Availability of powerful computation and communication technology as well as advances in artificial intelligence enable a new generation of complex, AI-intense systems and applications. Such systems and applications promise exciting improvements on a societal level, yet they also bring with them new challenges for their development. In this paper we argue that significant challenges relate to defining and ensuring behaviour and quality attributes of such systems and applications. We specifically derive four challenge areas from relevant use cases of complex, AI-intense systems and applications related to industry, transportation, and home automation: understanding, determining, and specifying (i) contextual definitions and requirements, (ii) data attributes and requirements, (iii) performance definition and monitoring, and (iv) the impact of human factors on system acceptance and success. Solving these challenges will imply process support that integrates new requirements engineering methods into development approaches for complex, AI-intense systems and applications. We present these challenges in detail and propose a research roadmap.
翻译:强大的计算和通信技术的可得性以及人工智能的进步使新一代复杂的人工智能系统和应用得以实现新一代的复杂、人工智能系统和应用。这些系统和应用在社会层面有望带来令人振奋的改进,但也给其发展带来了新的挑战。在本文件中,我们认为,重大挑战涉及界定和确保这类系统和应用的行为和质量特性。我们特别从与工业、运输和家庭自动化有关的复杂、人工智能系统和应用的相关使用案例中得出四个挑战领域:理解、确定和具体说明(一)背景定义和要求,(二)数据属性和要求,(三)绩效定义和监测,以及(四)人类因素对系统接受和成功的影响。解决这些挑战将意味着进程支持,将新的要求工程方法纳入复杂、人工智能系统和应用的发展方法。我们详细提出这些挑战并提出研究路线图。