The paper considers columns-based intelligent systems that work under conditions of incomplete information, that is, input patterns are represented by their sub-patterns. The definition of direct and inverse problems under incomplete information is given. The solution of these problems is shown using the method of element-wise comparison of patterns and the intersection method. A relation between system’s ability to work under incomplete information and prediction is shown.