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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Smithers, T., Laboratory VUBAI: Taking Eliminative Materialism Seriously: A Methodology for Autonomous Systems Research. Artificial Intelligence Laboratory, Vrije Universiteit Brussel, Brussels (1992)
Franklin, S.: Artificial Minds. MIT Press, Cambridge, p. 449 (1995)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology, p. 320, MIT Press, Cambridge (2000)
Froese, T., Virgo, N., Izquierdo, E.: Autonomy: a review and a reappraisal. In: Advances in Artificial Life, pp. 455–464. Springer, Heidelberg (2007)
Harbers, M., Peeters, M.M.M., Neerincx, M.A.: Perceived autonomy of robots: effects of appearance and context. In: Aldinhas Ferreira, M.I., Silva Sequeira, J., Tokhi, M.O., Kadar, E., Virk, G.S. (eds.) A World with Robots: International Conference on Robot Ethics: ICRE 2015, pp. 19–33. Springer, Cham (2017)
Bradshaw, J.M., Hoffman, R.R., Woods, D.D., Johnson, M.: The seven deadly myths of “autonomous systems”. IEEE Intell. Syst. 28, 54–61 (2013)
Rich, E., Knight, K.: Artificial Intelligence. McGraw-Hill Higher Education, p. 640 (1990)
Roe, A.W., Sur, M.: Visual projections routed to the auditory pathway in ferrets: receptive fields of visual neurons in primary auditory cortex. J. Neurosci. 12(9), 3651–3664 (1992)
Scharre, P., Horowitz, M.: An introduction to autonomy in weapon systems. In: Ethical Autonomy—working paper (2015)
Lane, D.M.: Persistent autonomy artificial intelligence or biomimesis? 2012 IEEE/OES Autonomous Underwater Vehicles (AUV) pp. 1–8 (2012)
Pernar, S.: The Evolutionary Perspective-a Transhuman Philosophy (2015)
Lomonova, E.A.: Advanced actuation systems—state of the art: fundamental and applied research. In: International Conference on Electrical Machines and Systems pp. 13–24 (2010)
Hong, J., Suh, E., Kim, S.: Context-aware systems: a literature review and classification. Expert. Syst. Appl. 36, 8509–8522 (2009)
Viterbo, J., Sacramento, V., Rocha, R., Baptista, G., Malcher, M., Endler, M.A.: Middleware architecture for context-aware and location-based mobile applications. In: 2008 32nd Annual IEEE Software Engineering Workshop pp. 52–61 (2008)
Gui, F., Zong, N., Adjouadi, M.: Artificial intelligence approach of context-awareness architecture for mobile computing. Sixth International Conference on Intelligent Systems Design and Applications 2, 527–533 (2006)
Haugeland, J.: Artificial Intelligence: The Very Idea. Bradford, Cambridge, MA (1985)
Pierce, D., Kuipers, B.J.: Map learning with uninterpreted sensors and effectors. Artificial Intelligence 92, 169–227 (1987)
Chaput, H.H.: The constructivist learning architecture: A model of cognitive development for robust autonomous robots. PhD. AI Laboratory, The University of Texas at Austin. Supervisors: Kuipers and Miikkulainen (2004). https://pdfs.semanticscholar.org/7bb4/18868b2c95443243f5f7a5b9a5a15d342570.pdf. Accessed 12 Aug 2018
Oudeyer, P.Y., Kaplan, F., Hafner, V.V.: Intrinsic motivation systems for autonomous mental development. IEEE Trans. Evol. Comput. 11, 265–286 (2007)
Barto, A.G.: Intrinsic motivation and reinforcement learning. In: Baldassarre, G., Mirolli, M. (eds.) Intrinsically Motivated Learning in Natural and Artificial Systems. pp. 17–47. Springer, Heidelberg (2013)
Santucci, V.G., Baldassarre, G., Mirolli, M.: GRAIL: a goal-discovering robotic architecture for intrinsically-motivated learning. IEEE Trans. Cognit. Develop. Syst. 8, 214–231 (2016)
Schmidhuber, J.: Formal theory of creativity, fun, and intrinsic motivation (1990–2010). IEEE Trans. Auton. Mental Develop. 2, 230–247 (2010)
Frank, M., Leitner, J., Stollenga, M., Förster, A., Schmidhuber, J.: Curiosity driven reinforcement learning for motion planning on humanoids. Frontiers in Neurorobotics 7, 25 (2013)
