探索AI规划在复杂物理系统中的新方法，助力现实应用。
AAAI Workshop on AI Planning for Cyber-Physical Systems -- CAIPI24
- 融合神经符号、大模型与强化学习提升规划能力
- 针对复杂数据系统提出高效算法，突破传统方法局限
- 适合关注智能系统与实际落地的科研与工程人员
2024年2月26日，第38届国际人工智能会议（AAAI 2024）在加拿大温哥华举行，'基于人工智能的网络物理系统规划'研讨会汇聚研究者，探讨面向网络物理系统（CPS）的AI规划最新进展。由于CPS具有高度复杂性与数据密集特性，传统规划算法常难应对。研讨会聚焦神经符号架构、大语言模型（LLMs）、深度强化学习及符号规划的最新突破，这些方法在处理系统复杂性方面展现出潜力，具备广泛的实际应用前景。
原文摘要 · Abstract (English)
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.
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