Machine autonomy: definition, approaches, challenges and research gaps.

Pascal, Chinedu; Starkey, Andrew

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.

Citation

EZENKWU, C.P. and STARKEY, A. 2019. Machine autonomy: definition, approaches, challenges and research gaps. In Arai, K., Bhatia, R. and Kapoor, S. (eds.) Intelligent computing: proceedings of the 2019 Computing conference, 16-17 July 2019, London, UK. Advances in intelligent systems and computing, 997. Cham: Springer [online], volume 1, pages 335-358. Available from: https://doi.org/10.1007/978-3-030-22871-2

• Conference Name: 2019 Computing conference
• Conference Location: London, UK
• Start Date: Jul 16, 2019
• End Date: Jul 17, 2019
• Acceptance Date: Jun 23, 2019
• Online Publication Date: Jun 23, 2019
• Publication Date: Dec 31, 2019
• Deposit Date: Mar 29, 2024
• Publicly Available Date: Apr 25, 2024
• Publisher: Springer
• Pages: 335-358
• Series Title: Advances in intelligent systems and computing
• Series Number: 997
• Series ISSN: 2194-5357; 2194-5365
• Book Title: Intelligent computing
• ISBN: 9783030228705
• DOI: https://doi.org/10.1007/978-3-030-22871-2_24
• Keywords: Autonomous agents; Automation; Robots; Artificial intelligence; Machine learning
• Public URL: https://rgu-repository.worktribe.com/output/2287889