Patents by Inventor Joshua G. Fadaie
Joshua G. Fadaie has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20250232092Abstract: An example includes a method of training a first computational model to emulate a second computational model. The method includes using the first computational model to generate a first output in response to receiving an input and selecting a reward based on whether a difference between the first output and a second output is less than a threshold. The second output is generated by the second computational model in response to receiving the input. The method further includes updating the first computational model using the reward.Type: ApplicationFiled: January 16, 2024Publication date: July 17, 2025Inventors: Deepak Khosla, Joshua G. Fadaie, Charles Tullock, Nathan Hemming, Arya Ketabchi Haghighat
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Patent number: 12282839Abstract: A method including receiving a pre-determined constraint on user actions. A constraint vector is generated based on the pre-determined constraint. The constraint vector is input into a machine learning model. A first output is generated from the machine learning model by executing the machine learning model using the constraint vector as a first input to the machine learning model. The constraint vector is converted into a legal action mask. A probability vector is generated by executing a masked softmax operator. The masked softmax operator takes, as a second input, the first output. The masked softmax operator takes, as a third input, the legal action mask. The masked softmax operator generates, as a second output, the probabilities vector. Action outputs are generated by applying a sampling system to the probability vector. The action outputs include a subset of the user actions, and wherein the subset includes only allowed user actions.Type: GrantFiled: November 17, 2021Date of Patent: April 22, 2025Assignee: The Boeing CompanyInventors: Joshua G. Fadaie, Richard Hanes
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Publication number: 20240232611Abstract: An example includes a method for training an agent to control an aircraft. The method includes: selecting, by the agent, first actions for the aircraft to perform within a first environment respectively during first time intervals based on first states of the first environment during the first time intervals, updating the agent based on first rewards that correspond respectively to the first states, selecting, by the agent, second actions for the aircraft to perform within a second environment respectively during second time intervals based on second states of the second environment during the second time intervals, and updating the agent based on second rewards that correspond respectively to the second states. At least one first rule of the first environment is different from at least one rule of the second environment.Type: ApplicationFiled: October 25, 2022Publication date: July 11, 2024Inventors: Yang Chen, Fan Hung, Deepak Khosla, Sean Soleyman, Joshua G. Fadaie
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Publication number: 20240135167Abstract: An example includes a method for training an agent to control an aircraft. The method includes: selecting, by the agent, first actions for the aircraft to perform within a first environment respectively during first time intervals based on first states of the first environment during the first time intervals, updating the agent based on first rewards that correspond respectively to the first states, selecting, by the agent, second actions for the aircraft to perform within a second environment respectively during second time intervals based on second states of the second environment during the second time intervals, and updating the agent based on second rewards that correspond respectively to the second states. At least one first rule of the first environment is different from at least one rule of the second environment.Type: ApplicationFiled: October 24, 2022Publication date: April 25, 2024Inventors: Yang Chen, Fan Hung, Deepak Khosla, Sean Soleyman, Joshua G. Fadaie
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Patent number: 11586200Abstract: A method includes receiving, by machine-learning logic, observations indicative of a states associated with a first and second group of vehicles arranged within an engagement zone during a first interval of an engagement between the first and the second group of vehicles. The machine-learning logic determines actions based on the observations that, when taken simultaneously by the first group of vehicles during the first interval, are predicted by the machine-learning logic to result in removal of one or more vehicles of the second group of vehicles from the engagement zone during the engagement. The machine-learning logic is trained using a reinforcement learning technique and on simulated engagements between the first and second group of vehicles to determine sequences of actions that are predicted to result in one or more vehicles of the second group being removed from the engagement zone. The machine-learning logic communicates the plurality of actions to the first group of vehicles.Type: GrantFiled: June 22, 2020Date of Patent: February 21, 2023Assignees: The Boeing Company, HRL Laboratories LLCInventors: Joshua G. Fadaie, Richard Hanes, Chun Kit Chung, Sean Soleyman, Deepak Khosla
