Building custom, adaptive and heterogeneous multi-agent systems for semantic information retrieval using organizational-multi-agent systems engineering, O-MaSE

The paper presented here intends to demonstrate the usage of Organizational Multi Agent Software Engineering Methodology now on referred to as O-MaSE for the purpose of analyzing, modeling and designing custom intelligent agents for an organization specifically relevant for context aware systems. The paper discusses Genomic Information Retrieval. O-MaSE is an extension over MaSE and is a more comprehensive agent development methodology as compared to existing methodologies. The biologists generate a big amount of unprocessed data in sequencing organisms and the data is distributed and heterogeneous, dispersed in varied formats and diverse platforms. The need is to devise a system that fills this gap to quite an extent and this is carried through the help of O-MaSE and its modeling tool, Agent Tool 3, AT³. A method to model the concepts required for building a multi-agent system for organizations is presented including goals, roles, agents, protocols, plans and the mapping of agents over roles.