The problem of scene understanding by artificial autonomous agents

Article Sidebar

PDF
Published: 2025-12-31
DOI: https://doi.org/10.59203/zfn.79.732
Keywords:
Array, Array, Array, Array, Array

Main Article Content

Andrzej Bielecki
AGH University of Krakow
Marzena Bielecka
AGH University of Krakow

Abstract

In this paper, the problem of scene understanding in the context of autonomous robotics is considered. The implemented solutions, based on various types of artificial intelligence systems, are described and the perspectives of their development are discussed. In particular, the problem of cognition that reflects the structure and nature of the environment in which the artificial autonomous agent operates, is studied. The specifics of scene models and algorithms for their construction and verification of their adequacy are discussed. The presentation of the existing partial solutions, including the results obtained by the authors, is the starting point of the analysis. Possibilities of new approaches are analysed, especially those that are biologically inspired. The potential fundamental limitations are studied. These problems are discussed in the context of epistemology and in the frame of cybernetics. The analysis of the relationship between philosophy and artificial intelligence in the aspect of cognitive vision systems of autonomous robots is the aim of this paper.

Article Details

Issue

No. 79 (2025)

Section

SPECIAL ISSUE: Philosophy in Technology
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite

Bielecki, A. and Bielecka, M. (2025) “The problem of scene understanding by artificial autonomous agents”, Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce) [Preprint], (79). doi:10.59203/zfn.79.732.

References

Abbate, Francesco. 2023. Natural and Artificial Intelligence: A Comparative Analysis of Cognitive Aspects. Minds and Machines 33 (4): 791–815. https://doi.org/10.1007/s11023-023-09646-w.

Assunção, Gustavo, Bruno Patrão, Miguel Castelo-Branco, and Paulo Menezes. 2022. An Overview of Emotion in Artificial Intelligence. IEEE Transactions on Artificial Intelligence 3 (6): 867–886. https://doi.org/10.1109/TAI.2022.3159614.

Bajcsy, Ruzena, Yiannis Aloimonos, and John K. Tsotsos. 2018. Revisiting active perception. Autonomous Robots 42(2): 177–196. https://doi.org/10.1007/s10514-017-9615-3.

Barreiro, C., Jose M. Barreiro, J. A. Lara, D. Lizcano, M. A. Martínez, and J. Pazos. 2020. The Third Construct of the Universe: Information. Foundations of Science 25 (2): 425–440. https://doi.org/10.1007/s10699-019-09630-7.

Bhushan, Bharat. 2009. Biomimetics: lessons fromnature–an overview. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367 (1893): 1445–1486. https://doi.org/10.1098/rsta.2009.0011.

Bielecka, Marzena. 2018. Syntactic-geometric-fuzzy hierarchical classifier of contours with application to analysis of bone contours in X-ray images. Applied Soft Computing 69:368–380. https://doi.org/10.1016/j.asoc.2018.04.038.

Bielecka, Marzena, Andrzej Bielecki, Aleksander Suchorab, and Igor Wojnicki. 2025. Hierarchical Structural Information – Theory and Applications. In Computational Science – ICCS 2025. 25th international conference: Singapore, July 7–9, 2025: proceedings, Pt. 3, edited by Michael H. Lees, Wentong Cai, Siew Ann Cheong, Yi Su, David Abramson, Jack J. Dongarra, and Peter M. A. Sloot, 15905:48–59. Series Title: Lecture Notes in Computer Science. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-97632-2_4.

Bielecki, Andrzej. 2014. A Model of Human Activity Automatization as a Basis of Artificial Intelligence Systems. IEEE Transactions on Autonomous Mental Development 6 (3): 169–182. https://doi.org/10.1109/TAMD.2014.2319740.

Bielecki, Andrzej. 2021. The Systemic Concept of Contextual Truth. Foundations of Science 26 (4): 807–824. https://doi.org/10.1007/s10699-020-09713-w.

Bielecki, Andrzej. 2024a. Artificial Neural Networks. In Cognitive Science. Vol. 13, edited by Józef Bremer, 299–314. Social Dictionaries. Kraków: Ignatianum University Press.

Bielecki, Andrzej. 2024b. Cybernetics. In Cognitive Science. Vol. 13, edited by Józef Bremer, 281–298. Social Dictionaries. Kraków: Ignatianum University Press.

Bielecki, Andrzej, Marzena Bielecka, and Przemysław Bielecki. 2017. Conditioned Anxiety Mechanism as a Basis for a Procedure of Control Module of an Autonomous Robot. In Artificial Intelligence and Soft Computing. 16th International Conference: ICAISC 2017 Zakopane, Poland, June 11–15, 2017. Proceedings, Pt. 2, edited by Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, and Jacek M. Zurada, 10246:390–398. Series Title: Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-59060-8_35.

