Decision making is one of the central problems in the artificial intelligence and specifically in robotics. In most cases this problem comes with uncertainty both in the data received by the decision maker/agent and in the actions performed in the environment. One effective method to solve this problem is to model the environment and the agent as a Partially Observable Markov Decision Process(POMDP). A POMDP has a wide range of applications such as: Machine Vision, Marketing, Network troubleshooting, Medical diagnosis etc. In recent years, There has been a significant interest in the developing techniques for finding policies for (POMDPs). We consider a new technique, called Recursive Point Filter (RPF) based on the Incremental Pruning(IP) POMDP solver to introduce an alternative method to the Linear Programming (LP) filter.