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{{More footnotes|date=January 2012}}
{{Artificial intelligence|Major goals}}
'''Automated [https://pesofts.com/how-to-do-automation-of-planning-schedule/ planning and scheduling]''', sometimes denoted as simply '''AI planning''',<ref>{{Citation | last1=Ghallab | first=Malik | last2=Nau | first2=Dana S. | last3=Traverso | first3=Paolo | title=Automated Planning: Theory and Practice | publisher=[[Morgan Kaufmann]] | year=2004 | url=http://www.laas.fr/planning/ | isbn=1-55860-856-7 | access-date=2008-08-20 | archive-date=2009-08-24 | archive-url=https://web.archive.org/web/20090824103124/http://www.laas.fr/planning/ | url-status=live }}</ref> is a branch of [[artificial intelligence]] that concerns the realization of [[strategy|strategies]] or action sequences, typically for execution by [[intelligent agent]]s, [[autonomous robot]]s and [[unmanned vehicle]]s. Unlike classical [[control system|control]] and [[Statistical classification|classification]] problems, the solutions are complex and must be discovered and optimized in multidimensional space. Planning is also related to [[decision theory]]. [https://pesofts.com/ Pesofts]
In known environments with available models, planning can be done offline. Solutions can be found and evaluated prior to execution. In dynamically unknown environments, the [[strategy]] often needs to be revised online. Models and policies must be adapted. Solutions usually resort to iterative [[trial and error]] processes commonly seen in [[artificial intelligence]]. These include [[dynamic programming]], [[reinforcement learning]] and [[combinatorial optimization]]. Languages used to describe planning and scheduling are often called [[action language]]s.
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