Qualitative Reasoning (QR) is an exciting research area of Artificial Intelligence that combines the quest for fundamental understanding of effective reasoning about physical systems and new ways to supplement conventional modeling, analysis, diagnosis, and control techniques to tackle real-world applications. This year we have broadened the scope of the Workshop to investigate the links between qualitative modeling and reasoning techniques and cognitive science research. In particular, we have decided to collocate with the International Conference on Artificial Intelligence in Education (AI-ED 2001) to promote discussion of common problems and solutions between Cognitive Scientists and Education researchers and the traditional QR community. Previous QR Workshops have been a forum where people from a wide range of disciplines gather to discuss and share their results in this area of human knowledge. This year we would like to invite submissions in all of the traditional submission areas, along with papers in cognitive science and education that employ techniques and methodologies that are related to QR. Examples of relevant topic areas include:
· QR techniques (e.g., qualitative simulation, ontologies, management of multiple models, reasoning over time and space, mathematical formalizations of QR, qualitative algebras, qualitative dynamics, qualitative kinematics, qualitative optimization)
· Task-level reasoning (e.g., design/planning, monitoring, diagnosis and repair, explanation, tutoring and training, process control/supervision)
· QR and Cognitive modeling (e.g., QR models for spatial reasoning, cognitive maps, cognitive theories of reasoning about physical systems, models of human reasoning)
· QR applications in engineering, education, and business (e.g., descriptions of application problems and solutions; applications in designing educational and tutoring systems, applications dealing with: real-time responses, large scale models and scaling techniques in general, different knowledge sources)
· QR techniques and conventional approaches (e.g., system dynamics and bond-graphs, signal processing, numerical methods, statistical techniques, differential equations)
· New application areas (e.g., biology, chemistry, ecology, economics, environmental science, medicine, and law)
· Knowledge acquisition methods (e.g., model building tools and techniques, automated model construction and machine learning, acquisition of models from data)
· Methodological issues (e.g., classifying and relating QR approaches, evaluating QR approaches, criteria for selecting methods)