Entropy-based techniques for creation of well-balanced computer based reasoning systems

A system for creating a computer-based reasoning model, comprising:

• a training and analysis system executing on one or more computing devices, and configured to execute training and analysis instructions, which, when executed, perform the steps of:
• receiving a request to determine whether to include one or more particular features of data elements in a computer-based reasoning model;
• determining a first Probability Density or Mass Function (PDMF) for a set of data elements that does not include the one or more particular features of data elements, wherein the set of data elements is associated with the computer-based reasoning model;
• determining a second PDMF for the set of data elements combined with the one or more particular features of data elements;
• determining surprisal of the one or more particular features of data elements based on a ratio of the first PDMF and the second PDMF;
• in response to determining that the surprisal of the one or more particular features of data elements is out of bounds of one or more thresholds, including the one or more particular features of data elements in the computer-based reasoning model;
• in response to determining that the surprisal of the one or more particular features of data elements is not out of bounds of the one or more thresholds;
• excluding the one or more particular features of data elements from the computer-based reasoning model;
• sending the computer-based reasoning model to a control system;
• a control system executing on the one or more computing devices, configured to communicate with the training and analysis system and to execute control system instructions, which, when executed, perform the steps of:
• receiving the computer-based reasoning model from the training and analysis system;
• receiving a current context for a target system, wherein the target system can be controlled by the control system;
• determining an action to take for the target system based on the current context for the target system and the computer-based reasoning model;
• causing the target system to perform the determined action.

A system for creating computer-based reasoning model for controlling vehicles, comprising:

• a vehicle training and analysis system executing on one or more computing devices, and configured to execute training and analysis instructions, which, when executed, perform the steps of:
• receiving a request to determine whether to include one or more particular features of vehicular data elements in a vehicular computer-based reasoning model;
• determining a first Probability Density or Mass Function (PDMF) for a set of vehicular data elements that does not include the one or more particular features of vehicular data elements, wherein the set of vehicular data elements is associated with the vehicular computer-based reasoning model;
• determining a second PDMF for the set of vehicular data elements combined with the one or more particular features of vehicular data elements,
• determining surprisal of the one or more particular features of vehicular data elements based on a ratio of the first PDMF and the second PDMF;
• in response to determining that the surprisal of the one or more particular features of vehicular data elements is out of bounds of one or more thresholds, including the one or more particular features of vehicular data elements in the computer-based reasoning model;
• in response to determining that the surprisal of the one or more particular features of vehicular data elements is not out of bounds of the one or more thresholds, excluding the one or more particular features of vehicular data elements from the vehicular computer-based reasoning model;
• sending the vehicular computer-based reasoning model to a control system;
• a control system executing on the one or more computing devices, configured to communicate with the vehicle training and analysis system and to execute control system instructions, which, when executed, perform the steps of:
• receiving the vehicular computer-based reasoning model from the vehicle training and analysis system;
• receiving a current context for a vehicle, control of which can be performed by the control system;
• determining an action to take for the vehicle based on the current context for the vehicle and the vehicular computer-based reasoning model;
• causing the vehicle to perform the determined action.