Neuroevolution: Real-Time Creation of Sequential Digital Systems for Control, Design, and Decision Making

Computing & Wireless : Utility Software

Available for licensing

Inventors

  • Risto Miikkulainen, Ph.D. , Computer Science
  • Kenneth Stanley, Ph.D. , Computer Science

Background/unmet need

Many applications lack a method of evolving networks for learning tasks. In difficult real-world learning tasks such as controlling robots, playing games, or pursuing or evading an enemy, there are no direct targets that can specify the correct action for every situation. Neural networks promise to solve this problem, but a long process of training must occur before it can be effective. A new system for the training of neural networks is needed and can be used to solve a number of problems.

Invention Description

Real-time NeuroEvolution of Augmenting Topologies (rtNEAT) is a genetic algorithm that trains and evolves neural networks of increasing complexity from a minimal starting point. This means networks that succeed continue while others are discarded, avoiding the problem of preparatory (non-real-time) training. Agents governed by rtNEAT neural networks can learn processes and even invent new solutions based on feedback without the guidance of a human programmer or controller, freeing the programmer from having to script extensive behaviors.

Benefits/Advantages

    Can find solutions efficiently in real-timeCan solve new problems without trainingCan discover novel solutionsEvolves increasingly optimal and complex controllersCan be universally installed in systemsBroad range of beneficial applications

Features

    Continual, indefinite evolution Evolution occurs in real-time rather than at fixed intervals while the user has to waitBehavioral responses to environment and scenariosPackaged as a software development kit

Market potential/applications

Since NEAT and rtNEAT are general algorithms for evolving controllers, any application involving the automated control of some process, object, vehicle or sensory system could be viable. This technology currently is being directed towards the video game industry for the possibility of evolving characters in games and massive multiplayer online games. The uses for this algorithm, however, can be expanded to military simulations, educational games and applications, robotics, vehicle control systems, factories or as a research tool for modeling. The algorithms could also be implemented in pattern recognition and prediction applications.

Development Stage

Beta product/commercial prototype

IP Status

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