Automated Reasoning and Robotics: A Systematic Review
Abstract
Nowadays, humans are more dependent on machines and specifically robots, due to the technological advancements in this area. When it comes to robots, it’s a combination of machine and artificial intelligence programs, which are built on logic and planning. We could define the robotics navigations based on logical representations for the possible allowed movements without any human interactions. Hence, such automated logic\reasoning will help the programs act and produce logical orders to the correspondent part to take any action or answer some questions. However, the task of robots learning involves integrating with many objects such as sensors, cameras for vision, and movements; this process involves understanding how automated reasoning can be applied to robots’ computer programs and how it will enhance the robots’ responses. Besides, it involves understanding what the used techniques are in automated reasoning which is related to Robotics. This paper will review the different types of knowledge representation used in robotics.References
Abou Samra, R., Al Sharari, N., & AlTunaiji, S. (2020). Conceptual Model for Challenges and Succession Opportunities for Virtual Project Teams in the GCC. Future of Information and Communication Conference, 1130 AISC, 328–340. https://doi.org/10.1007/978-3-030-39442-4_25
Abousamra, R., & Al Ali, A. (2017). Qualitative Analysis of the Innovative Knowledge Creation Style of Project Managers and its Relationship with Performance Stability in IT Projects. International Journal of Information Technology and Language Studies, 1(2).
Akbari, A., Lagriffoul, F., & Rosell, J. (2019a). Combined heuristic task and motion planning for bi-manual robots. Autonomous Robots, 43(6), 1575–1590. https://doi.org/10.1007/s10514-018-9817-3
Akbari, A., Lagriffoul, F., & Rosell, J. (2019b). Combined heuristic task and motion planning for bi-manual robots. Autonomous Robots, 43(6), 1575–1590. https://doi.org/10.1007/s10514-018-9817-3
Alajmi, Q. A., Kamaludin, A., Arshah, R. A., & Al-Sharafi, M. A. (2018). The effectiveness of Cloud-Based E-Learning towards quality of academic services: An Omanis’ expert view. International Journal of Advanced Computer Science and Applications, 9(4). https://doi.org/10.14569/IJACSA.2018.090425
Al-Amri, R., Murugesan, R. K., Man, M., Abdulateef, A. F., Al-Sharafi, M. A., & Alkahtani, A. A. (2021). A Review of Machine Learning and Deep Learning Techniques for Anomaly Detection in IoT Data. Applied Sciences, 11(12). https://doi.org/10.3390/APP11125320
Al-Emran, M. (2015a). Hierarchical Reinforcement Learning: A Survey. International Journal of Computing and Digital Systems, 4(2), 137–143.
Al-Emran, M. (2015b). Speeding Up the Learning in A Robot Simulator. International Journal of Computing and Network Technology, 3(3).
Al-Emran, M., Mezhuyev, V., Kamaludin, A., & AlSinani, M. (2018). Development of M-learning Application based on Knowledge Management Processes. 2018 7th International Conference on Software and Computer Applications (ICSCA 2018), 248–253.
Al-Emran, M., & Shaalan, K. (2017). Academics’ Awareness Towards Mobile Learning in Oman. International Journal of Computing and Digital Systems, 6(1), 45–50. https://doi.org/10.12785/IJCDS/060105
Al-Emran, M., Zaza, S., & Shaalan, K. (2015). Parsing modern standard Arabic using Treebank resources. 2015 International Conference on Information and Communication Technology Research, ICTRC 2015. https://doi.org/10.1109/ICTRC.2015.7156426
Al-Maroof, R. A., & Al-Emran, M. (2021). Research Trends in Flipped Classroom: A Systematic Review. In Recent Advances in Intelligent Systems and Smart Applications (pp. 253–275). Springer.
Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 1–42. https://doi.org/10.1007/s10639-020-10197-1
Al-Saedi, K., Al-Emran, M., Abusham, E., & El-Rahman, S. A. (2019). Mobile Payment Adoption: A Systematic Review of the UTAUT Model. 2019 International Conference on Fourth Industrial Revolution, ICFIR 2019. https://doi.org/10.1109/ICFIR.2019.8894794
Alshaafee, A. A., Iahad, N. A., & Al-Sharafi, M. A. (2021). Benefits or risks: What influences novice drivers regarding adopting smart cars? Sustainability (Switzerland), 13(21). https://doi.org/10.3390/su132111916
Bidot, J., Karlsson, L., Lagriffoul, F., & Saffiotti, A. (2017a). Geometric backtracking for combined task and motion planning in robotic systems. Artificial Intelligence, 247, 229–265. https://doi.org/10.1016/j.artint.2015.03.005
Bidot, J., Karlsson, L., Lagriffoul, F., & Saffiotti, A. (2017b). Geometric backtracking for combined task and motion planning in robotic systems. Artificial Intelligence, 247, 229–265. https://doi.org/10.1016/j.artint.2015.03.005
Diab, M., Akbari, A., Ud Din, M., & Rosell, J. (2019a). PMK—A knowledge processing framework for autonomous robotics perception and manipulation. Sensors (Switzerland), 19(5). https://doi.org/10.3390/s19051166
Diab, M., Akbari, A., Ud Din, M., & Rosell, J. (2019b). PMK—A knowledge processing framework for autonomous robotics perception and manipulation. Sensors (Switzerland), 19(5). https://doi.org/10.3390/s19051166
Elsevier. (2021a). Scopus. Elsevier. https://www.scopus.com/search/form.uri?display=basic#basic
Elsevier. (2021b). Scopus. Elsevier.
Erdem, E., Gelfond, M., & Leone, N. (2016a). Applications of answer set programming. AI Magazine, 37(3), 53–68. https://doi.org/10.1609/aimag.v37i3.2678
Erdem, E., Gelfond, M., & Leone, N. (2016b). Applications of answer set programming. AI Magazine, 37(3), 53–68. https://doi.org/10.1609/aimag.v37i3.2678
Feyzabadi, S., & Carpin, S. (2014). Knowledge and data representation for motion planning in dynamic environments. Advances in Intelligent Systems and Computing, 274, 233–240. https://doi.org/10.1007/978-3-319-05582-4_20
Gayathri, R., & Uma, V. (2018). Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey. In ICT Express (Vol. 4, Issue 2, pp. 69–74). Korean Institute of Communications Information Sciences. https://doi.org/10.1016/j.icte.2018.04.008
Gemignani, G., Capobianco, R., Bastianelli, E., Bloisi, D. D., Iocchi, L., & Nardi, D. (2016). Living with robots: Interactive environmental knowledge acquisition. Robotics and Autonomous Systems, 78, 1–16. https://doi.org/10.1016/j.robot.2015.11.001
google. (2021a). Google Scholar. Website. https://scholar.google.com/
google. (2021b). Google Scholar. Website.
Hammood, W. A., Abdullah, R., Hammood, O. A., Asmara, S. M., Al-Sharafi, M. A., & Hasan, A. M. (2020). A Review of User Authentication Model for Online Banking System based on Mobile IMEI Number. IOP Conference Series: Materials Science and Engineering, 769(1). https://doi.org/10.1088/1757-899X/769/1/012061
Jnr, B. A., Nweke, L. O., & Al-Sharafi, M. A. (2021). Applying software-defined networking to support telemedicine health consultation during and post Covid-19 era. Health and Technology, 11(2). https://doi.org/10.1007/s12553-020-00502-w
Lemaignan, S., Warnier, M., Sisbot, E. A., Clodic, A., & Alami, R. (2017). Artificial cognition for social human–robot interaction: An implementation. Artificial Intelligence, 247, 45–69. https://doi.org/10.1016/j.artint.2016.07.002
Miyazawa, A., Ribeiro, P., Li, W., Cavalcanti, A., Timmis, J., & Woodcock, J. (2019). RoboChart: modelling and verification of the functional behaviour of robotic applications. Software and Systems Modeling, 18(5), 3097–3149. https://doi.org/10.1007/s10270-018-00710-z
