Attacking the knowledge acquisition bottleneck through games-for-modelling

Many model-based methods in AI require some sort of formal representation of knowledge as input. Acquisition of such formal models is either done manually, using a knowledge elicitation and modelling method, or automatically, applying knowledge discovery and machine learning techniques to available data. For the acquisition of highly structured, domainspecific knowledge, machine learning techniques still fall short, and knowledge elicitation and modelling is then the standard. However, obtaining formal models from informants who have few or no formal skills is a non-trivial aspect of knowledge acquisition, which can be viewed as an instance of the well-known "knowledge acquisition bottleneck". In addition, if there are social requirements on knowledge representations, e.g. constructive agreement on concepts and propositions, this poses a further challenge. Based on our work in conceptual modelling and method engineering, we propose to cast methods for knowledge modelling in the framework of games. The resulting games-formodelling approach is illustrated by a number of examples from ongoing projects. Our chief long-term aim is to decrease the threshold for formal knowledge acquisition and modelling.