Explainable Artificial Intelligence for Workflow Verification in Visual IoT/Robotics Programming Language Environment  

作　　者：Gennaro De Luca Yinong Chen 

机构地区：[1]Arizona State University,Tempe,AZ 85281 USA

出　　处：《Journal of Artificial Intelligence and Technology》2021年第1期21-27,共7页人工智能技术学报（英文）

基　　金：supported by general funding at IoT and Robotics Education Lab and FURI program at Arizona State University.

摘　　要：Teaching students the concepts behind computational thinking is a difficult task,often gated by the inherent difficulty of programming languages.In the classroom,teaching assistants may be required to interact with students to help them learn the material.Time spent in grading and offering feedback on assignments removes from this time to help students directly.As such,we offer a framework for developing an explainable artificial intelligence that performs automated analysis of student code while offering feedback and partial credit.The creation of this system is dependent on three core components.Those components are a knowledge base,a set of conditions to be analyzed,and a formal set of inference rules.In this paper,we develop such a system for our own language by employing π-calculus and Hoare logic.Our detailed system can also perform self-learning of rules.Given solution files,the system is able to extract the important aspects of the program and develop feedback that explicitly details the errors students make when they veer away from these aspects.The level of detail and expected precision can be easily modified through parameter tuning and variety in sample solutions.

关 键 词：explainable AI Π-CALCULUS VIPLE education 

分 类 号：H31[语言文字—英语]