Muhayyuddin, Akbari, A., & Rosell, J. (2018a). κ-PMP: Enhancing Physics-based Motion Planners with Knowledge-Based Reasoning. Journal of Intelligent and Robotic Systems: Theory and Applications, 91(3–4), 459–477. https://doi.org/10.1007/s10846-017-0698-z
Muhayyuddin, Akbari, A., & Rosell, J. (2018b). κ-PMP: Enhancing Physics-based Motion Planners with Knowledge-Based Reasoning. Journal of Intelligent and Robotic Systems: Theory and Applications, 91(3–4), 459–477. https://doi.org/10.1007/s10846-017-0698-z
Niekum, S., Osentoski, S., Konidaris, G., Chitta, S., Marthi, B., & Barto, A. G. (n.d.). Learning grounded finite-state representations from unstructured demonstrations. The International Journal of Robotics Research, 1–27. https://doi.org/10.1177/0278364914554471
Paulius, D., & Sun, Y. (2019a). A Survey of Knowledge Representation in Service Robotics. Robotics and Autonomous Systems, 118, 13–30. https://doi.org/10.1016/j.robot.2019.03.005
Paulius, D., & Sun, Y. (2019b). A Survey of Knowledge Representation in Service Robotics. Robotics and Autonomous Systems, 118, 13–30. https://doi.org/10.1016/j.robot.2019.03.005
Portoraro, F. (2019). Automated Reasoning (Stanford Encyclopedia of Philosophy/Spring 2019 Edition). Metaphysics Research Lab, Stanford University. https://plato.stanford.edu/archives/spr2019/entries/reasoning-automated/
Rajan, K., & Saffiotti, A. (2017a). Towards a science of integrated AI and Robotics. In Artificial Intelligence (Vol. 247, pp. 1–9). Elsevier B.V. https://doi.org/10.1016/j.artint.2017.03.003
Rajan, K., & Saffiotti, A. (2017b). Towards a science of integrated AI and Robotics. In Artificial Intelligence (Vol. 247, pp. 1–9). Elsevier B.V. https://doi.org/10.1016/j.artint.2017.03.003
Ramirez-Amaro, K., Beetz, M., & Cheng, G. (2017). Transferring skills to humanoid robots by extracting semantic representations from observations of human activities. Artificial Intelligence, 247, 95–118. https://doi.org/10.1016/j.artint.2015.08.009
Schlenoff, C., Prestes, E., Madhavan, R., Goncalves, P., Li, H., Balakirsky, S., Kramer, T., & Miguelanez, E. (2012). An IEEE standard Ontology for Robotics and Automation. IEEE International Conference on Intelligent Robots and Systems, 1337–1342. https://doi.org/10.1109/IROS.2012.6385518
Semantic Scholar. (2021a). Semantic Scholar | AI-Powered Research Tool. Website. https://www.semanticscholar.org/
Semantic Scholar. (2021b). Semantic Scholar | AI-Powered Research Tool. Website.
Sridharan, M., Gelfond, M., Zhang, S., & Wyatt, J. (2019a). ReBA: A refinement-based architecture for knowledge representation and reasoning in robotics. Journal of Artificial Intelligence Research, 65, 87–180. https://doi.org/10.1613/jair.1.11524
Sridharan, M., Gelfond, M., Zhang, S., & Wyatt, J. (2019b). ReBA: A refinement-based architecture for knowledge representation and reasoning in robotics. Journal of Artificial Intelligence Research, 65, 87–180. https://doi.org/10.1613/jair.1.11524
Wikipedia. (2021). Automated reasoning - Wikipedia. Wikipedia. https://en.wikipedia.org/wiki/Automated_reasoning
Zhang, S., Sridharan, M., & Wyatt, J. L. (2015). Mixed Logical Inference and Probabilistic Planning for Robots in Unreliable Worlds. IEEE Transactions on Robotics, 31(3), 699–713. https://doi.org/10.1109/TRO.2015.2422531
Downloads
Published
Issue
Section
License
Authors submitting articles to the IJITLS warrent that the work is original and that she/he is the author of the submission together with the named co-authors; to the extend the submission incorporates text passages, figures, data or other material from the work of others, the submitting author has obtained any necessary permission.
Articles in this journal are published under the Creative Commons Attribution Licence (CC-BY 4.0).
By submitting an article, the author grants to this journal the non-exclusive right to publish it. The author retains the copyright and the publishing rights for his article without any restrictions.
