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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(More)
—The UTˆ2 bot, which had a humanness rating of 27.2727% in BotPrize 2010, is based on two core ideas: (1) multiobjective neuroevolution is used to learn skilled combat behavior, but filters on the available combat actions ensure that the behavior is still human-like despite being evolved for performance, and (2) a database of traces of human play is used to(More)
— The General Game Playing Competition [1] poses a unique challenge for Artificial Intelligence. To be successful, a player must learn to play well in a limited number of example games encoded in first-order logic and then generalize its game play to previously unseen games with entirely different rules. Because good opponents are usually not available,(More)
Video and computer games provide a rich platform for testing adaptive decision systems such as value-based reinforcement learning and neuroevolution. However, integrating such systems into the game environment and evaluating their performance in it is time and labor intensive. In this paper, an approach is developed for using general integration and(More)
Neuroevolution is a promising approach for constructing intelligent agents in many complex tasks such as games, robotics, and decision making. It is also well suited for evolving team behavior for many multiagent tasks. However, new challenges and opportunities emerge in such tasks, including facilitating cooperation through reward sharing and(More)
OpenNERO is an open source game platform designed for game AI research. The software package combines features commonly available in modern game engines (such as 3D graphics, physics simulation, 3D audio rendering , networked play, and a powerful scripting interface) with an easy to use API and tools for defining machine learning tasks, environments, and(More)
Many different methods for combining human expertise with machine learning in general, and evolutionary computation in particular, are possible. Which of these methods work best, and do they outperform human design and machine design alone? In order to answer this question, a human-subject experiment for comparing human-assisted machine learning methods was(More)
In the NeuroEvolving Robotic Operatives (NERO) video game, the player trains a team of virtual robots for combat against other players' teams. The virtual robots learn in real time through interacting with the player. Since NERO was originally released in June, 2005, it has been downloaded over 50,000 times, appeared on Slashdot, and won several honors. The(More)
Neuroevolution is a promising learning method in tasks with extremely large state and action spaces and hidden states. Recent advances allow neuroevolution to take place in real time, making it possible to e.g. construct video games with adap-tive agents. Often some of the desired behaviors for such agents are known, and it would make sense to prescribe(More)