We investigate the problem of descriptive learning-learning rules that describe the underlying structure of a domain-in rich, qualitative worlds. Previous approaches to this problem have searched for laws in top-down, enumerative fashion. We present algorithms that belong to an alternative, data-driven search paradigm. In our algorithms, search is guided not by relationships between the forms of the hypothesized rules, but by correlations in the data they represent. We exploit anomalies in this data, hypothesizing that that patterns that are unlikely to have arisen by chance must represent...
We investigate the problem of descriptive learning-learning rules that describe the underlying structure of a domain-in rich, qualitative worlds. Prev...