Epistemic Logic and Planning

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. 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 phase of CBR with the performance of case-based planning (type 2). This method is proved to be more efficient then using planning algorithms alone. Planning algorithms are computationally expensive. CBR, using a nearest neighbor algorithm (KNN) is used to make the process faster. A STRIPS planner creates plans for the case-base of a robot that delivers parts in a factory. The manager defines the problem, KNN extracts a plan and a logic sub-system adapts it according to belief revision theorems to resolve the plan inconsistencies.