Abstract
The problem of understanding intelligence is treated, by some prominent researchers, as the greatest problem of this century. In this article we justify that a decision support systems to be intelligent there is a need for developing new reasoning tools which can take into account the significance of the processes of sensory measurement, experience and perception about the concerned situations; i.e., understanding the process of perceiving a situation is also required for making relevant decisions. We discuss how such reasoning, called adaptive judgment, can be performed over objects interacting in the physical world using Interactive Granular Computing Model (IGrC). The basic objects in IGrC are called the complex granules (c-granules, for short). A c-granule is designed to link the abstract and physical worlds and to realize the paths of judgments starting from sensory measurement, experience to perception. Some c-granules are extended by information layers, called informational c-granules (ic-granules, for short); they can create the basis for modeling a notion of control conducting the whole process of computation over the c-granules.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The case when some ic-granules from configuration have their own control will be considered elsewhere.
- 2.
- 3.
In [6] this example is elaborated using c-granules without informational layers where encoding information from soft_suit is made by an external observer.
References
Brooks, F.P.: The Mythical Man-Month: Essays on Software Engineering. Addison-Wesley, Boston (1975). (extended Anniversary Edition in 1995)
Dutta, S., Jankowski, A., Rozenberg, G., Skowron, A.: Linking reaction systems with rough sets. Fundamenta Informaticae 165, 283–302 (2019). https://doi.org/10.3233/FI-2019-1786
Dutta, S., Skowron, A., Chakraborty, M.K.: Information flow in logic for distributed systems: extending graded consequence. Inf. Sci. 491 232–250 (2019). https://doi.org/10.1016/j.ins.2019.03.057
Gerrish, S.: How Smart Machines Think. MIT Press, Cambridge (2018)
Gershenson, C., Heylighen, F.: How can we think the complex? In: Richardson, K. (ed.) Managing Organizational Complexity: Philosophy, Information Age Publishing, Theory and Application, pp. 47–61 (2005)
Jankowski, A.: Interactive Granular Computations in Networks and Systems Engineering: A Practical Perspective. LNNS, vol. 17. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57627-5
Jankowski, A., Skowron, A.: A wistech paradigm for intelligent systems. In: Peters, J.F., Skowron, A., Düntsch, I., Grzymała-Busse, J., Orłowska, E., Polkowski, L. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 94–132. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-71200-8_7
Martin, W.M. (ed.): Theories of Judgment. Psychology, Logic, Phenomenology. Cambridge University Press, Cambridge (2006)
Miłkowski, M.: Explaining the Computational Mind. The MIT Press, Cambridge (2013)
Nöe, A.: Action in Perception. MIT Press, Cambridge (2004)
Ortiz Jr., C.L.: Why we need a physically embodied Turing test and what it mightlook like. AI Mag. 37, 55–62 (2016). https://doi.org/10.1609/aimag.v37i1.2645
Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)
Pearl, J.: Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009). https://doi.org/10.1214/09-SS057
Pedrycz, W., Skowron, S., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley, Hoboken (2008)
Pedrycz, W.:Granular computing for data analytics: a manifesto of human - centric computing. IEEE/CAA J. Automatica Sinica 5, 1025–1034 (2018). https://doi.org/10.1109/JAS.2018.7511213
Pietarinen, A.V.: Signs of Logic Peircean Themes on The Philosophy of Language, Games, and Communication. Springer, Heidelberg (2006). https://doi.org/10.1007/1-4020-3729-5
Salgues, B.: Society 5.0. Industry of the Future, Technologies, Methods and Tools. ISTE. Wiley, Hoboken (2018)
Skowron, A., Jankowski, A., Dutta, S.: Interactive granular computing. Granular Comput. 1(2), 95–113 (2016). https://doi.org/10.1007/s41066-015-0002-1
Zadeh, L.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997). https://doi.org/10.1016/S0165-0114(97)00077-8
Zadeh, L.: Computing with Words: Principal Concepts and Ideas. Studies Fuzziness Soft Computing, vol. 277. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-27473-2
Zhong, N., et al.: Research Challenges and perspectives on wisdom web of things (W2T). J. Supercomput. 64(3), 862–882 (2013). https://doi.org/10.1007/s11227-010-0518-8
Acknowledgement
Andrzej Skowron was partially supported by the ProME (Prognostic Modeling of the COVID-19 Epidemic) grant.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Dutta, S., Skowron, A. (2021). Interactive Granular Computing Model for Intelligent Systems. In: Shi, Z., Chakraborty, M., Kar, S. (eds) Intelligence Science III. ICIS 2021. IFIP Advances in Information and Communication Technology, vol 623. Springer, Cham. https://doi.org/10.1007/978-3-030-74826-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-74826-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-74825-8
Online ISBN: 978-3-030-74826-5
eBook Packages: Computer ScienceComputer Science (R0)
