Rapid adaptation of video game AI.
01/2008; pp.79-86 In proceeding of: Proceedings of the 2008 IEEE Symposium on Computational Intelligence and Games, CIG 2009, Perth, Australia, 15-18 December, 2008
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ABSTRACT: Current approaches to adaptive game AI typically require numerous trials to learn effective behavior (i.e., game adaptation is not rapid). In addition, game developers are concerned that applying adaptive game AI may result in uncontrollable and unpredictable behavior (i.e., game adaptation is not reliable). These characteristics hamper the incorporation of adaptive game AI in commercially available video games. In this paper, we discuss an alternative to these current approaches. Our alternative approach to adaptive game AI has as its goal adapting rapidly and reliably to game circumstances. Our approach can be classified in the area of case-based adaptive game AI. In the approach, domain knowledge required to adapt to game circumstances is gathered automatically by the game AI, and is exploited immediately (i.e., without trials and without resource-intensive learning) to evoke effective behavior in a controlled manner in online play. We performed experiments that test case-based adaptive game AI on three different maps in a commercial real-time strategy (RTS) game. From our results, we may conclude that case-based adaptive game AI provides a strong basis for effectively adapting game AI in video games.IEEE Transactions on Computational Intelligence and AI in Games 07/2009; · 1.62 Impact Factor
Conference Proceeding: Controller for TORCS created by imitation[show abstract] [hide abstract]
ABSTRACT: This paper is an initial approach to create a controller for the game TORCS by learning how another controller or humans play the game. We used data obtained from two controllers and from one human player. The first controller is the winner of the WCCI 2008 Simulated Car Racing Competition, and the second one is a hand coded controller that performs a complete lap in all tracks. First, each kind of controller is imitated separately, then a mix of the data is used to create new controllers. The imitation is performed by means of training a feed forward neural network with the data, using the backpropagation algorithm for learning.Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on; 10/2009
Conference Proceeding: A data mining approach to strategy prediction[show abstract] [hide abstract]
ABSTRACT: We present a data mining approach to opponent modeling in strategy games. Expert gameplay is learned by applying machine learning techniques to large collections of game logs. This approach enables domain independent algorithms to acquire domain knowledge and perform opponent modeling. Machine learning algorithms are applied to the task of detecting an opponent's strategy before it is executed and predicting when an opponent will perform strategic actions. Our approach involves encoding game logs as a feature vector representation, where each feature describes when a unit or building type is first produced. We compare our representation to a state lattice representation in perfect and imperfect information environments and the results show that our representation has higher predictive capabilities and is more tolerant of noise. We also discuss how to incorporate our data mining approach into a full game playing agent.Computational Intelligence and Games, 2009. CIG 2009. IEEE Symposium on; 10/2009
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