Ngo, H., Luciw, M., Forster, A., Schmidhuber, J.: Learning skills from play: artificial curiosity on a katana robot arm. In: The 2012 International Joint Conference on Neural Networks (IJCNN) pp. 1–8 (2012)
Gordon, G., Ahissar, E.: A curious emergence of reaching. Advances in Autonomous Robotics. In: TAROS 2012 Lecture Notes in Computer Science, Berlin, Heidelberg, 7429: 1–12 (2012)
Georgeon, O.L., Marshall, J.B., Gay, S.: Interactional motivation in artificial systems: between extrinsic and intrinsic motivation. In: 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) pp. 1–2 (2012)
Cangelosi, A., Schlesinger, M.: Developmental Robotics: From Babies to Robots. The MIT Press, Cambridge, p. 408 (2014)
Froese, T., Ziemke, T.: Enactive artificial intelligence: investigating the systemic organization of life and mind. Artificial Intelligence 173, 466–500 (2009)
Thórisson, K.R., Eric, N., Ricardo, S., Pei, W.: Editorial: approaches and assumptions of self-programming in achieving artificial general intelligence. J. Artif. Gen. Intell. 3, 1 (2013)
Georgeon, O.L., Marshall, J.B.: Demonstrating sensemaking emergence in artificial agents: a method and an example. Int. J. Mach. Conscious. 05, 131–144 (2013)
Stramandinoli, F., Tikhanoff, V., Pattacini, U., Nori, F.: A bayesian approach towards affordance learning in artificial agents. In: 2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) pp. 298–299 (2015)
Wang, J.G., Mahendran, P.S., Teoh, E.K.: Deep affordance learning for single- and multiple-instance object detection. In: TENCON 2017 - 2017 IEEE Region 10 Conference pp. 321–326 (2017)
Glover, A.J., Wyeth, G.F.: Toward lifelong affordance learning using a distributed markov model. IEEE Trans. Cognit. Develop. Syst. 10, 44–55 (2018)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Pearson Education, London, p. 1132 (2003)
Mohri, M., Rostamizadeh, A., Talwalkar, A.: Foundations of Machine Learning. The MIT Press, Cambridge, p. 480 (2012)
Ng, A.Y.: Preventing “overfitting” of cross-validation data. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 245–253 (1997)
Bhlmann, P., Van De Geer, S.: Statistics for High-Dimensional Data: Methods, Theory and Applications. Springer, Berlin, Incorporated, p. 573 (2011)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Improving neural networks by preventing co-adaptation of feature detectors. CoRR 2012; abs/1207.0580 (2012)
Hussein, A., Gaber, M.M., Elyan, E., Jayne, C.: Imitation learning: a survey of learning methods. ACM Comput. Surv. 50, 21:1–21:35 (2017)
Tscherepanow, M., Hillebrand, M., Hegel, F., Wrede, B., Kummert, F.: Direct imitation of human facial expressions by a user-interface robot. In 2009 9th IEEE-RAS International Conference on Humanoid Robots, pp. 154–160. IEEE (2009)
Billard, A., Grollman, D.: Imitation Learning in Robots. In: Seel, N.M. (ed.) Encyclopedia of the Sciences of Learning, pp. 1494–1496. Springer US, Boston (2012)
Nehaniv, C.L., Dautenhahn, K.: Imitation in animals and artifacts. In: Dautenhahn, K., Nehaniv, C.L. (eds.) pp. 41–61. MIT Press, Cambridge (2002)
Szepesvari, C.: Algorithms for Reinforcement Learning. Morgan and Claypool Publishers, San Rafael (2010)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436 (2015)
Marcus, G.: Deep Learning: A Critical Appraisal, CoRR, abs/1801.00631 (2018)
Lanchantin, P., Pieczynski, W.: Unsupervised restoration of hidden nonstationary markov chains using evidential priors. IEEE Trans. Signal Process. 53, 3091–3098 (2005)
Acharyya, R., Ham, F.M.: A new approach for blind separation of convolutive mixtures. In: 2007 International Joint Conference on Neural Networks pp. 2075–2080 (2007)
