Categorizing methods for integrating machine learning with executable specifications

作     者：David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF 

作者机构：Department of Computer Science and Applied MathematicsWeizmann Institute of Science Department of Software and Information Systems EngineeringBen-Gurion University of the Negev 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2024年第67卷第1期

页      面：5-19页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by National Natural Science Foundation of China (NSFC) and Israel Science Foundation (ISF)(Grant No.3698/21) provided by a research grant from the Estate of Harry Levine,the Estate of Avraham Rothstein,Brenda Gruss,and Daniel Hirsch,the One8 Foundation,Rina Mayer,Maurice Levy,and the Estate of Bernice Bernath 

主　　题：machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey 

摘      要：Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.