The researcher will investigate the acquisition of general theory of causality by observing examples of events and their consequences. A theory of causality is information about the principles that lead one to believe that an action has a necessary consequence. For example, people tend to select a cause which is close in space and time to an effect. The work builds on prior implementation of a computer model which uses a theory of causality as an explicit bias to facilitate the acquisition of a theory of causation (i.e., inference rules encode knowledge of the effect of actions, such as "glass objects break when struck with sufficient force."). The new work is an investigation of computational models for acquiring these general principles from examples. Input to the learning program will consists of a temporal sequence of propositions representing the states of the world. The input representation will be constructed by simulation (to allow systematic experimentation by varying parameters such as the amount of noise in the data) and by hand coding examples of causal changes from children's introductory science texts (to insure that the learning program will be able to account for a wide variety of common causal relationships).
