Deceiving Autonomous Drones

Deceiving Autonomous Drones

William Hutchinson (Edith Cowan University, Joondalup, Australia)
Copyright: © 2020 |Pages: 14
DOI: 10.4018/IJCWT.2020070101


This speculative article examines the concept of deceiving autonomous drones that are controlled by artificial intelligence (AI) and can work without operational input from humans. This article examines the potential of autonomous drones, their implications and how deception could possibly be a defence against them and /or a means of gaining advantage. It posits that officially, no truly autonomous drone is operational now, yet the development of AI and other technologies could expand the capabilities of these devices, which will inevitably confront society with a number of deep ethical, legal, and philosophical issues. The article also examines the impact of autonomous drones and their targets in terms of the power/deception nexus. The impact of surveillance and kinetic impacts on the target populations is investigated. The use of swarms can make deception more difficult although security can be breached. The Internet of Things can be considered as based on the same model as a swarm and its impact on human behaviour indicates that deception or perhaps counter-deception should be considered as a defence. Finally, the issues raised are outlined. However, this article does not provide definitive answers but, hopefully, exposes a number of issues that will stimulate further discussion and research in this general area.
Article Preview

1. Introduction

In the last two decades, the term ‘drone’ usually meant a flying robot, but this has been expanded to include any mobile robot. In this paper, ‘drone’ and ‘robot’ are used interchangeably. They are now found in the aerial, terrestrial, aquatic and space environments. Combined with artificial intelligence and a myriad of sensors, they have become formidable weapons and surveillance platforms (see Dougherty, 2015 for the range involved). In fact, defence against them is difficult for all but the most well-resourced entities. This phenomenon stimulated the start of this research, which concentrates on autonomous rather than just automatic robots. The US Department of Defence (US DOD, 2014, p. 15) gives a simple explanation that an autonomous robot as: “when the aircraft [drone] is under remote control, it is not autonomous. And when it is autonomous, it is not under remote control.” In other words, it is independent of humans for its operating actions.

When considering the ‘intelligence’ and ‘knowledge’ aspects of this topic, it is useful to look at the types of systems that have been developed as these types of systems. Cummings (2017) states there is a hierarchy of knowledge systems starting with skills-based behaviours, then rules-based, then knowledge-based and finally expertise-based. Skills-based relies on the perception-cognition-action loop and can be automated without much difficulty. As the need for complexity increases, multiple and compound processes can be accomplished by Rules-based learning. The next two levels of system require a higher level of learning where Knowledge-based reasoning is needed where the stored set of rules does not match the existing environment, so a new set of rules have to be created. Expert-based systems use judgement and intuition. Although the move from automated to autonomous systems changes at the rules-based level, it is really at the Expert level that solutions to the ambiguities in the environment can start to be trusted. Cummings (ibid) contends that, there are no truly reliable autonomous systems relying on Knowledge-based or Expert based systems, in operation currently. Hence, whilst there are many automated systems there are not truly, fully autonomous ones.

Complete Article List

Search this Journal:
Open Access Articles: Forthcoming
Volume 11: 4 Issues (2021): Forthcoming, Available for Pre-Order
Volume 10: 4 Issues (2020): 3 Released, 1 Forthcoming
Volume 9: 4 Issues (2019)
Volume 8: 4 Issues (2018)
Volume 7: 4 Issues (2017)
Volume 6: 4 Issues (2016)
Volume 5: 4 Issues (2015)
Volume 4: 4 Issues (2014)
Volume 3: 4 Issues (2013)
Volume 2: 4 Issues (2012)
Volume 1: 4 Issues (2011)
View Complete Journal Contents Listing