Learning causal patterns: Making a transition from data-driven to theory-driven learning

Abstract

We describe an incremental learning algorithm, called theory-driven learning, that creates rules to predict the effect of actions. Theory-driven learning exploits knowledge of regularities among rules to constrain learning. We demonstrate that this knowledge enables the learning system to rapidly converge on accurate predictive rules and to tolerate more complex training data. An algorithm for incrementally learning these regularities is described and we provide evidence that the resulting regularities are sufficiently generally to facilitate learning in new domains. The results demonstrate that transfer from one domain to another can be achieved by deliberately overgeneralizing rules in one domain and biasing the learning algorithm to create new rules that specialize these overgeneralizations in other domains.