LEARNING REACTION MECHANISMS THROUGH QUALITATIVE SIMULATION: TOWARDS THE QUALITATIVE REASONING APPROACH

Abstract

This work discusses the application of an Artificial Intelligence (AI) technique called Qualitative Reasoning (QR) coupled with the Qualitative Process Theory (QPT) ontology to model, simulate and explain chemical behaviours of reaction mechanisms. We have tested the new approach on two types of organic mechanisms under nucleophilic substitution reaction. This paper describes one specific type of the mechanisms called SN1 to demonstrate how the qualitative models can be constructed for chemical processes such as “make-bond” and “break-bond”. The construction of these models is automated based on a set of QR algorithms. Each qualitative model represents some aspects of the chemical theories that are required to understand the subject. Several cases of causal explanation generation are also included to demonstrate how natural behavioural explanation can be derived from the automatically constructed models. A prototype called QRIOM based on the QR technology will serve as cognitive tool fostering the acquisition of conceptual understanding when learning about reaction mechanisms.