Transforming between Propositions and Features: Bridging the Gap

It is notoriously difficult to simultaneously deal with both probabilistic and structural representations in A.I., particularly because probability necessitates a uniform representation of the training examples. In this paper, we show how to build fully-specified probabilistic models from arbitrary propositional case descriptions about terrorist activities. Our method facilitates both reasoning and learning. Our solution is to use structural analogy to build probabilistic generalizations about those cases. We use these generalizations as a framework for mapping the structural representations, which are well-suited for reasoning, into features, which are well-suited for learning, and back again. Finally, we demonstrate how probabilistic generalizations are an excellent bridge for joining reasoning and learning by using them to perform a traditional machine learning technique, Bayesian network modeling, over arbitrarily high order structural data about terrorist actions, and further, we discuss how this might be used to facilitate automatic knowledge acquisition.