一句话：回顾从列表、高阶函数、Actor 到信息物理智能体的效应推理发展，总结核心原理并展望未来挑战。
该文回顾了带效应程序的推理理论演进。早期理论关注列表等可变数据的基本操作；随后需处理高阶编程，将函数作为一等对象加入可变数据；Actor 又引入异步交互的分布式开放系统；信息物理智能体则进一步带来不确定性、故障以及物理与逻辑效应，并通过传感器和执行器产生范围更广的效应，如自动驾驶、无人机和智能医疗设备。作者以回顾方式提炼各阶段的关键原理与技术，最后给出未来研究挑战。
Theories for reasoning about programs with effects initially focused on basic manipulation of lists and other mutable data. The next challenge was to consider higher-order programming, adding functions as first class objects to mutable data. Reasoning about actors added the challenge of dealing with distributed open systems of entities interacting asynchronously. The advent of cyber-physical agents introduces the need to consider uncertainty, faults, physical as well as logical effects. In addition cyber-physical agents have sensors and actuators giving rise to a much richer class of effects with broader scope: think of self-driving cars, autonomous drones, or smart medical devices. This paper gives a retrospective on reasoning about effects highlighting key principles and techniques and closing with challenges for future work.
@article{paperbot800,
title = {Reasoning about effects: from lists to cyber-physical agents},
author = {Ian A. Mason and Carolyn L. Talcott},
journal = {Logical Methods in Computer Science},
volume = {Volume 15, Issue 2},
year = {2019},
doi = {10.23638/lmcs-15(2:8)2019}
}