Patent Application
20240029570
The present disclosure relates generally to improvements to intelligent decision makers, and more particularly to an outcome assessment model deployable in conjunction with an intelligent decision maker operable to evaluate and predict expected performance of the decision maker, among other functions.
Missions such as aircraft fleet missions can be complex. To achieve mission success, operators (e.g., pilots) must be able to effectively manage numerous manned and unmanned aircraft while maintaining awareness of the constantly changing environment and assets under control. To alleviate operator workload, decision makers have been developed that make intelligent recommendations using artificial intelligence (AI).
AI-based decision makers are typically trained in a simulation environment using machine learning processes such as reinforcement learning (RL). In use, AI-based decision makers process stored and received data to recommend actions to be taken by an operator in real-time, for example, to complete a task, an objective, a mission, etc. Feedback of successes and failures may be part of the machine learning process and retraining.
While useful for managing complex missions, AI-based decision makers provide recommended actions without presenting the analytical data upon which the conclusion was based. As such, an operator must choose to follow or disregard a recommended action based on faith or lack thereof in the programming. Without having real-time access to complete information, the operator is not able to effectively evaluate key outcome likelihoods or perform “what if” scenarios in real time.
Therefore, what is needed is an analytic tool deployable in conjunction with an intelligent decision maker such that an operator is better informed to make decisions in real-time, for instance when executing a task or mission.
According to a first inventive aspect, the present disclosure provides a computer-based system for deployment in an aircraft. The system includes an intelligent decision maker, trained in a simulation environment based on training data, and including processing circuitry configured to generate, in real-time, a recommended action to be taken to complete a task based on the training data and state data and display, by a first display, the recommended action to be taken to complete the task. The system further includes an outcome assessor deployable in conjunction with the intelligent decision maker and including processing circuitry configured to communicate with the intelligent decision maker, generate, in real-time, a probability estimate of an outcome of a scenario based on the training data gathered from the intelligent decision maker and altered state data, and display, by a second display, the probability estimate.
In some embodiments, the state data includes data associated with a current state of assets under control, data associated with a current state of the operating environment, and data associated with the task, and the altered state data includes at least one of altered data associated with the current state of assets under control, altered data associated with the current state of the operating environment, and altered data associated with the task.
In some embodiments, the outcome assessor is further configured to determine from the decision maker the training data and the state data upon which the generated recommended action to complete a task was based and cause the second display to display the determined training data and the state data.
In some embodiments, the outcome assessor is further configured to categorize each of the state data as positive contributing data or negative contributing data in the generated probability estimate, and display, by the second display, each of the categorized positive contributing data and the negative contributing data.
In some embodiments, the altered state data is a altered version of the state data upon which the displayed recommended action to be taken to complete the task was based.
In some embodiments, the second display is an interactive display operative to input the altered state data.
In some embodiments, the system is implemented in an aircraft cockpit.
According to another aspect the present disclosure provides a computer-implemented method for implementation in an aircraft, for instance in an aircraft cockpit. The method includes providing an intelligent decision maker trained in a simulation environment based on training data and generating a recommended action to be taken to complete a task based on the training data and state data. The method further includes providing an outcome assessor deployed in conjunction with the intelligent decision maker and gathering the training data and the state data from the intelligent decision maker and generating a probability estimate of an outcome of a scenario based on the gathered training data and altered state data. The recommended action and the probability estimate are displayed on separate displays such as separate screens of the same display.
Implementations of the inventive concepts disclosed herein may be better understood when consideration is given to the following detailed description thereof. Such description refers to the included drawings, which are not necessarily to scale, and in which some features may be exaggerated, and some features may be omitted or may be represented schematically in the interest of clarity. Like reference numerals in the drawings may represent and refer to the same or similar element, feature, or function. In the drawings:
Before explaining at least one embodiment of the inventive concepts disclosed herein in detail, it is to be understood that the inventive concepts are not limited in their application to the details of construction and the arrangement of the components or steps or methodologies set forth in the following description or illustrated in the drawings. In the following detailed description of embodiments of the instant inventive concepts, numerous specific details are set forth in order to provide a more thorough understanding of the inventive concepts. However, it will be apparent to one of ordinary skill in the art having the benefit of the instant disclosure that the inventive concepts disclosed herein may be practiced without these specific details. In other instances, well-known features may not be described in detail to avoid unnecessarily complicating the instant disclosure. The inventive concepts disclosed herein are capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting.
As used herein a letter following a reference numeral is intended to reference an embodiment of the feature or element that may be similar, but not necessarily identical, to a previously described element or feature bearing the same reference numeral (e.g., 1, 1a, 1b). Such shorthand notations are used for purposes of convenience only and should not be construed to limit the inventive concepts disclosed herein in any way unless expressly stated to the contrary.
Further, unless expressly stated to the contrary, “or” refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by anyone of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).
In addition, use of the “a” or “an” are employed to describe elements and components of embodiments of the instant inventive concepts. This is done merely for convenience and to give a general sense of the inventive concepts, thus “a” and “an” are intended to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.
Finally, as used herein any reference to “one embodiment,” or “some embodiments” means that a particular element, feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the inventive concepts disclosed herein. The appearances of the phrase “in some embodiments” in various places in the specification are not necessarily all referring to the same embodiment, and embodiments of the inventive concepts disclosed may include one or more of the features expressly described or inherently present herein, or any combination of sub-combination of two or more such features, along with any other features which may not necessarily be expressly described or inherently present in the instant disclosure.
