Meta-Model Agents for Dynamic Simulation Learning
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Solution Overview
Problem
Current digital simulation technologies are unable to generate agents capable of machine learning within simulated environments, particularly those generated procedurally or stochastically, limiting their usefulness in simulating real-world situations.
Innovation Solution
A system and method for generating and applying meta-models in simulated environments, where agents are created with specific goals and operate using meta-models that describe their interactions, allowing for dynamic and evolving behavior within the simulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If agents operate with hard-coded and static behaviors in simulated environments, then the simulation engine can run reliably with pre-set agents, but the agents cannot learn or adapt to procedural or stochastic environments
Solution Approach 1:
The patent transforms static, hard-coded agent behaviors into dynamic, learnable behaviors through meta-models. Agents evolve from rigid pre-programmed entities to adaptive systems that can learn optimal strategies through reinforcement learning, allowing them to adapt to procedural and stochastic environments while maintaining simulation reliability
Solution Approach 2:
The patent introduces meta-models as an intermediary layer between the simulation environment and agent behaviors. These meta-models serve as learnable representations that mediate between environmental inputs and agent actions, enabling agents to adapt to complex environments without requiring complete reprogramming of the simulation engine
2Adaptability or versatility
If simulated environments are generated procedurally or stochastically to elucidate real-world situations, then the simulation becomes more useful for real-world applications, but existing agents cannot operate effectively because they rely on hard-coded behaviors
Solution Approach 1:
The patent implements feedback loops where agents receive rewards or penalties based on their actions in procedural environments. This reinforcement learning mechanism allows agents to continuously improve their behaviors by learning from environmental feedback, ensuring reliable operation across varying procedural generations while maintaining adaptability to real-world scenarios
Solution Approach 2:
The patent enables parameter changes in agent behaviors through learned meta-models. Instead of fixed hard-coded parameters, agents dynamically adjust their behavioral parameters based on environmental conditions and learned experiences, allowing reliable operation across diverse procedural and stochastic environments that mimic real-world variability
3Adaptability or versatility
If agents use meta-models to describe interactions and learn behaviors, then agents can adapt and learn in simulated environments, but the system complexity increases significantly
Solution Approach 1:
The patent segments the complex learning system into distinct components: environment simulations, meta-models, and agent instances. This modular architecture allows each component to be developed and managed independently, reducing overall system complexity while enabling advanced learning capabilities through the coordinated interaction of segmented elements
Data Source
AI summary
A system and method for generating and applying meta-models in simulated environments, in which an agent simulation is selected, one or more agent goals are received, and agents are created which are individual instances of the agent simulation with each agent having at least one of the agent goals, wherein the agents are used in the execution of an environment simulation which dynamically changes based on the collective behavior of the agents. The agents operate in the environment simulation using meta-models which describe how the agents interact with other agent and how the agents interact within the simulation.