Paper Info
Title
Real-time evolution of neural networks in the NERO video game
Abstract
A major goal for AI is to allow users to interact with agents that learn in real time, making new kinds of interactive simulations, training applications, and digital entertainment possible. This paper describes such a learning technology, called real-time NeuroEvolution of Augmenting Topologies (rtNEAT), and describes how rtNEAT was used to build the NeuroEvolving Robotic Operatives (NERO) video game. This game represents a new genre of machine learning games where the player trains agents in real time to perform challenging tasks in a virtual environment. Providing laymen the capability to effectively train agents in real time with no prior knowledge of AI or machine learning has broad implications, both in promoting the field of AI and making its achievements accessible to the public at large.
Year
Venue
Keywords
2006
AAAI
challenging task,neural network,video game,real time,nero video game,new kind,real-time evolution,providing layman,machine learning,augmenting topologies,broad implication,new genre,neuroevolving robotic operatives,virtual environment,learning technology
Field
DocType
Citations 
Educational technology,Digital entertainment,Virtual machine,Computer science,Game design,Neuroevolution of augmenting topologies,Artificial intelligence,Train,Artificial neural network,Machine learning
Conference
18
PageRank 
References 
Authors
0.89
11
4
Name
Order
Citations
PageRank
Kenneth O. Stanley14347225.49
Bobby D. Bryant2586.70
Igor Karpov3504.44
Risto Miikkulainen42981224.85