Hypothesizing and Refining Causal Models,
MASSACHUSETTS INST OF TECH CAMBRIDGE ARTIFICIAL INTELLIGENCE LAB
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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 naive 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 rehypothesize. 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 of casual hypotheses to generalize its casual models. Finally, a simple example involving an analogy illustrates another way to repair incomplete causal models.