Broadly, the present disclosure provides embodiments of an outcome assessor, also referred to herein as an outcome assessment model or module (OAM), deployed in conjunction with an intelligent decision maker, for instance an artificial intelligence-based decision maker (AIDM). Applications include, but are not limited to, mission reasoner pilot aids and autonomy systems. In embodiments, the OAM is operable to convey data and probability outcomes associated with AIDM performance and outcome scenarios, for example, to determine acceptability of AIDM recommended actions in real-time. In embodiments, the OAM is developed to model the AIDM interaction with the current environment to generate outcome probability estimates, allowing the OAM to assess the AIDM effectiveness in a scenario. Providing the ability to change a scenario further enables an operator, for instance an aircraft pilot, to evaluate “what-if” scenarios. Combining the OAM with other explainability methods allows an operator to understand why the OAM determines a scenario is likely or unlikely to result in a particular outcome. The OAM of the present disclosure may be used in conjunction with and is applicable to various applications involving a learning-based decision maker.
Inventive aspects of the present disclosure include, but are not limited to, OAM development using supervised machine learning to predict AIDM expected performance in metrics understandable to an operator based on simulation results, using an OAM to evaluate AIDM performance in “what-if” scenarios to make informed “GO” or “NO GO” decisions, and using explainability methods to provide causal reasoning for operational state to AIDM expected performance.
An OAM according to the present disclosure can be developed for any outcome of interest including, but not limited to, mission success, resource health, fuel consumption, and timeliness. Benefits of the inventive aspects include, but are not limited to, generation of outcome predictions in real-time such that an operator can make an informed decision whether to follow or disregard an AIDM recommended action, generation of odds of success comparisons in ideal scenarios versus non-ideal scenarios (e.g., a resource is damaged or a weather event occurs). Causes of predictions may be explained to the operator in real time through explainability methods.
Computer-implemented systems and methods according to the present disclosure can be implemented in a computer environment generally including hardware processors, programmable logic devices, microcontrollers, memory devices and other hardware components configured to perform the tasks described herein. Other hardware components may include coupled memory devices configured to store instructions executable by the one or more hardware processors. Processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), field programmable gate arrays (FPGAs), and application specific integrated circuit (ASICs). Memory devices may include, but are not limited to, random access memory (RAM), read-only memory (ROM) and other memory devices configured to store data and processor instructions for implementing various functionalities described herein. For example, the hardware processors may execute computer instructions stored in the memory device.
Regarding the AIDM, in some embodiments the AIDM is an intelligent decision maker trained in a simulation environment, implemented as a module sited onboard an aircraft, and tasked with decision making during a mission. The AIDM may receive input from a vehicle health system, mission phase analysis, and external assets to build an explainable decision network based on a knowledge database providing a decision aid. The AIDM operates to recommend actions to complete one or more tasks or mission objectives.
In embodiments, the AIDM includes or communicates with an interactive interface such as a human machine interface (HMI) onboard the aircraft. The HMI may include a display (e.g., a first display) including a screen and equipped with voice recognition, communications, aural warnings, and operator input output (I/O) functionality. The AIDM may be operable to assess a mission, assess asset health, analyze tasks associated with a mission, etc. The AIDM may communicate with a multi-vehicle health interface configured to monitor vehicle status of a plurality of vehicles offboard the aircraft, for instance unmanned drones.
In use, the AIDM operates to provide recommended actions to be taken to enhance task and/or mission success. Recommended actions may include “GO” decisions, “NO-GO” decisions, re-tasking decisions, and re-planning decisions, each dynamically updated during the mission. Actions are recommended based on the training data from the AIDM simulation training and received data from various state sources including, but not limited to, current state of assets under control (e.g., health of manned and unmanned aircraft, capabilities, fuel, etc.), current state of the environment (e.g., weather, hostiles, etc.), and mission metrics. The AIDM operates to analyze the current states with the mission phase to determine the recommended actions such as continue as planned, re-task, re-plan, etc.
The operator has the ability to perform “what-if” scenarios by altering the environment and resource states. Thus, the probability estimates generated by the OAM can be performed with or without altered state data for comparison purposes. For example, an altered scenario may include removing a resource from a mission, and the probability estimate with and without the resource can be generated by the OAM and compared to determine a change in the odds of mission success with and without the resource. The AIDM 100 and the OAM 200 are decoupled in that the AIDM continues to operate as trained when deployed, and the OAM performs simulations from data gathered from the AIDM without altering the data.
The determined probability estimate may cause the operator to follow the recommended action in the case of a high probability of success or cause the operator to make a change to the plan in the case of a low probability of success. Running a simulation based on an altered state returns a probability estimate for the altered state for consideration by the operator. The OAM conveys the probability estimate to the operator via a display. Altered simulations may be performed in real-time and the operator has the ability to modify states, revealed by the OAM, to run “what-if” scenarios. Probability estimates can be generated for task and/or objective completion. In some embodiments, the AIDM is configured to make step-by-step recommendations with allocations at each time step, and in parallel the OAM estimates the likelihood of task and/or mission success with explanation of the AIDM output.
According to the method of the present disclosure, processing circuitry of the OAM generates the probability estimate(s) of task or mission success. The processing circuitry further operates to output the probability estimate(s) to the operator display. The probability estimate may include the most likely estimate and the estimate may be refined according to operator input received through the display. As discussed above, the AIDM and OAM operate separately such that their respective outputs are separate and traceable such that the OAM output can be used to retrain the AIDM to improve the performance of the AIDM.
While the foregoing description provides embodiments of the invention by way of example only, it is envisioned that other embodiments may perform similar functions and/or achieve similar results. Any and all such equivalent embodiments and examples are within the scope of the present invention and are intended to be covered by the appended claims.
