My primary research interest involves the (semi-)automated construction of models of real-world systems/scenarios. The ultimate aim of these techniques is the development of systems that enable the identification of the system or scenario that caused observed behaviours, symptoms or evidence. One aspect of this work involves the development of knowledge representation formalisms to represent model construction knowledge and corresponding inference mechanisms. One approach, in which I am particularly interested, known as compositional modelling, involves representations of modelling knowledge in the form of partial models or model fragments, and inference mechanisms that aim to compose these model fragments into complete models that meet certain requirements. Another aspect of this work involve ranking models based on their relative adequacy or plausibility. To tackle this issue, I have adapted and employed constraint satisfaction techniques, Bayesian networks, qualitative reasoning approaches and symbolic preference calculi. Recently, I have applied this work to the ecological modelling and serious crime investigation domains. The objective of my work in the former domain was the development of a repository of ecological models. The objective of my work in the latter domain is the development of decision support systems that aid serious crime investigators. Specialties: Artificial intelligence, knowledge representation, inference, diagnosis, truth maintenance, approximate reasoning, qualitative reasoning, rule-based systems, model based reasoning.