Abstract

Most research on computational scientific discovery has focused on developing an initial model, but an equally important task involves revising a model in response to new data. In this paper, we present an approach that represents candidate models as sets of quanti-tative processes and that treats revision as search through a model space which is guided by time-series observations and constrained by background knowledge cast as generic processes. We demonstrate our system’s ability on three different scientific domains and associated data sets. We also discuss its relation to other work on model revision and consider directions for additional research. 1

Citations