Learning games for configuration and diagnosis tasks.
Alexander Felfernig, Michael Jeran, Thorsten Ruprechter, Alexander Ziller, Stefan Reiterer, Martin Stettinger · 2015
A goal of many Artificial Intelligence (AI) courses is to teach properties of synthesis and analysis tasks such as configuration and diagnosis. Configuration is a special case of design activity where the major goal is to identify configurations that satisfy the user requirements and are consistent with the configuration knowledge base. If the requirements are inconsistent with the knowledge base, changes (repairs) for the current requirements have to be identified. In this paper we present games that can, for example, be used within the scope of Artificial Intelligence courses to easier understand configuration and diagnosis concepts. We first present the CONFIGURATIONGAME and then continue with two further games (COLORSHOOTER and EATIT) that support the learning of modelbased diagnosis concepts.