@inproceedings{2942026a286d48988b14f350982881ba,
title = "Machine Autonomy: Definition, Approaches, Challenges and Research Gaps",
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{\textquoteright}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.",
keywords = "Autonomous agent, Machine Autonomy, Automation, Robots, Artificial Intelligence, Learning, Artificial intelligence, Machine autonomy",
author = "Chinedu Ezenkwu and Andrew Starkey",
year = "2019",
doi = "10.1007/978-3-030-22871-2_24",
language = "English",
isbn = "978-3-030-22870-5",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer ",
pages = "335--358",
editor = "Kohei Arai and Rahul Bhatia and Supriya Kapoor",
booktitle = "Intelligent Computing",
note = "Computing Conference 2019 ; Conference date: 16-07-2019 Through 17-07-2019",
}