Cooperating agents for 3-D scientific data interpretation

Many organizations collect vast quantities of three-dimensional (3-D) scien tific data in volumetric form for a range of purposes, including resource e xploration, market forecasting, and process modeling. Traditionally, these data have been interpreted by human experts With only minimal software assi stance. However, such manual interpretation is a painstakingly slow and ted ious process. Moreover, since interpretation involves subjective judgments and each interpreter has different scientific knowledge and experience, for mulation of an effective interpretation often requires the cooperation of n umerous such experts, Hence, there is a pressing need for a software system in which individual interpretations can be generated automatically and the n refined through the use of cooperative reasoning and information sharing. To this end, a prototype system, Surface Mapper, has been developed in whi ch a community of cooperating software agents automatically locate and disp lay interpretations in a volume of 3-D scientific data. The challenges and experiences in designing and building such a system are discussed, Particul ar emphasis is given to the agents' interactions and an empirical evaluatio n of the effectiveness of different cooperation strategies is presented.