Knowledge Planning and Learned Personalization for Web-Based Case Adaptation

A knowledge planning approach in which a planner takes explicit knowledge goals as input and generates a plan for satisfying them from a set of general operators is proposed to develop flexible, general and extensible procedures for gathering adaptation-relevant knowledge from the Web.

Abstract

How to endow case-based reasoning systems with effective case adaptation capabilities is a classic problem. A significant impediment to developing automated adaptation procedures is the difficulty of acquiring the required knowledge. Initial work on WebAdapt [1] proposed addressing this problem with "just-in-time" knowledge mining from Web sources. This paper addresses two key questions building on that work. First, to develop flexible, general and extensible procedures for gathering adaptation-relevant knowledge from the Web, it proposes a knowledge planning [2] approach in which a planner takes explicit knowledge goals as input and generates a plan for satisfying them from a set of general operators. Second, to focus selection of candidate adaptations from the potentially enormous space of possibilities, it proposes personalizing adaptations based on learned information about user preferences. Evaluations of the system are encouraging for the use of knowledge planning and learned preference information to improve adaptation performance.