Categorizing methods for integrating machine learning with executable specifications  

作　　者：David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF 

机构地区：[1]Department of Computer Science and Applied Mathematics,Weizmann Institute of Science,Rehovot 76100,Israel [2]Department of Software and Information Systems Engineering,Ben-Gurion University of the Negev,Beer Sheva 8410501,Israel

出　　处：《Science China(Information Sciences)》2024年第1期1-15,共15页中国科学（信息科学）（英文版）

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。

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 readily.That promise is already delivering extremely impressive results in a variety of areas.However,while DL-enabled systems achieve excellent performance,they are far from perfect.It has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto encountered.Furthermore,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human engineers.At 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 behavior.In 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 here.We 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 BP.We conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关 键 词：machine learning artificial intelligence grey box learning domain knowledge RULES behavioral programming deep reinforcement learning SURVEY 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]