Empirical game theoretic system and method for adversarial decision analysis

A computer implemented method for performing a simulated vehicle control task based on adversarial decision analysis, the method comprising an act of:

• causing one or more processors to execute instructions encoded on a non-transitory computer-readable medium, such that upon execution, the one or more processors perform operations of:
• performing a plurality of empirical game theoretic analyses between an evolving population of neural network strategies and an adversary population of neural network strategies, wherein each empirical game theoretic analysis comprises:
• using a neuroevolution procedure, performing a fitness-based selection of a strategy in the evolving population of neural network strategies that out-performs the adversary population of neural network strategies;
• and
• using an empirical game theory procedure, iteratively running the neuroevolution procedure and adding the selected strategy to the adversary population of neural network strategies with each iteration,
• wherein addition of the selected strategy to the adversary population of neural network strategies results in monotonic strategy improvement with each iteration;
• following the plurality of empirical game theoretic analyses, selecting a final strategy for the evolving population of neural network strategies and the adversary population of neural network strategies using a tournament selection procedure;
• using the final strategy, training a neural network;
• and
• using the trained neural network to perform a simulated vehicle control task.

A computer program product for performing a simulated vehicle control task based on adversarial decision analysis, the computer program product comprising:

• computer-readable instructions stored on a non-transitory computer-readable medium that are executable by a computer having one or more processors for causing the processor to perform operations of:
• performing a plurality of empirical game theoretic analyses between an evolving population of neural network strategies and an adversary population of neural network strategies, wherein each empirical game theoretic analysis comprises:
• using a neuroevolution procedure, performing a fitness-based selection of a strategy in the evolving population of neural network strategies that out-performs the adversary population of neural network strategies;
• and
• using an empirical game theory procedure, iteratively running the neuroevolution procedure and adding the selected strategy to the adversary population of neural network strategies with each iteration,
• wherein addition of the selected strategy to the adversary population of neural network strategies results in monotonic strategy improvement with each iteration;
• following the plurality of empirical game theoretic analyses, selecting a final strategy for the evolving population of neural network strategies and the adversary population of neural network strategies using a tournament selection procedure;
• using the final strategy, training a neural network;
• and
• using the trained neural network to perform a simulated vehicle control task.