Knowledge-Intensive Teaching Assistance System for Industrial Robots Using Case-Based Reasoning and Explanation-Based Learning

摘要

This paper presents a novel human-system collaborative robot-programming platform, where case-based reasoning (CBR) and explanation-based learning (EBL) are integrated together. CBR takes advantage of its unique CBR cycle to achieve the knowledge acquisition and reuse in the form of case, realizing efficient robot programming with the support of experienced human experts. EBL optimizes the rule structure in the knowledge base through learning in retrieving speedup rules in order to accelerate the case adaptation process. Feasibility of this proposal is verified via a number of experiments that allow the system to output both schemata for generalized robot programming tasks whose calculation rules are adaptive enough so that it can be applied to novel task inputs. Moreover, it is shown that our system is adaptive to the increase of the cases processed and be able to tackle with the learning utility problem.