Qualitative Reasoning


Qualitative Reasoning for Intelligent Agents

AITopics Original Links

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.


Home :: The Qualitative Reasoning and Modelling Portal

AITopics Original Links

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.


Papers on Qualitative Reasoning

AITopics Original Links

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.


Qualitative Reasoning: Everyday, Pervasive, and Moving Forward -- A Report on QR-15

AI Magazine

The 28th International Workshop on Qualitative Reasoning (QR-15) presented advances toward reasoning tractably with massive qualitative and quantitative models, automatically learning and reasoning about continuous processes, and representing knowledge about space, causation, and uncertainty.


VModel: A Visual Qualitative Modeling Environment for Middle-school Students

AI Magazine

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.


Qualitative Reasoning about Population and Community Ecology

AI Magazine

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.


Current Topics in Qualitative Reasoning

AI Magazine

In this editorial introduction to this special issue of AI Magazine on qualitative reasoning, we briefly discuss the main motivations and characteristics of this branch of AI research. We also summarize the contributions in this issue and point out challenges for future research.


Qualitative Modeling in Education

AI Magazine

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.


Mathematical Foundations of Qualitative Reasoning

AI Magazine

We examine different formalisms for modeling qualitatively physical systems and their associated inferential processes that allow us to derive qualitative predictions from the models. We highlight the mathematical aspects of these processes along with their potential and limitations.


Learning Qualitative Models

AI Magazine

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.