"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).
Bredeweg, Bert (University of Amsterdam) | Liem, Jochem (University of Amsterdam) | Beek, Wouter (University of Amsterdam) | Linnebank, Floris (University of Amsterdam) | Gracia, Jorge (Universidad Politécnica de Madrid) | Lozano, Esther (Universidad Politécnica de Madrid) | Wißner, Michael (University of Augsburg) | Bühling, René (University of Augsburg) | Salles, Paulo (University of Brasília) | Noble, Richard (University of Hull) | Zitek, Andreas (University of Natural Resources and Applied Life Sciences) | Borisova, Petya (Institute of Biodiversity and Ecosystem Research) | Mioduser, David (Tel Aviv University)
Articulating thought in computer-based media is a powerful means for humans to develop their understanding of phenomena. We have created DynaLearn, an Intelligent Learning Environment that allows learners to acquire conceptual knowledge by constructing and simulating qualitative models of how systems behave. DynaLearn uses diagrammatic representations for learners to express their ideas. The environment is equipped with semantic technology components capable of generating knowledge-based feedback, and virtual characters enhancing the interaction with learners. Teachers have created course material, and successful evaluation studies have been performed. This article presents an overview of the DynaLearn system.
Qualitative information about spatial or temporal entities is represented by specifying qualitative relations between these entities. It is then possible to apply qualitative reasoning methods for tasks such as checking consistency of the given information, deriving previously unknown information or answering queries. Depending on the kind of information that is represented, qualitative reasoning methods might lead to incorrect results, and it is a topic of ongoing research efforts to determine when and why this occurs. In this paper we present two possible explanations for this behaviour: (1) the existence of implicit entities that we do not explicitly represent; (2) the existence of implicit constraints that have to be satisfied, but which are not explicitly represented. We show that both of these can lead to undetected inconsistencies. By making these implicit entities and constraints explicit, and by including them in the qualitative representation, we are able to solve problems that could not be solved qualitatively before. We present different examples of implicit entities and implicit constraints and an algorithm for solving them.
However, for many biological processes detailed quantitative information is not available, only qualitative or fuzzy statements about the nature of interactions. In a previous paper we have shown the applicability of qualitative reasoning methods for molecular biological regulatory processes. Now, we present a newly developed simulation environment, BioSim, that is written in Prolog using constraint logic programming techniques. The simulator combines the basic ideas of two main approaches to qualitative reasoning and integrates the contents of a molecular biology knowledge base, EcoCyc. We show that qualitative reasoning can be combined with automatic transformation of contents of genomic databases into simulation models to give an interactive modelling system that reasons about the relations and interactions of biological entities. This is demonstrated on the glycolytic pathway. Introduction Nearly all of the many existing simulation packages require users to specify their models in precise, numerical terms. For biologists this is a handicap since there is a large amount of mainly qualitative information emerging from genome mapping and functional genomic experiments that can not be included into numerical kinetic simulations.
In this paper we describe a qualitative approach for natural language communication about vehicle traffic. It is an intuitive and simple model that can be used as the basis for defining more detailed position descriptions and transitions. It can also function as a framework for relating different aggregation levels. We apply a diagrammatic abstraction of traffic that mirrors the different possible interpretations of it and with this the different mental abstractions that humans might make. The abstractions are kept in parallel and according to the communicative context it will be switched to the corresponding interpretation.
A central goal of qualitative physics is to provide a framework for organizing and using quantitative knowledge. One important use of quantitative knowledge is numerical simulation. While current numerical simulators are powerful, they are often hard to construct, do not reveal the assumptions underlying their construction, and do not produce explanations of the behaviors they predict. This paper shows how to combine qualitative and quantitative models to produce a new class of self-explanatory simulations which combine the advantages of both kinds of reasoning. Self-explanat*ory simulations provide the accuracy of numerical models and the interpretive power of qualitative reasoning.
ABSTRACT The central component of commonsense reasoning about causdity is the envisionment: a description of the behavior of a phvsical system that is derived from its structural description by qualitative simulation. Two problems with creating the envisionmcnt are the qualitative representation of quentlty and the detection of previously-unsuspcctcd points ot qualitative change. The representation presetlted here has the expressive power of differenil;ll equations, and the qualitarive envisionment strategy needed ior commonsense knowledge. A detailed example shows IICW it is able to detect a previously unsuspected point at which the system is in stable equilibrium. THE ENVISIONblENT Causal reasoning --- the ability to reason about how things work ___ is central to expert performance at problem-solving and expinnatii:n in many different areas.
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.