Swarm system including an operator control section enabling operator input of mission objectives and responses to advice requests from a heterogeneous multi-agent population including information fusion, control diffusion, and operator infusion agents that controls platforms, effectors, and sensors
First Claim
1. A complex adaptive command guided swarm system comprising:
- a first command and control section comprising a user interface, a computer system, a network interface, and a plurality of command and control systems executable on the computer system; and
a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs), each of the plurality of SAASoSPs comprising;
a second command and control section;
one or more sensors;
a network communication section configured to communicate with at least the first command and control section and one or more other SAASoSPs of the plurality of SAASoSPs; and
one or more equipment systems configured to create a physical effect on an object or an environment external to the SAASoSP;
wherein the second command and control section includes;
at least one information fusion agent configured to mine and process information that originates in the environment external to the SAASoSP, the information including features including patterns recognized within a set of pixel vectors, objects including patterns recognized within a set of feature vectors, situations including patterns recognized within a set of object state vectors, and impacts or threats including patterns recognized within a set of situation vectors;
at least one control diffusion agent configured to generate control actions based, in part, on the information collected and processed by the at least one information fusion agent and disseminate the control actions within the SAASoSP; and
at least one operator infusion agent configured to receive a range of operator inputs via the first command and control section including at least a range of high level mission objective data;
wherein the second control and command system is configured to operate using active advice-seeking learning logic using a combination of active learning and advice-seeking learning, wherein each SAASoSP of the plurality of SAASoSPs is configured to solicit or query for advice from a human operator via the first control and command system during either training of the complex adaptive command guided swarm system or execution of a mission, including configuration to receive input of advice from the human operator concerning one or more complex domains of interest and/or concerning broad characterizations of entire regions of one or more data input spaces.
0 Assignments
0 Petitions
Accused Products
Abstract
Systems and methods are provided relating to a complex adaptive command guided swarm system including an operator section comprising a first command and control section and a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs). The first command and control section includes a user interface, computer system, network interface, and plurality of command and control systems executed or running on the computer system. The networked SAASoSPs each include a second command and control section, wherein the second command and control section utilizes artificial intelligence (AI) configured with a combination of both symbolic and probabilistic machine learning for various functions including pattern recognition and new pattern identification. The AI is also configured to combine advice-based learning with active learning, wherein the AI solicits advice from a domain expert user via the first command and control section as necessary during both training and operational stages of the system.
-
Citations
18 Claims
-
1. A complex adaptive command guided swarm system comprising:
-
a first command and control section comprising a user interface, a computer system, a network interface, and a plurality of command and control systems executable on the computer system; and a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs), each of the plurality of SAASoSPs comprising; a second command and control section; one or more sensors; a network communication section configured to communicate with at least the first command and control section and one or more other SAASoSPs of the plurality of SAASoSPs; and one or more equipment systems configured to create a physical effect on an object or an environment external to the SAASoSP; wherein the second command and control section includes; at least one information fusion agent configured to mine and process information that originates in the environment external to the SAASoSP, the information including features including patterns recognized within a set of pixel vectors, objects including patterns recognized within a set of feature vectors, situations including patterns recognized within a set of object state vectors, and impacts or threats including patterns recognized within a set of situation vectors; at least one control diffusion agent configured to generate control actions based, in part, on the information collected and processed by the at least one information fusion agent and disseminate the control actions within the SAASoSP; and at least one operator infusion agent configured to receive a range of operator inputs via the first command and control section including at least a range of high level mission objective data; wherein the second control and command system is configured to operate using active advice-seeking learning logic using a combination of active learning and advice-seeking learning, wherein each SAASoSP of the plurality of SAASoSPs is configured to solicit or query for advice from a human operator via the first control and command system during either training of the complex adaptive command guided swarm system or execution of a mission, including configuration to receive input of advice from the human operator concerning one or more complex domains of interest and/or concerning broad characterizations of entire regions of one or more data input spaces. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
-
-
15. A method of operating a complex adaptive command guided swarm system comprising:
-
supplying a first command and control section comprising a user interface, a computer system, a network interface, and a plurality of command and control systems executable on the computer system; and supplying a plurality of networked swarm of semi-autonomously agent controlled system of systems platforms (SAASoSPs), each of the plurality of SAASoSPs comprising; a second command and control section; one or more sensors; a network communication section configured to communicate with at least the first command and control section and one or more other SAASoSPs of the plurality of SAASoSPs; and one or more equipment systems configured to create a physical effect on an object or an environment external to the SAASoSP; wherein the second command and control section is configured to implement; at least one information fusion agent configured to mine and process information that originates in the environment external to the SAASoSP, the information including features including patterns recognized within a set of pixel vectors, objects including patterns recognized within a set of feature vectors, situations including patterns recognized within a set of object state vectors, and impacts or threats including patterns recognized within a set of situation vectors; at least one control diffusion agent configured to generate control actions based, in part, on the information collected and processed by the at least one information fusion agent and disseminate the control actions within the SAASoSP; and at least one operator infusion agent configured to receive a range of operator inputs via the first command and control section including at least a range of high level mission objective data; wherein the second control and command system is configured to operate using active advice-seeking learning logic using a combination of active learning and advice-seeking learning, wherein each SAASoSP of the plurality of SAASoSPs is configured to solicit or query for advice from a human operator via the first control and command system during either training of the complex adaptive command guided swarm system or execution of a mission, including configuration to receive input of advice from the human operator concerning one or more complex domains of interest and/or concerning broad characterizations of entire regions of one or more data input spaces. - View Dependent Claims (16, 17, 18)
-
Specification