| An outstanding problem is how to make decisions with uncertain and incomplete data from disparate sources without NP-hard algorithms. Here we introduce a new reasoning methodology, Fuzzy-Inferenced Decisionmaking (FIND), to solve this problem in polynomial time. In this methodology, a fuzzy-belief-state base (FBSB) is created from historical data of the states of a system by clustering the set of values for each variable into three clusters upon whose centers fuzzy set membership functions are defined. When given an incomplete and uncertain observation of the system state, the FBSB is then mined for fuzzy association rules to infer values for the missing data. After that, each case in the FBSB is matched against the inference-completed observation to retrieve the best matching fuzzy belief state record, which contains a decision as an extra variable. It is somewhat analogous to case-based reasoning, but uses fuzzification to ameliorate uncertainty and complete missing data. The test results on real world data prove the effectiveness of this methodology. |
