Machine Autonomy

Definition, Approaches, Challenges and Research Gaps

Chinedu Pascal Ezenkwu, Andrew Starkey

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationIntelligent Computing
Subtitle of host publicationCompCom 2019, Proceedings
EditorsKohei Arai, Rahul Bhatia, Supriya Kapoor
Place of PublicationCham
PublisherSpringer
Pages335-358
Number of pages24
ISBN (Electronic)978-3-030-22871-2
ISBN (Print)978-3-030-22870-5
DOIs
Publication statusPublished - 2019
EventComputing Conference 2019 - London, United Kingdom
Duration: 16 Jul 201917 Jul 2019

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
ISSN (Print)2194-5357

Conference

ConferenceComputing Conference 2019
CountryUnited Kingdom
CityLondon
Period16/07/1917/07/19

Fingerprint

Autonomous agents
Robots
End effectors
Industrial plants
Railroad cars

Keywords

  • Autonomous agent
  • Machine Autonomy
  • Automation
  • Robots
  • Artificial Intelligence
  • Learning
  • Artificial intelligence
  • Machine autonomy

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Ezenkwu, C. P., & Starkey, A. (2019). Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. In K. Arai, R. Bhatia, & S. Kapoor (Eds.), Intelligent Computing: CompCom 2019, Proceedings (pp. 335-358). (Advances in Intelligent Systems and Computing). Cham: Springer . https://doi.org/10.1007/978-3-030-22871-2_24

Machine Autonomy : Definition, Approaches, Challenges and Research Gaps. / Ezenkwu, Chinedu Pascal; Starkey, Andrew.

Intelligent Computing: CompCom 2019, Proceedings. ed. / Kohei Arai; Rahul Bhatia; Supriya Kapoor. Cham : Springer , 2019. p. 335-358 (Advances in Intelligent Systems and Computing).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Ezenkwu, CP & Starkey, A 2019, Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. in K Arai, R Bhatia & S Kapoor (eds), Intelligent Computing: CompCom 2019, Proceedings. Advances in Intelligent Systems and Computing, Springer , Cham, pp. 335-358, Computing Conference 2019, London, United Kingdom, 16/07/19. https://doi.org/10.1007/978-3-030-22871-2_24
Ezenkwu CP, Starkey A. Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. In Arai K, Bhatia R, Kapoor S, editors, Intelligent Computing: CompCom 2019, Proceedings. Cham: Springer . 2019. p. 335-358. (Advances in Intelligent Systems and Computing). https://doi.org/10.1007/978-3-030-22871-2_24
Ezenkwu, Chinedu Pascal ; Starkey, Andrew. / Machine Autonomy : Definition, Approaches, Challenges and Research Gaps. Intelligent Computing: CompCom 2019, Proceedings. editor / Kohei Arai ; Rahul Bhatia ; Supriya Kapoor. Cham : Springer , 2019. pp. 335-358 (Advances in Intelligent Systems and Computing).
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