Artificial nonmonotonic neural networks

In this paper, we introduce Artificial Nonmonotonic Neural Networks (ANNNs), a kind of hybrid learning systems that are capable of nonmonotonic reasoning. Nonmonotonic reasoning plays an important role in the development of artificial intelligent systems that try to mimic common sense reasoning, as exhibited by humans. On the other hand, a hybrid learning system provides an explanation capability to trained Neural Networks through acquiring symbolic knowledge of a domain, refining it using a set of classified examples along with Connectionist learning techniques and, finally, extracting comprehensible symbolic information. Artificial Nonmonotonic Neural Networks acquire knowledge represented by a multiple inheritance scheme with exceptions, such as nonmonotonic inheritance networks, and then can extract the refined knowledge in the same scheme. The key idea is to use a special cell operation during training in order to preserve the symbolic meaning of the initial inheritance scheme. Methods for knowledge initialization, knowledge refinement and knowledge extraction are introduced. We, also, prove that these methods address perfectly the constraints imposed by nonmonotonicity. Finally, performance of ANNNs is compared to other well-known hybrid systems, through extensive empirical tests.