University of Texas uses Torque for AI Game Experiment
University of Texas uses Torque for AI Game Experiment
| News Link: | http://nn.cs.utexas.edu/NERO/description.php |
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| Submitted: | Jay Moore |
| Posted: | Jun 27, 2005 |
| Synopsis: | The University of Texas at Austin, Digital Media Collaboratory/Innovation Creativity and Capital Institute Neuroevolution Group/Department of Computer Sciences, novel experimental game is called NERO, which stands for Neuro-Evolving Robotic Operatives. It is set in a fictional post-apocalyptic world, where robots struggle over the relics of human civilization. |
| Keywords: | garagegames development |
Article
| The NERO Game Our novel experimental game is called NERO, which stands for Neuro-Evolving Robotic Operatives. It is set in a fictional post-apocalyptic world, where robots struggle over the relics of human civilization. Download NERO the game (Win / 34 MB) Although it resembles some RTS games, unlike most RTS games NERO consists of two distinct phases of play. In the first phase individual players deploy robots in a 'sandbox' and train them to the desired tactical doctrine. Once a collection of robots has been trained, a second phase of play allows players to pit their robots in a battle against robots trained by some other player, to see how well their training regimens prepared their robots for battle. The training phase is the most innovative aspect of game play in NERO, and is also the most interesting from the perspective of AI research. (See screenshot below.) Training for complex tactical behaviors will require a player to think out and implement a shaping plan, leading the robots through a series of sandbox scenarios that guide them stepwise to the desired battlefield doctrine. Real-Time Neuroevolution with rtNEAT The robots in NERO use artificial neural networks for their "brains", and they learn by means of neuroevolution. Neuroevolution is a genetic algorithm, a type of reinforcement learning algorithm that operates by rewarding the agents in a population that perform the best and punishing those that perform the worst. For the NERO project we are using a specific neuroevolutionary algorithm called NEAT, Neuro-Evolution of Augmenting Topologies. Unlike most neuroevolutionary algorithms, NEAT starts with an artificial neural network of minimal connectivity and adds complexity only when it helps solve a problem. This helps ensure that the algorithm does not produce unnecessarily complex solutions. In NERO we are introducing a new real-time variant of NEAT, called rtNEAT, in which a small population evolves while you watch. (Most genetic algorithms use generation-based off-line processing, and only provide a result at the end of some pre-specified amount of training.) rtNEAT solves several technical challenges. For example, in order to allow continual adaptation, rtNEAT discards the traditional notion of generations for the genetic algorithm, and instead keeps a small population that is evaluated continuously, with regular replacement of the poorest performers. rtNEAT is powerful enough that we are able to work with a population as small as 30 even for non-trivial learning tasks. This allows the entire population to be replaced quickly enough for human viewers to see the population's behavior adapt while they watch. Real-time neuroevolution We break up the regimented schedule of generation-based evolution by imposing an artificial lifetime on each "brain" being evaluated. In this example the brains control individual agents in a simulation. Whenever a brain's evaluation lifetime is up, we compare its fitness (based on the agent's performance while "wearing" that brain) to the fitness of other brains in the population. If it is among the least fit brains it is immediately discarded and replaced by breeding two high-fitness brains together using NEAT. Otherwise the brain is put on the shelf, ready to be inserted into another robot for further evaluation when its turn comes up again. Acknowledgements Production of the NERO project is funded by the Digital Media Collaboratory and the IC2 Institute of the University of Texas at Austin. The original NEAT research was supported in part by the National Science Foundation and the Texas Higher Education Coordinating Board. NERO is built on the Torque game engine, licensed from GarageGames, Inc. Background In August 2003 the Digital Media Collaboratory (DMC) of the Innovation Creativity and Capital Institute (IC2) at the University of Texas held its second annual GameDev conference. The focus of the 2003 conference was artificial intelligence, and consequently the DMC invited several Ph.D. students from the Neuroevolution Group at the University's Department of Computer Science (UTCS) to make presentations on state-of-the-art academic AI research with potential game applications. The GameDev conference also held break-out sessions where groups brainstormed ideas for innovative games, and in one of the sessions Ken Stanley proposed an idea for a game based on a real-time variant of his previously published NEAT learning algorithm. On the basis of Ken's proposal the DMC/IC2 resolved to staff and fund a project to create a professional-quality demo of the game. The resulting NERO project started in October 2003 and has continued through the present, generating several spin-off research projects in its wake. As a result of the project we have imported the latest in AI research from the UTCS Neuroevolution Group into a commercial game engine, providing the DMC with a case study in technology transfer and a polished demo of an entertaining game. Ken Stanley, Bobby Bryant, and Risto Miikkulainen won the Best Paper Award at the IEEE 2005 Symposium on Computational Intelligence and Games, for Evolving Neural Network Agents in the NERO Video Game. Risto gave a keynote talk and NERO producer Aliza Gold presented a paper on transfering academic AI to game applications. |
Submit your own resources!| Blake Lowry (Jun 27, 2005 at 01:06 GMT) |
| Jay Moore (Jun 27, 2005 at 02:52 GMT) |
| Jameson Bennett (Jun 27, 2005 at 02:56 GMT) |
The Nero project was partially funded by the National Science Foundation. That's how our tax dollars should be spent!
| Blake Lowry (Jun 27, 2005 at 03:00 GMT) |
| Ryan V. Jaeger (Jun 28, 2005 at 02:54 GMT) |
| Ace (Jun 28, 2005 at 03:52 GMT) |
| Blake Lowry (Jun 28, 2005 at 06:14 GMT) |
| Ace (Jun 28, 2005 at 21:35 GMT) |
| University Of Texas 1 (Jun 28, 2005 at 21:36 GMT) Resource Rating: 5 |
| Ace (Jun 29, 2005 at 02:44 GMT) |
I just think its the best way for technoligy to advance.
www.garagegames.com/mg/forums/result.thread.php?qt=18652
Youll notice my source is available through the sdk forms only.
Edited on Jun 29, 2005 02:44 GMT
| Xavier "eXoDuS" Amado (Jun 29, 2005 at 04:32 GMT) |
| Shon Gale (Aug 17, 2005 at 23:26 GMT) |
http://nn.cs.utexas.edu/NERO//index.php?page=description&part=nerogame
| Michael Chrien (Aug 18, 2005 at 07:25 GMT) |
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