"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. IEEE Expert: Intelligent Systems (May/June 1997).
Project Summary: This project explores the use of qualitative physics to provide capabilities for intelligent agents. Understanding and using common sense reasoning about the physical world is a necessary prerequisite to creating many kinds of useful intelligent agents that collaborate with human partners to accomplish tasks. Examples of such tasks include damage control assessment, operations planning, sifting through on-line information for relevant data, teaching and tutoring, and developing complex scientific and engineering models. Our vehicle for these investigations is the creation of an experimental prototype, an Explanation Agent that accumulates explanations of how engineered systems work, and that uses this accumulated knowledge to answer questions and interactively formulate task-specific models of those systems.
The Qualitative Reasoning and Modelling (QRM) portal provides software tools (Garp3), documentation and support for users to build and simulate qualitative models. 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 (Bredeweg & Struss, 2003). Qualitative Reasoning has particularly value for developing, strengthening and further improving education and training on topics dealing with systems and their behaviour. Particularly the Garp3 workbench is being developed to support users in articulating, simulating and inspecting their conceptual knowledge of system's behaviour.
Chatter box abstraction eliminates chatter by performing a focused envisionment while Behavior Aggregation eliminations event occurrence branching. Describes a simulation technique that uses a cross between a state-based representation and a history-based representation. Models are decomposed into components and then each component is simulated separately. Temporal correlations between variables within different components is eliminated thus reducing many irrelevant distinctions within the behavioral description.
Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe Vmodel, a system we have created that uses visual representations and that enables middle-school students to create qualitative models. We discuss the design of the visual representation language, how Vmodel works, and evidence from school studies that indicate it is successful in helping students.
Traditional approaches to ecological modeling, based on mathematical equations, are hampered by the qualitative nature of ecological knowledge. In this article, we demonstrate that qualitative reasoning provides alternative and productive ways for ecologists to develop, organize, and implement models. We present a qualitative theory of population dynamics and use this theory to capture and simulate commonsense theories about population and community ecology. Advantages of this approach include the possibility of deriving relevant conclusions about ecological systems without numeric data; a compositional approach that enables the reusability of models representing partial behavior; the use of a rich vocabulary describing objects, situations, relations, and mechanisms of change; and the capability to provide causal interpretations of system behavior.
We argue that qualitative modeling provides a valuable way for students to learn. Two modelbuilding environments, VMODEL and HOMER/- VISIGARP, are presented that support learners by constructing conceptual models of systems and their behavior using qualitative formalisms. Both environments use diagrammatic representations to facilitate knowledge articulation. Preliminary evaluations in educational settings provide support for the hypothesis that qualitative modeling tools can be valuable aids for learning.
In general, modeling is a complex and creative task, and building qualitative models is no exception. In this article, we review approaches to learning qualitative models, either from numeric data or qualitative observations. We illustrate this using applications associated with systems control, in particular, the identification and optimization of controllers and human operator's control skill. We also review approaches that learn models in terms of qualitative differential equations.