A new approach is presented for handling missing values in multi-level agent-based simulation (MABS) at micro-level by using truth tables and logical relations. Although micro-level simulation is a vast field to use logical relations with truth tables to find missing values but it takes values into account at individual levels. We used databases in form of tables to extract missing values. We have defined logical relations according to scenario by interacting with truth tables to find appropriate missing values. In this research we have concluded that missing values would be handled in different ways, such as: Artificial neural network, K-nearest neighbor, Statistical method and Data mining; etc… These methods have not facilitated in finding appropriate missing values as we saw in literature. We have created a method that can find missing values and produce good results. We have run our method on a specific scenario to check the efficiency of input handling that motivated us to arrange database in a proper way to handle missing values along.