Applying the connectionist inductive learning and logic programming system to power system diagnosis

The connectionist inductive learning and logic programming system, C-IL²P, integrates the symbolic and connectionist paradigms of artificial intelligence through neural networks that perform massively parallel logic programming and inductive learning from examples and background knowledge. This work presents an extension of C-IL²P that allows the implementation of extended logic programs in neural networks. This extension makes C-IL²P applicable to problems where the background knowledge is represented in a default logic. As a case example, we have applied the system for fault diagnosis of a simplified power system generation plant, obtaining good preliminary results