Enhancing the initial requirements capture of Multi-Agent Systems through conceptual graphs

A key purpose of Multi-Agent Systems (MAS) is to assist humans make better decisions given the vast and disparate information that global systems such as the Web have enabled. The resulting popularity of Agent-Oriented Software Engineering (AOSE) thus demands a methodology that facilitates the development of robust, scalable MAS implementations that recognise real-world semantics. Using an exemplar in the Community Healthcare domain and Conceptual Graphs (CG), we describe an AOSE approach that elicits the hitherto hidden requirements of a system much earlier in the software development lifecycle, whilst also incorporating model-checking to ensure robustness. The resulting output is then available for translation into Agent Oriented Unified Modelling Language (AUML) and further developed using the agent development toolkit of choice.