These are the working notes of the workshop on Model-based and Qualitative Reasoning in Biomedicine, which was held during the European Conference on Artificial Intelligence in Medicine, AIME’03, on 19th October, 2003, in Protaras, Cyprus. The workshop brought together various researchers involved in the development and use of model-based and qualitative reasoning methods in tackling biomedical problems. Much of the biomedical knowledge is essentially model-based, as it is the understanding of the structure and function of biomedical systems that researchers wish (...) to achieve, and this is best done by developing models of these systems. In situations where it may not be appropriate or possible to use quantitative methods, researchers use qualitative approaches. Depending on the biomedical problem concerned, such descriptions may involve causal, temporal and spatial knowledge, possibly of an uncertain nature. Also in the medical management of disorders in patients, qualitative and model-based approaches are being used. For example, systems used for diagnosing disease rely on explicit models of normal or abnormal structure and behaviour (often referred to as ’first-principles models’) of the underlying disease process. Qualitative knowledge plays a role in the modelling of disease and treatment processes, including the handling of the uncertainty involved in these processes. Hence, there is little doubt that model-based and qualitative methods fit the biomedical domain really well. However, one of the problems with research in the biomedical field is that researchers applying model-based and qualitative-reasoning methods are often closely linked to their application field, such as, for example, cell biology or clinical medicine, and find it difficult to keep in contact with colleagues doing similar research, but working in a different biomedical application field. This is even more difficult if the techniques used are also different. For example, researchers involved in Bayesian network research and researchers using qualitative simulation methods hardly exchange views and ideas, despite the fact that their methods have in common that they emphasise representing qualitative biomedical knowledge. It was the aim of this workshop to bring together researchers along the entire spectrum of the biomedical field, from health-care research and clinical medicine to human biology, using a variety of methods and techniques, from (qualitative) Bayesian networks and symbolic machine learning, to qualitative simulation.. (shrink)

Sketching is a powerful means of working out and communicating ideas. Sketch understanding involves a combination of visual, spatial, and conceptual knowledge and reasoning, which makes it both challenging to model and potentially illuminating for cognitive science. This paper describes CogSketch, an ongoing effort of the NSF-funded Spatial Intelligence and Learning Center, which is being developed both as a research instrument for cognitive science and as a platform for sketch-based educational software. We describe the idea of open-domain sketch understanding, the (...) scientific hypotheses underlying CogSketch, and provide an overview of the models it employs, illustrated by simulation studies and ongoing experiments in creating sketch-based educational software. (shrink)

We use our knowledge of causal relationships to imagine possible events. We also use these relationships to look deep into the past and infer events that were not witnessed or to infer what can not be directly seen in the present. Knowledge of causal relationships allows us to go beyond the here and now. This chapter introduces a new theoretical framework for how this very basic concept might be mentally represented. It proposes an epistemological theory of causation — that is, (...) a theory that specifies the nature of people's knowledge of causation, the notion of causation used in everyday language and reasoning. (shrink)