Ramezani, R., Saraee, M., Nematbakhsh, M.A.: MRAR: mining multi-relation association rules. J. Comp. Security 1(2), 133–158 (2014)
Heaton, J.: Comparing dataset characteristics that favor the apriori, eclat or FP-growth frequent itemset mining algorithms. SoutheastCon 2016, 1–7 (2016)
Fodor, I.K.: A survey of dimension reduction techniques. Center for Applied Scientific Computing, Lawrence Livermore National Laboratory 9, 1–18 (2002)
Chaput, H.H.: The constructivist learning architecture: a model of cognitive development for robust autonomous robots (2004)
Zhong, C., Liu, S., Lu, Q., Zhang, B.: Continuous learning route map for robot navigation using a growing-on-demand self-organizing neural network. Int. J. Adv. Robot. Syst. 14, 1729881417743612 (2017)
Karhunen, J., Raiko, T., Cho, K.: Unsupervised deep learning: a short review (2014). https://pdfs.semanticscholar.org/9a9a/9e32ca5cb15e00d0b5a1f2a2656905ba79df.pdf. Accessed 24 Nov 2018
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philosophical Transactions, Series A, Mathematical, Physical, and Engineering Sciences 374(2065), 20150202 (2016)
Pindah, W., Nordin, S., Seman, A., Mohamed Said, M.S.: Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks, pp. 172–176 (2015)
Xie, H., Li, J., Xue, H.: A survey of dimensionality reduction techniques based on random projection, CoRR, abs/1706.04371 (2017)
Ishii, K., Nishida, S., Ura, T.: A self-organizing map based navigation system for an underwater robot, robotics and automation. In: Proceedings of 2004 IEEE International Conference on ICRA 2004, vol. 5, pp. 4466–4471, April 2004
Kim, S., Park, F.C.: Fast robot motion generation using principal components: framework and algorithms. IEEE Trans. Ind. Elec. 55(6), 2506–2516 (2008)
Finn, C., Tan, X.Y., Duan, Y., Darrell, T., Levine, S., Abbeel, P.: Learning visual feature spaces for robotic manipulation with deep spatial autoencoders, CoRR, abs/1509.06113 (2015)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning. MIT Press, Cambridge, MA, USA (1998)
Sutton, R.S., Barto, A.G.: Introduction to Reinforcement Learning (2nd edition, in preparation). MIT Press, Cambridge (2017)
Watkins, C.J.C.H., Dayan, P.: Q-learning. Mach. Learn. 8, 279–292 (1992)
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., Hassabis, D.: Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016)
Kormushev, P., Calinon, S., Caldwell, G.D.: Reinforcement learning in robotics: applications and real-world challenges. Robotics 2(3), 122–148 (2013)
Abbeel, P., Ng, A.Y.: Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the Twenty-First International Conference on Machine Learning, Banff, Alberta, Canada, p. 1 (2004)
Settles, B.: Active Learning Literature Survey. University of Wisconsin, Madison. (2009)
Olsson, F.: A literature survey of active machine learning in the context of natural language processing (2009)
Hussein, A., Elyan, E., Gaber, M.M., Jayne, C.: Deep imitation learning for 3D navigation tasks. Neural Comput. Appl. 29, 389–404 (2018)
Mash-Simulator. https://github.com/idiap/mash-simulator. Accessed 10 July 2018
Dima, C., Hebert, M.: Active learning for outdoor obstacle detection (2005)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowled. Data Eng. 22, 1345–1359 (2010)
Brys, T., Harutyunyan, A., Taylor, M.E., Nowé, A.: Ann.: Policy Transfer Using Reward Shaping, pp. 181–188 (2015)
Weiss, K., Khoshgoftaar, T.M, Wang, D.: A survey of transfer learning. J. Big Data 3, 9 (2016)
Tsung, F., Zhang, K., Cheng, L., Song, Z.: Statistical transfer learning: a review and some extensions to statistical process control. Quality Engineering 30, 115–128 (2018)
Barrett, S., Taylor, M.E., Stone, P.: Transfer learning for reinforcement learning on a physical robot, p. 1 (2010)
