Hypothesizing and Refining Causal Models
An important common sense competence is the ability to hypothesize causal relations. This paper presents a set of constraints which make the problem of formulating causal hypotheses about simple physical systems a tractable one. The constraints include: (1) a temporal and physical proximity requirement, (2) a set of abstract causal explanations for changes in physical systems in terms of dependences between quantities, and (3) a teleological assumption that dependences in designed physical systems are functions. These constraints were embedded in a learning system which was tested in two domains: a sink and a toaster. The learning system successfully generated and refined naï¶¥ causal models of these simple physical systems. The causal models which emerge from the learning process support causal reasoning- explanation, prediction, and planning. Inaccurate predictions and failed plans in turn indicate deficiencies in the causal models and the need to re-hypothesize. Thus learning supports reasoning which leads to further learning. The learning system makes use of standard inductive rules of inference as well as the constraints on causal hypotheses to generalize its causal models. Finally, a simple example involving an analogy illustrates another way to repair incomplete causal models.