Artificial Intelligence algorithms can be divided into two groups according to the type of problems they solve. Knowledge-intensive domains contain explicit knowledge, whereas knowledge-poor domains contain implicit knowledge. Rules, decision trees and logical methods are more suitable for the first type. Neural networks and case-based reasoning (CBR) are more suitable for the second type. This project combines the inferencing power of epistemic logic (type 1) in the adaptation of CBR with the performance of case-based planning (type 2). This method is proved to be more efficient then using planning algorithms alone. Here a simulated robot is assigned to deliver parts in a factory. The robot needs to plan the path that it should choose to achieve its goals and uses epistemic logic to solve problems. Planning algorithms are computationally expensive. CBR, using KNN is used to make the process faster. A STRIPS planner creates plans. The manager defines the problem, KNN extracts a plan and a logic sub-system adapts it according to belief revision theorems. The new plan is retained for future use.