A Methodological Framework of Human-Machine Co-Evolutionary Intelligence for Decision-Making Support of ATM

Xiao-Bing Hu
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引用次数: 1

Abstract

Despite of the success of artificial intelligent (AI) methods in many domains, there is big dilemma for AI when applying to air traffic management (ATM). That is AI researchers have long stated their AI methods are effective and reliable enough to handle many ATM problems, while human controllers still refuse to adopt such AI methods. We believe the dilemma is not about whether AI methods is effective or reliable enough, but about why human controllers should be replaced by AI methods. In other words, as long as an AI method aims to compete and replace human controllers, it will be confronted with the difficulty of not being accepted by human controllers. To address this dilemma, this paper proposes a new thinking about applying AI methods, i.e., an AI method should be developed in such a way of assisting human controllers, but never in the way of competing and replacing human controllers. This new thinking is called human-machine coevolutionary intelligence (HMCEI). A methodological framework of HMCEI is further developed for decision-making support of ATM, in order to demonstrate the concept of HMCEI is practicably possible.
面向ATM决策支持的人机协同进化智能方法框架
尽管人工智能(AI)方法在许多领域取得了成功,但人工智能在空中交通管理(ATM)中的应用却面临着很大的困境。也就是说,人工智能研究人员长期以来一直表示,他们的人工智能方法有效可靠,足以处理许多ATM问题,而人类控制者仍然拒绝采用这种人工智能方法。我们认为,困境不在于人工智能方法是否有效或足够可靠,而在于为什么人类控制器应该被人工智能方法取代。换句话说,只要一种人工智能方法的目标是竞争和取代人类控制器,它就会面临不被人类控制器接受的困难。为了解决这一困境,本文提出了应用人工智能方法的新思路,即人工智能方法应该以辅助人类控制器的方式发展,而不是以竞争和取代人类控制器的方式发展。这种新思维被称为人机共同进化智能(HMCEI)。为了证明HMCEI的概念在实际应用中是可行的,进一步开发了HMCEI的方法框架,为ATM的决策提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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