Knowledge representation for fuzzy model composition

Compositional Modelling (CM) has been applied to synthesize automatically plausible scenarios in many problem domains with promising results. However, due to the lack of capability to deal with imprecise or illdefined information, there is a pressing need to improve the robustness and accuracy of the existing CM work. This paper presents a more flexible knowledge representation formalism that combines fuzzy set theory and recently developed CM methods to support automating the process of generating plausible scenario spaces. The proposed knowledge representation corporates both fuzzy parameters and fuzzy constraints into the representation of conventional model fragments. The fuzzy model composition process is illustrated by means of a simple worked example for aiding in crime investigation.