Bielecki, Andrzej, Tomasz Buratowski, M. Ciszewski, and Piotr Śmigielski. 2016. Vision based techniques of 3D obstacle reconfiguration for the outdoor drilling mobile robot. In Artificial intelligence and soft computing. lecture notes in computer science, edited by Leszek Rutkowski, Marcin Korytkowski, Rafał Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, and Jacek M. Zurada, 9693:602–612. Series Title: Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-39384-1_53.

Bielecki, Andrzej, Tomasz Buratowski, and Piotr Śmigielski. 2013. Recognition of two-dimensional representation of urban environment for autonomous flying agents. Expert Systems with Applications 40 (9): 3623–3633. https://doi.org/10.1016/j.eswa.2012.12.068.

Bielecki, Andrzej, Tomasz Buratowski, and Piotr Śmigielski. 2014. Three-Dimensional Urban-Type Scene Representation in Vision System of Unmanned Flying Vehicles. In Artificial Intelligence and Soft Computing. 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part I, edited by David Hutchison, Takeo Kanade, Josef Kittler, Jon M. Kleinberg, Alfred Kobsa, Friedemann Mattern, John C. Mitchell, et al., 8467:662–671. Series Title: Lecture Notes in Computer Science. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-07173-2_56.

Bielecki, Andrzej, and Michael Schmittel. 2022. The Information Encoded in Structures: Theory and Application to Molecular Cybernetics. Foundations of Science 27 (4): 1327–1345. https://doi.org/10.1007/s10699-022-09830-8.

Bielecki, Andrzej, and Piotr Śmigielski. 2017. Graph representation for two-dimensional scene understanding by the cognitive vision module. International Journal of Advanced Robotic Systems 14(1): 1729881416682694. https://doi.org/10.1177/1729881416682694.

Bielecki, Andrzej, and Piotr Śmigielski. 2021. Three-Dimensional Outdoor Analysis of Single Synthetic Building Structures by an Unmanned Flying Agent Using Monocular Vision. Sensors 21:s21217270. https://doi.org/10.3390/s21217270.

Bielecki, Andrzej, and Ryszard Stocki. 2023. The concept of structural information and possible applications. Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce), no. 75, 157–183. https://doi.org/10.59203/zfn.75.654.

Bremer, Józef, and Mariusz Flasiński. 2022. The Turing Test, or a Misuse of Language when Ascribing Mental Qualities to Machines. Forum Philosophicum 27 (1): 6–25. https://doi.org/10.35765/f orphil.2022.2701.01.

Chaib-draa, B. 2002. Causal maps: theory, implementation, and practical applications in multiagent environments. IEEE Transactions on Knowledge and Data Engineering 14 (6): 1201–1217. https://doi.org/10.1109/TKDE.2002. 1047761.

Flasiński, Mariusz. 1997. "Every Man in His Notions" or Alchemists’ Discussion on Artificial Intelligence. Foundations of Science 2 (1): 107–121. https://doi.org/10.1023/A:1009687513096.

Flasiński, Mariusz. 2016. Introduction to Artificial Intelligence. Cham: Springer Nature.

Flasiński, Mariusz. 2019. Syntactic Pattern Recognition. New Jersey-London-Singapore:World Scientific.

Flasiński, Mariusz. 2024. Artificial Inteligence. In Cognitive Science. Vol. 13, edited by Józef Bremer, 315–330. Social Dictionaries. Kraków: Ignatianum University Press.

Floreano, Dario, and Robert J.Wood. 2015. Science, technology and the future of small autonomous drones. Nature 521 (7553): 460–466. https://doi.org/10.1038/nature14542.

Frish, Karl. 1954. The Dancing Bees. Berlin-Gotingen-Heidelberg: Springer.

Frish, Karl, and Martin Lindauer. 1956. The "Language" and Orientation of the Honey Bee. Annual Review of Entomology 1 (1): 45–58. https://doi.org/10.1146/annurev.en.01.010156.000401.

Friston, Karl. 2009. The free-energy principle: a rough guide to the brain? Trends in Cognitive Sciences 13 (7): 293–301. https://doi.org/10.1016/j.tics.2009.04.005.

Hu, Cheng, Farshad Arvin, Caihua Xiong, and Shigang Yue. 2017. Bio-inspired embedded vision system for autonomous micro-robots: The LGMD case. IEEE Transactions on Cognitive and Developmental Systems 9: 241–254.

Jakubowski, Ryszard. 1990. Decomposition of complex shapes for their structural recognition. Information Sciences 50 (1): 35–71. https://doi.org/10.1016/0020-0255(90)90004-T.

Jia, Weikuan, Yan Zhang, Jian Lian, Yuanjie Zheng, Dean Zhao, and Chengjiang Li. 2020. Apple harvesting robot under information technology: A review. International Journal of Advanced Robotic Systems 17 (3): 1729881420925310. https://doi.org/10.1177/1729881420925310.