Kira, Z.: Inter-robot transfer learning for perceptual classification, pp. 13–20 (2010)
Huang, Z., Pan, Z., Lei, B.: Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sensing 9, 907 (2017)
Grefenstette, J.J.: Evolutionary algorithms in robotics†. In: Fifth International Symposium on Robotics and Manufacturing, ISRAM 94 (1994)
Holland, J.H.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA (1992)
Vent, W.: Rechenberg, ingo, evolutionsstrategie—optimierung technischer systeme nach prinzipien der biologischen evolution. 170 S. mit 36 abb. Frommann‐Holzboog‐Verlag. stuttgart 1973. broschiert. Feddes Repert 86, p. 337 (2008)
Luke, S., Hamahashi, S., Kitano, H.: Genetic programming, pp. 1098–1105 (1999)
Nelson, A.L., Barlow, G.J., Doitsidis, L.: Fitness functions in evolutionary robotics: a survey and analysis. Robot. Auton. Syst. 57, 345–370 (2009)
Doncieux, S., Bredeche, N., Mouret, J., Eiben, A.E.G.: Evolutionary robotics: what, why, and where to. Frontiers in Robotics and AI 2, 4 (2015)
Such, F.P., Madhavan, V., Conti, E., Lehman, J., Stanley, K.O., Clune, J.: Deep neuroevolution: genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning. CoRR, abs/1712.06567 (2017)
Duarte, M., Costa, V., Gomes, J.C., Rodrigues, T., Silva, F., Oliveira, S.M., Christensen, A.L.: Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots, CoRR, abs/1511.03154 (2015)
Schrum, J., Lehman, J., Risi, S.: Automatic evolution of multimodal behavior with multi-brain HyperNEAT, pp. 21–22. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, Denver, Colorado, USA, (2016)
Turing, A.M.: Computers & Thought. In: Feigenbaum, E.A., Feldman, J. (eds.) MIT Press, Cambridge, pp. 11–35 (1995).
Piaget, J.: The origins of intelligence in children. International Universities Press, New York (1952)
Tsou, J.Y.: Genetic epistemology and piaget’s philosophy of science: Piaget vs. kuhn on scientific progress. Theory & Psychology 16, 203–224 (2006)
Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y., Ogino, M., Yoshida, C.: Cognitive developmental robotics: a survey. IEEE Trans. Auton. Mental Develop. 1, 12–34 (2009)
Guerin, F.: Learning like a baby: A survey of artificial intelligence approaches. Knowl. Eng. Rev. 26, 209–236 (2011)
Oudeyer, P.: Autonomous development and learning in artificial intelligence and robotics: scaling up deep learning to human-like learning. CoRR, abs/1712.01626 (2017)
Oudeyer, P., Kaplan, F., Hafner, V.V., Whyte, A.: The playground experiment: task-independent development of a curious robot. In: Proceedings of AAAI Spring Symposium on Developmental Robotics, pp. 42–47 (2005)
Zuo, B., Chen, J., Wang, L., Wang, Y.: A reinforcement learning based robotic navigation system. IEEE Int. Conf. Sys. Man Cyber. (SMC), pp. 3452–3457 (2014)
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.A.: Playing atari with deep reinforcement learning. CoRR, abs/1312.5602 (2013)
Singh, S.P., Barto, A.G., Chentanez, N.: Intrinsically motivated reinforcement learning. In: Advances in Neural Information Processing Systems 17 (NIPS). MIT Press (2004)
Assis, LdS., Soares, AdS., Coelho, C.J., Van Baalen, J.: An evolutionary algorithm for autonomous robot navigation. Proc. Comp. Sci. 80, 2261–2265 (2016)
Pathak, D., Agrawal, P., Efros, A.A., Darrell, T.: Curiosity-driven exploration by self-supervised prediction. CoRR, abs/1705.05363 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Ezenkwu, C.P., Starkey, A. (2019). Machine Autonomy: Definition, Approaches, Challenges and Research Gaps. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_24
Download citation
DOI: https://doi.org/10.1007/978-3-030-22871-2_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-22870-5
Online ISBN: 978-3-030-22871-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)