USA As Represented by Secretary of the Navy

Other

Michael W Walton

Benjamin J Migliori

Daniel J Gebhardt

Logan Straatemeier

Ronald John Wroblewski

John David Reeder

Maurice R Ayache

A system is provided for performing a predetermined function within a total area of operation, wherein the system includes a plurality of autonomous agents. Each autonomous agent is able to detect respective local parameters. Each autonomous agent uses a Kalman filter component to establish an environment state based a plurality of state measurements over time. The output of the Kalman filter component within a respective agent is applied to reinforcement learning by an actor-critic task controller, within the respective agent, to determine a subsequent action to be performed by the respective agent in accordance with a reward function. Each agent includes a Kalman consensus filter that addresses errors of the plurality of state measurements over time.

Method for performing multi-agent reinforcement learning in the presence of unreliable communications via distributed consensus

Motion abstraction includes ontologies having taxonomies for classification of various types of vessels (entities) and their movements based on inputted raw data. Kinematic-data abstraction, activity identification, entity classification, and entity identification, can be performed such that kinematic data is decomposed using an ontology describing motion, activities are decomposed using an ontology describing activities, and entity classes are decomposed using an ontology describing entity classes having unique-entity instances.

System and method for motion abstraction, activity identification, and vehicle classification

An asynchronous convolutional neural network (CNN) can interpret a sequence of input data. An input value representing a sample of the sequence of input data is received by a computational unit (CU) in a layer of the asynchronous CNN. The CU calculates a dot product of the input value and a weight assigned to the CU to produce an activation value. A change detector (CD) associated with the CU detects a difference between the activation value and previous activation values. The CD determines whether the detected difference is significant, indicating that the sample of the sequence of input data includes a significant change. If the detected difference is significant, the activation value is supplied to at least one subsequent CU included in a subsequent layer of the asynchronous CNN.

Asynchronous artificial neural network architecture

Time-varying input signals are denoised by a neural network. The neural network learns features associated with noise added to reference signals. The neural network recognizes features of noisy time-varying input signals mixed with the noise that at least partially match at least some of the features associated with the noise. The neural network predicts denoised time-varying output signals that correspond to the time-varying input signals based on the recognized features of the noisy time-varying input signals that at least partially match at least some of the features associated with the noise.

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Michael W Walton