Kang, Soo-Han, and Ji-Hyeong Han. 2023. Video Captioning Based on Both Egocentric and Exocentric Views of Robot Vision for Human-Robot Interaction. International Journal of Social Robotics 15 (4): 631–641. https://doi.org/10.1007/s12369-021-00842-1.

Kant, Immanuel. 1838. Critick of Pure Reason. Translated by Francis Haywood. London: William Pickering.

Katsev, Max, Anna Yershova, Benjamín Tovar, Robert Ghrist, and Steven M LaValle. 2011. Mapping and Pursuit-Evasion Strategies For a SimpleWall-Following Robot. IEEE Transactions on Robotics 27 (1): 113–128. https://doi.org/10.1109/TRO.2010.2095570.

Krzanowski, Roman. 2020. What Is Physical Information? Philosophies 5 (2): 10. https://doi . org/10 . 3390/philosophies5020010.

Krzanowski, Roman. 2022. Ontological information: information in the physical world. World Scientific series in information studies, vol. 13. Singapore:World Scientific.

Krzanowski, Roman, and Pawel Polak. 2022. The meta-ontology of AI systems with human-level intelligence. Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce), no. 73, 197–230. https://doi.org/10.59203/zfn.73.610.

Kurpaska, Sławomir, Andrzej Bielecki, Zygmunt Sobol, Marzena Bielecka, Magdalena Habrat, and Piotr Śmigielski. 2021. The Concept of the Constructional Solution of the Working Section of a Robot for Harvesting Strawberries. Sensors 21 (11): 3933. https://doi.org/10.3390/s21113933.

Li, Jun, Xue Mei, Danil Prokhorov, and Dacheng Tao. 2017. Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene. IEEE Transactions on Neural Networks and Learning Systems 28 (3): 690–703. https://doi.org/10.1109/TNNLS.2016.2522428.

Polak, Paweł. 2023. Philosophy in technology: A research program. Philosophical Problems in Science (Zagadnienia Filozoficzne w Nauce), no. 75, 59–81. https://doi.org/10.59203/zf n.75.682.

Quintas, João. 2023. Improving Visual Perception of Artificial Social Companions Using a Standardized Knowledge Representation in a Human-Machine Interaction Framework. International Journal of Social Robotics 15 (3): 425–444. https://doi.org/10.1007/s12369-021-00859-6.

Rosslenbroich, Bernd. 2014. On the Origin of Autonomy: A New Look at the Major Transitions in Evolution. Cham: Springer International Publishing AG.

Roterman, Irena, and Leszek Konieczny. 2023. Protein Is an Intelligent Micelle. Entropy 25 (6): 850. https://doi.org/10.3390/e25060850.

Rutar, Danaja,WanjaWiese, and Johan Kwisthout. 2022. From representations in predictive processing to degrees of representational features. Minds and Machines 32 (3): 461–484. https://doi.org/10.1007/s11023-022-09599-6.

Ryabko, Boris, and Zhanna Reznikova. 2009. The Use of Ideas of Information Theory for Studying “Language” and Intelligence in Ants. Entropy 11 (4): 836–853. https://doi.org/10.3390/e11040836.

Ryabko, Boris, and Zhanna Reznikova. 2012. Ants and bits. IEEE Information Theory Society Newsletter 62 (5): 17–20.

Scott, Alwyn. 1995. Stairway to the Mind: The Controversial New Science of Consciousness. New York: Springer New York.

Siagian, Christian, and Laurent Itti. 2009. Biologically Inspired Mobile Robot Vision Localization. IEEE Transactions on Robotics 25 (4): 861–873. https://doi.org/10.1109/TRO.2009.2022424.

Tadeusiewicz, Ryszard. 1992. Vision Systems of Industrial Robots.Warszawa: WNT Press.

Tadeusiewicz, Ryszard. 2010. New trends in neurocybernetics. Computer Methods in Materials Science 10 (1): 1–7. https://doi.org/10.7494/cmms.2010.1.0271.

Tejera, Gonzalo, Martin Llofriu, Alejandra Barrera, and AlfredoWeitzenfeld. 2018. Bio-Inspired Robotics: A Spatial Cognition Model integrating Place Cells, Grid Cells and Head Direction Cells. Journal of Intelligent & Robotic Systems 91 (1): 85–99. https://doi.org/10.1007/s10846-018-0852-2.

Umbrico, Alessandro, Riccardo De Benedictis, Francesca Fracasso, Amedeo Cesta, Andrea Orlandini, and Gabriella Cortellessa. 2023. A Mind-inspired Architecture for Adaptive HRI. International Journal of Social Robotics 15(3): 371–391. https://doi.org/10.1007/s12369-022-00897-8.

Wang, Heng, Bin Wang, Bingbing Liu, Xiaoli Meng, and Guanghong Yang. 2017. Pedestrian recognition and tracking using 3D LiDAR for autonomous vehicle. Robotics and Autonomous Systems 88:71–78. https://doi.org/10.1016/j.robot.2016.11.014.