In this episode, we introduce one of the most important tools in the description of logic operations: the truth table. Not only do truth tables allow us to describe a logic operation, they provide a means for us to prove logical equivalence.

In this thesis we presented a new direction for handling missing values in multi 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. Our literature review suggested us a method for input handling by using electronically saved truth tables. We have defined logical relations according to scenario by interacting with truth tables to find appropriate missing values. Our conclusions suggested a method which can find appropriate values for input parameters when they are missing. Accurate results have been gained according to updated database. In this thesis 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.