calamity
发表于 2025-3-25 03:33:15
Maurício C. Magnaguagno,Ramon Fraga Pereira,Martin D. Móre,Felipe Meneguzzi
Magnitude
发表于 2025-3-25 07:33:15
http://reply.papertrans.cn/55/5439/543896/543896_22.png
Esalate
发表于 2025-3-25 15:18:28
http://reply.papertrans.cn/55/5439/543896/543896_23.png
BALE
发表于 2025-3-25 17:50:36
KEPS Book: Planning.DomainsIn this chapter we describe the main pillars of the Planning.Domains initiative (API, Solver, Editor, and Education), detail some of the current use-cases for them, and outline the future path of the initiative. We further dive into some of the most recent developments of Planning.Domains, and shed light on what is next for the platform.
幸福愉悦感
发表于 2025-3-25 23:14:16
http://reply.papertrans.cn/55/5439/543896/543896_25.png
无法取消
发表于 2025-3-26 01:38:06
Mauro Vallati,Diane KitchinThere is no up-to-date book which covers this topic area, only a few scattered research/conference papers which address this topic.Chapters writen by International leaders from both industry and acade
开头
发表于 2025-3-26 05:35:50
http://image.papertrans.cn/k/image/543896.jpg
Interdict
发表于 2025-3-26 10:59:09
https://doi.org/10.1007/978-3-030-38561-3Artificial Intelligence; AI Planning & Scheduling; Model-Based Reasoning; Knowledge Engineering; Knowled
invert
发表于 2025-3-26 15:49:56
http://reply.papertrans.cn/55/5439/543896/543896_29.png
要塞
发表于 2025-3-26 20:47:53
Automated Domain Model Learning Tools for Planning by human experts or automatically learned through the observation of some existing plans (behaviours). Encoding a domain model manually from experience and intuition is a very complex and time-consuming task, even for domain experts. This chapter investigates various classical and state-of-the-art