Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artificial Intelligence For Computer Games: An Introduction
Artificial Intelligence For Computer Games: An Introduction
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
TabletopCars: interaction with active tangible remote controlled cars
Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction
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The success of the computer playing board games (e.g., chess [8], checkers [4], backgammon, tic-tac-toe, etc.) has long served as an indication of the progress in the field of artificial intelligence (AI). The expanding scope of applicability of AI, when the latter is employed to control the individual characters (agents) which are able to "learn" the environment, often including opponents, and to adopt an adaptive optimal (rather than a priori preprogrammed) playing tactics and strategy include soccer [15], motocross and car racing [3, 21], etc. [4], [6]. In this article, we focus on the domain of car racing, and consider the problem of designing a driving agent, able to remotely control a scale model of a racing car, which runs in a human-competitive, fast and consistent way. Adhering to the commonly recognized criteria of the human competitiveness of automatically evolved solutions, we attempt to verify that the evolved driving agent "holds its own or wins a regulated competition involving human contestants" [11].