Machine Autonomy: Definition, Approaches, Challenges and Research Gaps

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Abstract

The processes that constitute the designs and implementations of AI systems such as self-driving cars, factory robots and so on have been mostly hand-engineered in the sense that the designers aim at giving the robots adequate knowledge of its world. This approach is not always efficient especially when the agent’s environment is unknown or too complex to be represented algorithmically. A truly autonomous agent can develop skills to enable it to succeed in such environments without giving it the ontological knowledge of the environment a priori. This paper seeks to review different notions of machine autonomy and presents a definition of autonomy and its attributes. The attributes of autonomy as presented in this paper are categorised into low-level and high-level attributes. The low-level attributes are the basic attributes that serve as the separating line between autonomous and other automated systems while the high-level attributes can serve as a taxonomic framework for ranking the degrees of autonomy of any system that has passed the low-level autonomy. The paper reviews some AI techniques as well as popular AI projects that focus on autonomous agent designs in order to identify the challenges of achieving a true autonomous system and suggest possible research directions.

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APA

Ezenkwu, C. P., & Starkey, A. (2019). Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. In Advances in Intelligent Systems and Computing (Vol. 997, pp. 335–358). Springer Verlag. https://doi.org/10.1007/978-3-030-22871-2_24

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