Summary
- Release Year: 2005
- Genres: Simulator, Strategy
- Platforms: PC (Microsoft Windows)
- Developers: DMC/IC2
- Publishers: DMC/IC2
NERO: Neuro-Evolving Robotic Operatives (2005)
NERO is an innovative and groundbreaking game that combines elements of real-time strategy (RTS) with machine learning and artificial intelligence (AI) research. Developed by a team at the University of Texas at Austin, NERO is one of the earliest examples of a new genre of games known as Machine Learning Games (MLGs).
Gameplay
NERO is played in two distinct phases:
-
Training Phase: In this phase, players deploy and train simulated robots in a 3D physics sandbox environment. Players can customize their robots’ appearance, weapons, and behavioral tactics. The training process involves setting up waypoints, defining patrol routes, and specifying attack and defense strategies. Players can also use a variety of tools to monitor their robots’ performance and make adjustments to their training regimen.
-
Battle/Territory Phase: Once players have trained their robots, they can pit them against other players’ robots in either battle mode or territory mode. In battle mode, the goal is to destroy the opponent’s robots, while in territory mode, the goal is to capture and hold strategic points on the map.
Machine Learning and AI
One of the most innovative aspects of NERO is its use of machine learning and AI. The robots in NERO are not pre-programmed with specific behaviors; instead, they learn and adapt their behavior based on their training and experience. This is accomplished through the use of a neuroevolutionary algorithm, which is a type of AI that uses principles of natural selection and evolution to train artificial neural networks.
The neuroevolutionary algorithm in NERO works by generating a population of randomly initialized neural networks. Each neural network controls the behavior of a robot, and the robots are then evaluated based on their performance in the training environment. The best-performing robots are then selected and used to create the next generation of neural networks. This process is repeated over multiple generations, resulting in a population of robots that are increasingly well-adapted to the training environment.
Significance
NERO is a significant game for several reasons. First, it is one of the earliest examples of a Machine Learning Game, a new genre of games that combines gameplay with AI research. Second, NERO’s use of neuroevolutionary algorithms to train robots is a novel approach to AI in games. Third, NERO has been used as a research platform for studying the evolution of cooperation and competition in artificial systems.
Reception
NERO received generally positive reviews from critics. IGN praised the game’s innovative gameplay and AI, while GameSpot criticized the game’s graphics and lack of content. However, NERO has been more influential in the field of AI research than in the gaming community. The game has been used as a testbed for studying neuroevolutionary algorithms and has helped to advance the field of AI.
Legacy
NERO has had a lasting impact on the field of AI research. The game’s use of neuroevolutionary algorithms to train robots has inspired other researchers to explore the use of AI in games. NERO has also been used as a teaching tool in AI courses, and it has helped to raise awareness of the potential of AI in games.
Conclusion
NERO is a groundbreaking and innovative game that combines elements of RTS with machine learning and AI research. The game’s use of neuroevolutionary algorithms to train robots is a novel approach to AI in games, and it has been influential in the field of AI research. NERO is a significant game that has helped to advance the field of AI and has inspired other researchers to explore the use of AI in games.
Review Score
8.5/10
