M., Muise, C.: Learning neural-symbolic descriptive planning models via cube-space priors: the voyage home (to strips) (2020)

4. Balzereit, K., Niggemann, O.: Autoconf a new algorithm for reconfiguration of cyber-physical production systems. IEEE Trans. Industr. Inf. 19(1), 739–749 (2023)

5. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence);A Bit-Monnot,2019

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