Knowledge Representation and Simulation of Nucleophilic Substitution Reaction using Qualitative Reasoning Approach

This paper describes an artificial intelligence (AI) technique called qualitative reasoning (QR) for knowledge representation and simulation of organic reaction mechanisms. Even though there are many applications of AI technique in organic chemistry, none has involved QR approach in problem solving. The primary goal of QR research is to understand human-like commonsense reasoning. A software architecture has been designed which consists of two main components, the reasoning and the explanation modules. In qualitative modeling, chemical intuition and chemical commonsense that are required to understand the behavior of reaction mechanisms are represented using the modeling constructs of a QR ontology called qualitative process theory (QPT). Several model fragments have been developed that served as embedded intelligence to the system. A simulation scenario based on these models is presented together with explanation generation for a particular aspect of the system behavior