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→Contingent planning: Russel| and Norvig 2021 call this "contingency planning" |
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An early example of a conditional planner is “Warplan-C” which was introduced in the mid 1970s.<ref>{{cite conference |title=Conditional nonlinear planning |author=Peot, Mark A and Smith, David E |conference=Artificial Intelligence Planning Systems |pages=189–197 |year=1992 |publisher=Elsevier|url=https://sites.google.com/site/markpeot2/peot92conditional.pdf}}</ref> What is the difference between a normal sequence and a complicated plan, which contains if-then-statements? It has to do with uncertainty at [[Run time (program lifecycle phase)|runtime]] of a plan. The idea is that a plan can react to [[Soft sensor|sensor signals]] which are unknown for the planner. The planner generates two choices in advance. For example, if an object was detected, then action A is executed, if an object is missing, then action B is executed.<ref>{{cite conference |title=Conditional progressive planning under uncertainty |author=Karlsson, Lars |conference=IJCAI |pages=431–438 |year=2001|url=https://www.researchgate.net/publication/2927504}}</ref> A major advantage of conditional planning is the ability to handle [[Partial-order planning|partial plans]].<ref>{{cite tech report |title=A survey of planning in intelligent agents: from externally motivated to internally motivated systems |author=Liu, Daphne Hao |year=2008 |institution=Technical Report TR-2008-936, Department of Computer Science, University of Rochester |url=http://urresearch.rochester.edu/fileDownloadForInstitutionalItem.action?itemId=5342&itemFileId=8258 |access-date=2019-08-16 |archive-date=2023-03-15 |archive-url=https://web.archive.org/web/20230315180735/https://urresearch.rochester.edu/fileDownloadForInstitutionalItem.action?itemId=5342&itemFileId=8258 |url-status=live }}</ref> An agent is not forced to plan everything from start to finish but can divide the problem into [[Chunking (computational linguistics)|chunks]]. This helps to reduce the state space and solves much more complex problems.
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We speak of "contingent planning" when the environment is observable through sensors, which can be faulty. It is thus a situation where the planning agent acts under incomplete information. For a contingent planning problem, a plan is no longer a sequence of actions but a [[decision tree]] because each step of the plan is represented by a set of states rather than a single perfectly observable state, as in the case of classical planning.<ref>{{Cite conference|conference=International Joint Conference of Artificial Intelligence (IJCAI)|year=2009|author1=Alexandre Albore|author2=Hector Palacios|author3=Hector Geffner|title=A Translation-Based Approach to Contingent Planning|publisher=AAAI|___location=Pasadena, CA|url=http://www.aaai.org/ocs/index.php/IJCAI/IJCAI-09/paper/download/587/852|access-date=2019-07-03|archive-date=2019-07-03|archive-url=https://web.archive.org/web/20190703164319/http://www.aaai.org/ocs/index.php/IJCAI/IJCAI-09/paper/download/587/852|url-status=dead}}</ref> The selected actions depend on the state of the system. For example, if it rains, the agent chooses to take the umbrella, and if it doesn't, they may choose not to take it.
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