Abstract: To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article we also show how REDUCER has successfully been applied to 3 different case studies.
Abstract: This paper discusses the causal explanation capability
of QRIOM, a tool aimed at supporting learning of organic chemistry
reactions. The development of the tool is based on the hybrid use of
Qualitative Reasoning (QR) technique and Qualitative Process
Theory (QPT) ontology. Our simulation combines symbolic,
qualitative description of relations with quantity analysis to generate
causal graphs. The pedagogy embedded in the simulator is to both
simulate and explain organic reactions. Qualitative reasoning through
a causal chain will be presented to explain the overall changes made
on the substrate; from initial substrate until the production of final
outputs. Several uses of the QPT modeling constructs in supporting
behavioral and causal explanation during run-time will also be
demonstrated. Explaining organic reactions through causal graph
trace can help improve the reasoning ability of learners in that their
conceptual understanding of the subject is nurtured.
Abstract: This paper discusses a qualitative simulator QRiOM
that uses Qualitative Reasoning (QR) technique, and a process-based
ontology to model, simulate and explain the behaviour of selected
organic reactions. Learning organic reactions requires the application
of domain knowledge at intuitive level, which is difficult to be
programmed using traditional approach. The main objective of
QRiOM is to help learners gain a better understanding of the
fundamental organic reaction concepts, and to improve their
conceptual comprehension on the subject by analyzing the multiple
forms of explanation generated by the software. This paper focuses
on the generation of explanation based on causal theories to explicate
various phenomena in the chemistry subject. QRiOM has been tested
with three classes problems related to organic chemistry, with
encouraging results. This paper also presents the results of
preliminary evaluation of QRiOM that reveal its explanation
capability and usefulness.