1. A method comprising: obtaining, using one or more computing devices, context data for a current context of a self-driving vehicle; determining, using the one or more computing devices, a contextually-determined action for the self-driving vehicle based on the obtained context data and a reasoning model, wherein the reasoning model was determined based on multiple sets of training data, wherein the multiple sets of training data include multiple context data and action data pairings, and wherein determining the contextually-determined action for the self-driving vehicle comprises determining, using a premetric, closest context data in the multiple sets of training data that is closest to the current context based on the premetric and determining an action paired with the closest context data as the contextually-determined action for the self-driving vehicle, wherein the premetric is a Minkowski distance measure of order zero; determine, using the one or more computing devices, whether performance of the contextually-determined action results in an indication of an anomaly for the self-driving vehicle; determining, using the one or more computing devices, a portion of the reasoning model that caused the determination of the contextually-determined action that resulted in the indication of the anomaly for the self-driving vehicle based on the obtained context data; updating, using the one or more computing devices, the portion of the reasoning model that caused the determination of the contextually-determined action that resulted in the indication of the anomaly for the self-driving vehicle, in order to produce a corrected reasoning model; obtaining, using the one or more computing devices, subsequent contextual data for a second context for the self-driving vehicle; determining, using the one or more computing devices, a second contextually-determined action for the self-driving vehicle based on the obtained subsequent contextual data and the corrected reasoning model; and causing performance, using the one or more computing devices, of the second contextually-determined action for the self-driving vehicle.
1. A method comprising: obtaining, using one or more computing devices, context data for a current context of a self-driving vehicle; determining, using the one or more computing devices, a contextually-determined action for the self-driving vehicle based on the obtained context data and a reasoning model, wherein the reasoning model was determined based on multiple sets of training data, wherein the multiple sets of training data include multiple context data and action data pairings, and wherein determining the contextually-determined action for the self-driving vehicle comprises determining, using a premetric, closest context data in the multiple sets of training data that is closest to the current context based on the premetric and determining an action paired with the closest context data as the contextually-determined action for the self-driving vehicle, wherein the premetric is a Minkowski distance measure of order zero; determine, using the one or more computing devices, whether performance of the contextually-determined action results in an indication of an anomaly for the self-driving vehicle; determining, using the one or more computing devices, a portion of the reasoning model that caused the determination of the contextually-determined action that resulted in the indication of the anomaly for the self-driving vehicle based on the obtained context data; updating, using the one or more computing devices, the portion of the reasoning model that caused the determination of the contextually-determined action that resulted in the indication of the anomaly for the self-driving vehicle, in order to produce a corrected reasoning model; obtaining, using the one or more computing devices, subsequent contextual data for a second context for the self-driving vehicle; determining, using the one or more computing devices, a second contextually-determined action for the self-driving vehicle based on the obtained subsequent contextual data and the corrected reasoning model; and causing performance, using the one or more computing devices, of the second contextually-determined action for the self-driving vehicle. 3. The method of claim 1 , wherein determining the portion of the reasoning model that caused the determining of the contextually-determined action that resulted in the indication of the anomaly comprises determining the previously-identified closest context data in the training data.