"Broadly speaking, qualitative-reasoning research aims to develop representation and reasoning techniques that will enable a program to reason about the behavior of physical systems, without the kind of precise quantitative information needed by conventional analysis techniques such as numerical simulators. ... Observing pouring rain and a river's steadily rising water level is sufficient to make a prudent person take measures against possible flooding - without knowing the exact water level, the rate of change, or the time the river might flood." -Yumi Iwasaki

Kenneth D. Forbus' "next-to-final drafts, with more citations than final version" of the chapter "Qualitative Reasoning" for the CRC Handbook of Computer Science and Engineering. 1996. - This page provides links to both the PDF and Postscript versions.

The Qualitative Reasoning and Modelling (QRM) portal at the University of Amsterdam provides up-to-date and easy to use software tools (Garp3), documentation and support for users to build and simulate qualitative models.

Qualitative Reasoning (QR) is an area of research within Artificial Intelligence (AI) that automates reasoning and problem solving about the (physical) world. It creates non-numerical descriptions of systems and their behaviour, preserving important behavioural properties and qualitative distinctions. Successful application areas include autonomous spacecraft support, failure analysis and on-board diagnosis of vehicle systems, automated generation of control software for photocopiers, conceptual knowledge capture in ecology, and intelligent aids for human learning. For an overview of this AI research, consult the special issue of the AI magazine on Qualitative Reasoning (Bredeweg & Struss, 2003).

• "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." -from the Introduction

Immobots Take Control. By Wade Roush. Technology Review (December 2002/January 2003). "From photocopiers to space probes, machines injected with robotic self-awareness are reliable problem solvers. ... But Deep Space One had something Mars Polar Lander lacked: an onboard robot able to think autonomously and handle the unexpected. Using its engineering knowledge, the robot tried to repair the switch by toggling it on and off. When this failed, it devised a successful plan to complete the navigation maneuver, and the craft proceeded unharmed. The robot that saved Deep Space One was in the vanguard of a new breed of machines poised to have a big impact in space and here on Earth. Quite unlike the metallic contraptions that march stiffly through sci-fi movies or the mindless, stripped-down devices that heft parts on our assembly lines, the new robots have more brain than brawn. Each possesses a detailed picture of its own inner workings—encoded in software-based models—that gives it the ability to respond in novel ways to events its programmers might not have anticipated. Because many of these inward-focused, self-reconfiguring machines don’t move, some computer scientists call them immobile robots, or 'immobots.' ... A deep-space probe obviously requires much more autonomy than, say, a photocopier. But heavily used office machines must meet a similar demand for reliability and efficiency... 'This distinction between telling a system how to do its job and telling the system the end result you want is very fundamental,' says Robert Morris, director of IBM’s Almaden Research Center in San Jose, CA. IBM is working to build what it calls 'autonomic' characteristics -- model-based features, as well as others that employ classic heuristic programming -- into products such as Web servers and storage networks. These features will allow the products to reconfigure themselves for optimal performance, depending on what’s being asked of them."

Real-World Applications of Qualitative Reasoning. By Yumi Iwasaki. IEEE Expert: Intelligent Systems (May/June 1997). A very good introduction with a wonderful example that begins with: "Qualitative inference - To get a flavor of what is meant by qualitative reasoning, consider this everyday scenario: You are confronted with a water-filled pan on a lit stove. You can easily predict that the pan will warm up, which will warm the water. At some point, the water will start to boil, and the pan might eventually become empty. To make these predictions, I need not tell you the exact values of the variables involved, such as the amount of water, the temperatures of the stove and the water, or the boiling temperature. Neither do you need to know the exact mathematical relations among the variables."

An interview with Ben Kuipers: Using Qualitative Reasoning. ByTom Centrella. IEEE Expert Online. "Recently, I spoke to Ben Kuipers about qualitative reasoning. Kuipers believes qualitative reasoning is the key to bringing about 'the next quantum jump in embedded intelligence in engineering and scientific problem solving.'"

Making Sense of Common Sense Knowledge- Benjamin Kuipers on using commonsense reasoning to make useful conclusions, or, finding gold nuggets in a pan of sand. Ubiquity; Volume 4, Number 45 (January 13 - 19, 2004). "A *qualitative* differential equation is just like an ordinary differential equation, except that you can use 'monotonic function' constraints like y = M^+(x)."

Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. By Benjamin Kuipers (1994). (Cambridge, MA: MIT Press) ISBN 0-262-11190-X. From the abstract: "Qualitative reasoning is one of the most vigorous areas in artificial intelligence. ... Qualitative models are more able than traditional models to express states of incomplete knowledge about continuous mechanisms. Qualitative simulation guarantees to find all possible behaviors consistent with the knowledge in the model. This expressive power and coverage are important in problem-solving for diagnosis, design, monitoring, and explanation."

Qualitative Reasoning and the Science of Design. By Yumi Iwasaki. For related info, see our Architecture & Design page.

Papers on Qualitative Reasoning from The Qualitative Reasoning Research Group at the University of Texas at Austin's Computer Science Department.

Generating Multiple New Designs from a Sketch. By Thomas F. Stahovich, Randall Davis and Howard Shrobe (1998). Artificial Intelligence: Vol. 104, pp. 211-264. To learn more about qualitative geometric reasoning and the SketchIT program, visit the

The Qualitative Reasoning Group at Northwestern University. While there, be sure to see their Qualitative Reasoning for Intelligent Agents project.

The Qualitative Reasoning Research Group at the University of Texas, part of the Artificial Intelligence Lab and the Computer Science Department, University of Texas at Austin, and supervised by Professor Benjamin Kuipers.

Other References Offline

AI Magazine special issue about Qualitative Reasoning. Winter 2003 (Volume 24, No. 4):

• 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.