Granted Patent B2

US 12,204,921 · App. 17/355,499 · Granted Jan 21, 2025

Inventors: Jason Crabtree (Vienna, VA); Andrew Sellers (Monument, CO)

Assignee: QOMPLX LLC

G06F9/455G06F8/30G06F8/41G06F30/27H04L41/0813G06Q10/067

Abstract

Claims (33)

1. A system for generating meta-models in simulated environments, comprising:

a computing device comprising a memory and a processor;

a meta-model module comprising a first plurality of programming instructions stored in the memory and operating on the processor, wherein the first plurality of programming instructions, when operating on the processor, cause the computing device to:

obtain one or more models of an agent; generate source code of the one or more models of an agent;

compile the source code to provide compiled code; and

reconfigure two or more compiled codes from at least two agents to provide a meta-model relating to a plurality of interactions between the at least two agents; and

at least one simulation manager comprising a second plurality of programming instructions stored in the memory and operating on the processor, wherein the second plurality of programming instructions, when operating on the processor, cause the computing device to:

receive a simulation goal related to one or more agent goals;

select a dynamic environment simulation based on the simulation goal;

execute the dynamic environment simulation using a plurality of meta-models; and

continue the execution of the dynamic environment simulation that evolves with agent behavior from the execution of the plurality of agents and the plurality of meta-models until the simulation goal has been reached or until each agent has achieved its agent goal from the one or more agent goals; and

an agent creation engine comprising a third plurality of programming instructions stored in the memory and operating on the processor, wherein the third plurality of programming instructions, when operating on the processor, cause the computing device to

create a plurality of agents, wherein each agent is an individual instance based on the one or more models of an agent;

assign at least one agent goal to each created agent; and

provide the created agents for use in the execution of the dynamic environment simulation;

wherein each of the plurality of agents takes different actions in a non-deterministic environment based on its specifications and the specifications of the dynamic environment simulation to achieve the one or more agent goals;

wherein the different actions and learned behaviors acquired by individual agents further differentiating them from each other during the simulation execution.

2. The system of claim 1 , wherein the agent creation engine operates on a simulation execution server.

3. The system of claim 1 , wherein the agent creation engine operates on a separate server from the simulation execution server.

4. A method for generating meta-models in simulated environments, comprising:

obtaining one or more models of an agent;

generating source code of the one or more models of an agent;

compiling the source code to provide compiled code;

reconfiguring two or more compiled codes from at least two agents to provide a meta-model relating to a plurality of interactions between the at least two agents;

receiving a simulation goal related to one or more agent goals;

selecting a dynamic environment simulation based on the simulation goal;

executing the dynamic environment simulation using a plurality of meta-models; and

continuing the execution of the dynamic environment simulation that evolves with agent behavior from the execution of the plurality of agents and the plurality of meta-models until the simulation goal has been reached or until each agent has achieved its agent goal from the one or more agent goals;

creating a plurality of agents, wherein each agent is an individual instance based on the one or more models of an agent;

assigning at least one agent goal to each created agent; and

providing the created agents for use in the execution of the dynamic environment simulation;

wherein each of the plurality of agents takes different actions in a non-deterministic environment based on its specifications and the specifications of the dynamic environment simulation to achieve the one or more agent goals;

wherein the different actions and learned behaviors acquired by individual agents further differentiating them from each other during the simulation execution.

Assignments (6)

Continuity (17)

References Cited (97)