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Reasoning is a cognitive task ubiquitous everywhere: diagnosis, planning, scientific theory formation, speech understanding, etc. Unfortunately, solving reasoning problems is still difficult for most advanced machines since it is NPComplete. The use of artificial intelligence techniques, and especially neural networks, seems to be a promising direction which can solve these problems to a satisfactory level and in reasonable time scales. In this thesis, we distinguish two categories of causal reasoning; namely causetoeffect and effectto cause. Then, we propose algorithms to solve both categories and compare their performance with already existing proposals in the scientific literature.
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