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. 1. Case-Based Reasoning CBR is a methodology that solves new problems by remembering solutions to past problems [1]. There are many algorithms used during the retrieval stage of CBR, including: Nearest Neighbor, locally weighted regression and inductive algorithms. In planning domains the use of CBR is called case-based planning (CBP). In this project problems are plans. A target case is a task assigned by the manager. Plan adaptation is done by a logic sub-system. Traditionally CBR has been conceptualized by the CBR cycle involving the processes: Retrieve, Reuse, Revise, and Retain [2]. Fig 1 shows a enhanced CBR cycle. Revision of Adaptation is between Reuse and Retain. Three types of sub-process can be categorized under the adaptation label: 1. Apply the solution to the problem. Check it is solved or not. Stay in the modification loop until problem is solved. 2. If the original retrieved case did solve the problem add a memo to a field about this new problem and retain it. 3. If the original case was modified then create a new case and retain it. In Fig. 1, the terms Reuse, Adaptation and Case are used in the following manner: Reuse = apply the best case to the problem Adapt = modify the best case, create and modify a new case Case = information about a past solved problem plus its solution Target case = information about the present unsolved problem Figure 1 is inspired by [1] and [2] and substitutes the traditional CBR cycle with a more accurate diagram. In this project, if a retrieved case is inefficient to solve the problem then the adaptation sub-system resolves inconsistencies using epistemic logic to create a new solution (a new case) that will be appended to the case-base.