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1.发明授权公开(公告)号：US11880775B1
公开(公告)日：2024-01-23
申请号：US16000043
申请日：2018-06-05
CPC分类号： G06N5/022 , G05B13/028 , G05B13/04 , G06N7/01 , G05B17/02
摘要： Techniques for improved automated selection in computer-based reasoning systems are presented. The techniques include receiving context data for operation of a system, determining two or more candidate actions to take, each from a different computer-based reasoning model, and determining the surprisal of each. The surprisals are then compared and, in some embodiments, the one with the lowest surprisal is chosen. In some embodiments, this chosen action is performed on the system. In some embodiments, the chosen action is passed up a control hierarchy for consideration along with entropy and other factors, and the action chosen at that level is performed on the controlled system.
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公开(公告)号：US20230351228A1
公开(公告)日：2023-11-02
申请号：US18347408
申请日：2023-07-05
CPC分类号： G06N5/04 , G05D1/0088 , G05D1/0221 , G06F18/2415 , G06N7/01 , G06N20/00 , G05D1/0061 , G05D2201/0213
摘要： The techniques herein include using an input context to determine a suggested action. One or more explanations may also be determined and returned along with the suggested action. The one or more explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and / or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; (x) conviction ratio; (xi) contribution ratio; and / or other measures such as the ones discussed herein, including certainty. In some embodiments, the explanation data may be used to determine whether to perform a suggested action.
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公开(公告)号：US11783211B2
公开(公告)日：2023-10-10
申请号：US17900502
申请日：2022-08-31
摘要： Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic training data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, validity of the generated value may be checked based on feature information. In some embodiments, generated synthetic data may be checked against all or a portion of the training data to ensure that it is not overly similar.
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公开(公告)号：US11748635B2
公开(公告)日：2023-09-05
申请号：US17346583
申请日：2021-06-14
IPC分类号： G06N5/02 , G06N5/025 , G06F16/2458
CPC分类号： G06N5/025 , G06F16/2465
摘要： Techniques for detecting and correcting anomalies in computer-based reasoning systems are provided herein. The techniques can include obtaining current context data and determining a contextually-determined action based on the obtained context data and a reasoning model. The reasoning model may have been determined based on one or more sets of training data. The techniques may cause performance of the contextually-determined action and, potentially, receiving an indication that performing the contextually-determined action in the current context resulted in an anomaly. The techniques include determining a portion of the reasoning model that caused the determination of the contextually-determined action based on the obtained context data and causing removal of the portion of the model that caused the determination of the contextually-determined action, to produce a corrected reasoning model. Subsequently, second context data is obtained, a second action is determined based on that data and the corrected reasoning model, and the second contextually-determined action can be performed.
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5.发明公开公开(公告)号：US20230244954A1
公开(公告)日：2023-08-03
申请号：US18299524
申请日：2023-04-12
IPC分类号： G06N3/126
CPC分类号： G06N3/126
摘要： Techniques are provided for evolutionary computer-based optimization and artificial intelligence systems, and include receiving first and second candidate executable code (with ploidy of at least two and one, respectively) each selected at least in part based on a fitness score. If the desired ploidy of the resultant executable code is one, then the first candidate executable code and the second candidate executable code are combined to produce haploid executable code. If the desired ploidy is two, then the first candidate executable code and the second candidate executable code are combined to produce diploid executable code. A fitness score is determined for the resultant executable code, and a determination is made whether the resultant executable code will be used as a future candidate executable code based at least in part on the third fitness score. If an exit condition is met, then resultant executable code is used as evolved executable code.
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公开(公告)号：US11625625B2
公开(公告)日：2023-04-11
申请号：US16713714
申请日：2019-12-13
摘要： Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic training data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, validity of the generated value may be checked based on feature information. In some embodiments, generated synthetic data may be checked against all or a portion of the training data to ensure that it is not overly similar.
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公开(公告)号：US11385633B2
公开(公告)日：2022-07-12
申请号：US16992876
申请日：2020-08-13
发明人： Christopher James Hazard , Michael Resnick , Ravisutha Sakrepatna Srinivasamurthy , David R. Cheeseman , Ju Hyun Kim , Yamac Alican Isik
摘要： Techniques are provided herein for creating well-balanced computer-based reasoning systems and using those to control systems. The techniques include receiving a request to determine whether to use one or more particular data elements, features, cases, etc. in a computer-based reasoning model (e.g., as data elements, cases or features are being added, or as part of pruning existing features or cases). Conviction measures are determined and inclusivity conditions are tested. The result of comparing the conviction measure can be used to determine whether to include or exclude the feature, case, etc. in the model and/or whether there are anomalies in the model. A controllable system may then be controlled using the computer-based reasoning model. Examples controllable systems include self-driving cars, image labeling systems, manufacturing and assembly controls, federated systems, smart voice controls, automated control of experiments, energy transfer systems, health care systems, cybersecurity systems, and the like.
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公开(公告)号：US20210326652A1
公开(公告)日：2021-10-21
申请号：US17333671
申请日：2021-05-28
发明人： Christopher James Hazard , Jacob Beel , Yash Shah , Ravisutha Sakrepatna Srinivasamurthy , Michael Resnick
摘要： Techniques for synthetic data generation in computer-based reasoning systems are discussed and include receiving a request for generation of synthetic data based on a set of training data cases. One or more focal training data cases are determined. For undetermined features (either all of them or those that are not subject to conditions), a value for the feature is determined based on the focal cases. In some embodiments, the generated synthetic data may be checked for similarity against the training data, and if similarity conditions are met, it may be modified (e.g., resampled), removed, and/or replaced.
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公开(公告)号：US11092962B1
公开(公告)日：2021-08-17
申请号：US15817627
申请日：2017-11-20
摘要： Techniques are provided for operational situation vehicle control, and include determining action and context data for one or more vehicle operations in one or more operational situations, training vehicle control rules for those operational situations, and using those vehicle control rules to control vehicles in compatible operational situations.
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公开(公告)号：US20200151598A1
公开(公告)日：2020-05-14
申请号：US16205431
申请日：2018-11-30
摘要： The techniques herein include using an input context to determine a suggested action. One or more explanations may also be determined and returned along with the suggested action. The one or more explanations may include (i) one or more most similar cases to the suggested case (e.g., the case associated with the suggested action) and, optionally, a conviction score for each nearby cases; (ii) action probabilities, (iii) excluding cases and distances, (iv) archetype and/or counterfactual cases for the suggested action; (v) feature residuals; (vi) regional model complexity; (vii) fractional dimensionality; (viii) prediction conviction; (ix) feature prediction contribution; and/or other measures such as the ones discussed herein, including certainty. In some embodiments, the explanation data may be used to determine whether to perform a suggested action.
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