Qualitative reasoning is the area of AI which creates representations for continuous aspects of the world, such as space, time, and quantity, which support reasoning with very little information. Typically it has focused on scientific and engineering domains, hence its other name, qualitative physics. It is motivated by two observations. First, people draw useful and subtle conclusions about the physical world without differential equations. In our daily lives we figure out what is happening around us and how we can affect it, working with far less data, and less precise data, than would be required to use traditional, purely quantitative methods. Creating software for robots that operate in unconstrained environments and modeling human cognition requires understanding how this can be done. Second, scientists and engineers appear to use qualitative reasoning when initially understanding a problem, when setting up more formal methods to solve particular problems, and when interpreting the results of quantitative simulations, calculations, or measurements. Thus advances in qualitative physics should lead to the creation of more flexible software that can help engineers and scientists.

Current research spans all aspects of the theory and applications of qualitative reasoning about physical systems.

• Cognitive modeling (e.g., cognitive theories of reasoning about physical systems, theories and experiments concerning human reasoning and learning of mental models, QR models for spatial reasoning, cognitive maps, cognitive robots);
• 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 and supervision);
• Applications (e.g., engineering, education, business, biology, chemistry, ecology, economics, social science, environmental science, medicine, and law);
• Intersection with other modeling approaches (e.g., system dynamics and bond-graphs, signal processing, numerical methods, statistical techniques, differential equations);
• Knowledge acquisition methods (e.g., model building tools and techniques, automated model construction and machine learning, acquisition of models from data).
• Theoretical foundations of qualitative reasoning techniques.

Here are a few ways to get started learning more about what's going on in QR these days:

• AAAI Introductory pages: Qualitative Reasoning
• The 2005, 2006, and 2007 workshops: QR 05, QR 06, QR 07, and QR 08.
• Archive of previous QR workshops: 1987-2004
• Special issue of AI Magazine: Winter 2003
  • Current Topics in Qualitative Reasoning, Editorial Introduction by Bert Bredeweg and Peter Struss.
  • Model-Based Systems in the Automotive Industry, by Peter Struss and Chris Price.
  • Qualitative Modeling in Education, by Bert Bredeweg and Ken Forbus.
  • Qualitative Spatial Reasoning Extracting and Reasoning with Spatial Aggregates, by Chris Bailey-Kellogg and Feng Zhao.
  • Model-Based Programming of Fault-Aware Systems, by Brian C. Williams, Michel D. Ingham, Seung Chung, Paul Elliott, Michael Hofbaur, and Gregory T. Sullivan.
  • Qualitative Reasoning about Population and Community Ecology, by Paulo Salles and Bert Bredeweg.
  • Mathematical Foundations of Qualitative Reasoning, by Louise Trave-Massuyes, Liliana Ironi, and Philippe Dague.
  • Learning Qualitative Models, by Ivan Bratko and Dorian Suc.
  • Model-Based Computing for Design and Control of Reconfigurable Systems, by Markus P. J. Fromherz, Daniel G. Bobrow, and Johan de Kleer.