Simple approach to case-based reasoning for data navigation tasks

摘要

An architecture and key algorithms are presented that simplify the implementation and improve the functionality of knowledge-based systems that employ case-based reasoning for data navigation tasks (e.g., solving help desk problems using a database of past problem instances). The various embodiments of the invention improve on the state-of-the-art in several respects: (a) provides greatly increased flexibility in choosing attributes relevant to a case in that choices are made dynamically at case acquisition instead of statically when the knowledge- based system is designed; (b) increased system flexibility and decreased system complexity by not requiring designers to specify the manner in which attribute values are compared; (c) simplified system design by avoiding the use of similarity metrics; (d) allows for the incremental acquisition of data in problem instances, instead of requiring that all potentially relevant data be collected; and (e) provides a way to explain why a problem solution was chosen. Case acquisition is accomplished by having end-users replay the steps taken to solve a problem instance. Each such step constitutes a navigation element that consists of: (i) a test expressing the conditions under which the navigation is done; (ii) a text description explaining the step; and (iii) a database query expressing the next navigation. Algorithms are presented for case acquisition, case retrieval, and case removal. The case repository is structured as a graph of navigation elements that, under appropriate circumstances, combines into a single node those navigation elements common to multiple cases.