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公开(公告)号：US20200065687A1
公开(公告)日：2020-02-27
申请号：US16670098
申请日：2019-10-31
IPC分类号： G06N5/04
摘要： A mechanism is provided for computing a solution to a plan recognition problem. The plan recognition problem includes the model and a partially ordered sequence of observations or traces. The plan recognition is transformed into an AI planning problem such that a planner can be used to compute a solution to it. The approach is general. It addresses unreliable observations: missing observations, noisy observations (or observations that need to be discarded), and ambiguous observations). The approach does not require plan libraries or a possible set of goals. A planner can find either one solution to the resulting planning problem or multiple ranked solutions, which maps to the most plausible solution to the original problem.
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公开(公告)号：US20210004741A1
公开(公告)日：2021-01-07
申请号：US16459324
申请日：2019-07-01
摘要： Embodiments are provided for providing top-K quality plans streaming applications in a computing environment. A set of top-K quality plans using a quality bound for a planning problem. The planning problem may be reformulated in one or more subsequent iterations and forbidding use one or more of the set of top-K quality plans. Identifying one or more of the set top-K quality plans having a quality less than the quality bound during the one or more subsequent iterations.
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公开(公告)号：US20180285770A1
公开(公告)日：2018-10-04
申请号：US15475551
申请日：2017-03-31
摘要： Embodiments for learning personalized actionable domain models by a processor. A domain model may be generated according to a plurality of actions, extracted from one or more online data sources, of a plurality of cluster representatives. The plurality of actions achieve a goal. A hierarchical action model may be generated based on probabilities of the domain model and the plurality of actions. The hierarchical action model comprises a sequence of actions of the plurality of actions for achieving the goal. The hierarchical action model may be personalized by filtering to a selected set of actions according to weighted actions of the plurality of actions.
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公开(公告)号：US20170147923A1
公开(公告)日：2017-05-25
申请号：US14962714
申请日：2015-12-08
CPC分类号： G06N5/045
摘要： A mechanism is provided for computing a solution to a plan recognition problem. The plan recognition problem includes the model and a partially ordered sequence of observations or traces. The plan recognition is transformed into an AI planning problem such that a planner can be used to compute a solution to it. The approach is general. It addresses unreliable observations: missing observations, noisy observations (or observations that need to be discarded), and ambiguous observations). The approach does not require plan libraries or a possible set of goals. A planner can find either one solution to the resulting planning problem or multiple ranked solutions, which maps to the most plausible solution to the original problem.
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公开(公告)号：US20210142197A1
公开(公告)日：2021-05-13
申请号：US16679434
申请日：2019-11-11
IPC分类号： G06N7/00 , G06F16/901
摘要： Embodiments for creating planning tasks are provided. A plurality of atoms are generated. The plurality of atoms are partitioned into a plurality of variables. A casual graph is generated based on the plurality of variables. A layered graph including interchanging variable value layers and action layers is created based on the casual graph. A planning task is generated based on the layered graph.
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6.发明申请公开(公告)号：US20190340525A1
公开(公告)日：2019-11-07
申请号：US15971911
申请日：2018-05-04
IPC分类号： G06N5/04 , G05B13/04 , G06F8/30 , G05B19/042
摘要： A method for improving performance of at least one hardware processor solving a top-k planning problem includes obtaining, in a memory coupled to the at least one processor, a specification of the planning problem in a planning language; obtaining, in a first iteration carried out by the at least one processor, at least one solution to the planning problem; and modifying the planning problem, in the first iteration carried out by the at least one processor, to forbid the at least one solution. The method further includes repeating, by the at least one processor, the obtaining of the at least one solution and the modifying to forbid the at least one solution, for a plurality of additional iterations, after the first iteration, until a desired number, k, of solutions to the planning problem are found or until no further solutions exist, whichever comes first.
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