Use of causal networks for problem-solving has been an active research area in AI, but current methods are limited because of their complexity and the difficulty of obtaining the needed causal knowledge. It is proposed here that these problems could be overcome by applying neural network methods directly to causal networks. To test this hypothesis, this project will 1) derive methods for formulating probabilistic inferences as highly parallel, strictly local computations, and 2) derive methods for learning causal relationships and their associated probabilities from case data. Unlike past neural modeling work, these methods will directly act on causal networks without requiring their own separate networks. Properties of these methods, such as systems convergence, will be analyzed using theories of stochastic processes and dynamic systems. Measurement of approximation errors will be developed. Computer simulations using different causal networks will be performed to verify the theoretical results and to select important parameters such as annealing and learning rates. The main contribution of this work is expected to be the development of new, potentially more efficient inference and learning methods that enjoy the advantages of both causal networks and neural networks. They can be used in expert systems for diagnosis, theory formation, decision support, and other applications involving probabilistic reasoning. It will also provide a concrete example of high level inference using neural network methods that make direct use of an AI representation.//