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Publication number: 20220214663Abstract: Solutions are provided for multi-agent autonomous instruction generation for manufacturing. An example includes: generating, for a plurality of actor agents, a first set of instructions for performing manufacturing tasks, wherein the actor agents include a human actor accessing a user interface (UI), an autonomous actor having a first sensor, a semi-autonomous actor having a second sensor, and a non-autonomous actor having a third sensor; receiving, by a control agent from at least the plurality of actor agents, observation data regarding performance of the actor agents on the manufacturing tasks, wherein the control agent comprises an autoregressive bidirectional long-term short-term memory (LSTM) attention network; and based at least on the instructions and the observation data, generating further instructions for performing manufacturing tasks. The instructions include at least one of a role assignment, platform control, tool selection, and tool utilization.Type: ApplicationFiled: December 6, 2021Publication date: July 7, 2022Inventor: Joshua G. Fadaie
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Publication number: 20220164636Abstract: A method including receiving a pre-determined constraint on user actions. A constraint vector is generated based on the pre-determined constraint. The constraint vector is input into a machine learning model. A first output is generated from the machine learning model by executing the machine learning model using the constraint vector as a first input to the machine learning model. The constraint vector is converted into a legal action mask. A probability vector is generated by executing a masked softmax operator. The masked softmax operator takes, as a second input, the first output. The masked softmax operator takes, as a third input, the legal action mask. The masked softmax operator generates, as a second output, the probabilities vector. Action outputs are generated by applying a sampling system to the probability vector. The action outputs include a subset of the user actions, and wherein the subset includes only allowed user actions.Type: ApplicationFiled: November 17, 2021Publication date: May 26, 2022Applicant: The Boeing CompanyInventors: Joshua G. Fadaie, Richard Hanes
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Publication number: 20210397179Abstract: A method includes receiving, by machine-learning logic, observations indicative of a states associated with a first and second group of vehicles arranged within an engagement zone during a first interval of an engagement between the first and the second group of vehicles. The machine-learning logic determines actions based on the observations that, when taken simultaneously by the first group of vehicles during the first interval, are predicted by the machine-learning logic to result in removal of one or more vehicles of the second group of vehicles from the engagement zone during the engagement. The machine-learning logic is trained using a reinforcement learning technique and on simulated engagements between the first and second group of vehicles to determine sequences of actions that are predicted to result in one or more vehicles of the second group being removed from the engagement zone. The machine-learning logic communicates the plurality of actions to the first group of vehicles.Type: ApplicationFiled: June 22, 2020Publication date: December 23, 2021Inventors: Joshua G. Fadaie, Richard Hanes, Chun Kit Chung, Sean Soleyman, Deepak Khosla
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Patent number: 11150670Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.Type: GrantFiled: May 28, 2019Date of Patent: October 19, 2021Assignee: The Boeing CompanyInventors: Deepak Khosla, Kevin R. Martin, Sean Soleyman, Ignacio M. Soriano, Michael A. Warren, Joshua G. Fadaie, Charles Tullock, Yang Chen, Shawn Moffit, Calvin Chung
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Publication number: 20200379486Abstract: Apparatus and methods for training a machine learning algorithm (MLA) to control a first aircraft in an environment that comprises the first aircraft and a second aircraft are described. Training of the MLA can include: the MLA determining a first-aircraft action for the first aircraft to take within the environment; sending the first-aircraft action from the MLA; after sending the first-aircraft action, receiving an observation of the environment and a reward signal at the MLA, the observation including information about the environment after the first aircraft has taken the first-aircraft action and the second aircraft has taken a second-aircraft action, the reward signal indicating a score of performance of the first-aircraft action based on dynamic and kinematic properties of the second aircraft; and updating the MLA based on the observation of the environment and the reward signal.Type: ApplicationFiled: May 28, 2019Publication date: December 3, 2020Inventors: Deepak Khosla, Kevin R. Martin, Sean Soleyman, Ignacio M. Soriano, Michael A. Warren, Joshua G. Fadaie, Charles Tullock, Yang Chen, Shawn Moffit, Calvin Chung