3D Scientific Data Interpretation Using Cooperating Agents

Abstract

Many organisations collect vast quantities of 3D scientific data in volumetric form for a range of purposes including resource exploration, market forecasting and process modelling. Traditionally, this data has been interpreted by human experts with only minimal software assistance. However such manual interpretation is a painstakingly slow and tedious process. Moreover, since interpretation involves subjective judgements and each interpreter has different scientific knowledge and experience, formulation of an effective interpretation often requires the co-operation of numerous such experts. Hence there is a pressing need for a software system in which individual interpretations can be generated automatically, and then refined through the use of co-operative reasoning and information sharing. To this end, a prototype system, named SurfaceMapper, has been developed in which a community of co-operating agents automatically locate and display interpretations in a volume of 3D scientific data. The challenges and experiences in designing and building such a system are discussed.