Learning for Universal Logic Operation Selection

Being an effective knowledge representation while using in a noisy, changing environment or system with full of sudden occurring events, symbolic logic should become more adaptive. In this paper, we introduce a method of fuzzy logical operator selection, which is mainly based on learning from examples. The adaptability of logic partially depends on the selective adapting of logical operators. In the binary logic, logical operation is fixed. Triangular norm theory made fuzzy logic and universal logic have the other choices on operation selection, but theoretically, the policy of choosing the proper operators in a special practical application among T-norm or T-conorm cluster is still a problem. General coefficient mechanism is a practical method for operation selection. Furthermore we introduce learning mechanism to keep the logic system more adaptive to the environment