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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.07245 |
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| _version_ | 1866916431263694848 |
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| author | Niggemann, Oliver Biswas, Gautam Diedrich, Alexander Ehrhardt, Jonas Heesch, René Widulle, Niklas |
| author_facet | Niggemann, Oliver Biswas, Gautam Diedrich, Alexander Ehrhardt, Jonas Heesch, René Widulle, Niklas |
| contents | The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouver, Canada, brought together researchers to discuss recent advances in AI planning methods for Cyber-Physical Systems (CPS). CPS pose a major challenge due to their complexity and data-intensive nature, which often exceeds the capabilities of traditional planning algorithms. The workshop highlighted new approaches such as neuro-symbolic architectures, large language models (LLMs), deep reinforcement learning and advances in symbolic planning. These techniques are promising when it comes to managing the complexity of CPS and have potential for real-world applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AAAI Workshop on AI Planning for Cyber-Physical Systems -- CAIPI24 Niggemann, Oliver Biswas, Gautam Diedrich, Alexander Ehrhardt, Jonas Heesch, René Widulle, Niklas Artificial Intelligence The workshop 'AI-based Planning for Cyber-Physical Systems', which took place on February 26, 2024, as part of the 38th Annual AAAI Conference on Artificial Intelligence in Vancouver, Canada, brought together researchers to discuss recent advances in AI planning methods for Cyber-Physical Systems (CPS). CPS pose a major challenge due to their complexity and data-intensive nature, which often exceeds the capabilities of traditional planning algorithms. The workshop highlighted new approaches such as neuro-symbolic architectures, large language models (LLMs), deep reinforcement learning and advances in symbolic planning. These techniques are promising when it comes to managing the complexity of CPS and have potential for real-world applications. |
| title | AAAI Workshop on AI Planning for Cyber-Physical Systems -- CAIPI24 |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.07245 |