Abstract
Development of methods for Wisdom Web of Things (W2T) should be based on foundations of computations performed in the complex environments of W2T. We discuss some characteristic features of decision making processes over W2T. First of all the decisions in W2T are very often made by different agents on the basis of complex vague concepts and relations among them (creating domain ontology) which are semantically far away from dynamically changing and huge raw data. Methods for approximation of such vague concepts based on information granulation are needed. It is also important to note that the abstract objects represented by different agents are dynamically linked by them with some physical objects and the aim is very often to control performance of computations in the physical world for achieving the target goals. Moreover, the decision making by different agents working in the W2T environment requires mechanisms for understanding (to a satisfactory degree) reasoning performed in natural language on concepts and relations from the domain ontology. We discuss a new computation model, where computations are progressing due to interactions of complex granules (c-granules) linked with the physical objects. C-granules are defined relative to a given agent. We extend the existing Granular Computing (GrC) approach by introducing complex granules (c-granules, for short) making it possible to model interactive computations of agents in complex systems over W2T. One of the challenges in the approach is to develop methods and strategies for adaptive reasoning, called adaptive judgement, e.g., for adaptive control of computations. In particular, adaptive judgement is required in the risk/efficiency management by agents supported by W2T. The discussed approach is a step toward realization of the Wisdom Technology (WisTech) program. The approach was developed over years of work on different real-life projects.
Keywords
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[Hyper world] consists of the cyber, social, and physical worlds, [...] [Wisdom Web of Things] focuses on the data cycle, namely “from things to data, information, knowledge, wisdom, services, humans, and then back to things."A W2T data cycle system is designed to implement such a cycle, which is, technologically speaking, a practical way to realize the harmonious symbiosis of humans, computers, and things in the emerging hyper world.
– Ning Zhong et al. [53]
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Unlike most algorithms, they can run in environments unknown to the designer, and they learn by interacting with the environment how to act effectively in it. After sufficient interaction they will have expertise not provided by the designer, but extracted from the environment [46].
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References
ISO 31000 standard, http://webstore.ansi.org/
J. Barwise, J. Seligman, Information Flow: The Logic of Distributed Systems (Cambridge University Press, 1997)
J. Bazan, Hierarchical classifiers for complex spatio-temporal concepts. Trans. Rough Sets IX: J. Subline LNCS 5390, 474–750 (2008)
D. Deutsch, A. Ekert, R. Lupacchini, Machines, logic and quantum physics. Bull. Symbol. Logic 6, 265–283 (2000)
A. Ehrenfeucht, J. Kleijn, M. Koutny, G. Rozenberg, Reaction systems: a natural computing approach to the functioning of living cells, in A Computable Universe, Understanding and Exploring Nature as Computation, ed. by H. Zenil (World Scientific, Singapore, 2012), pp. 189–208
A. Einstein, Geometrie und Erfahrung (Geometry and Experience) (Julius Springer, Berlin, 1921)
D. Goldin, S. Smolka, P. Wegner (eds.), Interactive Computation: The New Paradigm (Springer, 2006)
M. Heller, The Ontology of Physical Objects. Four Dimensional Hunks of Matter (Cambridge Studies in Philosophy, Cambridge University Press, 1990)
A. Jankowski, Complex Systems Engineering: Wisdom for Saving Billions Based on Interactive Granular Computing (Springer, Heidelberg, 2016). (in preparation)
A. Jankowski, A. Skowron. A WisTech paradigm for intelligent systems. Trans. Rough Sets VI: J. Subline, 94–132
A. Jankowski, A. Skowron. Wisdom technology: a rough-granular approach, in M. Marciniak, A. Mykowiecka (eds.), Bolc Festschrift, Lectures Notes in Computer Science, vol. 5070, pp. 3–41 (Springer, Heidelberg, 2009)
A. Jankowski, A. Skowron, R.W. Swiniarski. Interactive computations: toward risk management in interactive intelligent systems, in P. Maji, A. Ghosh, M.N. Murty, K. Ghosh, S.K. Pal (eds.), Pattern Recognition and Machine Intelligence—5th International Conference, PReMI 2013, Kolkata, India, December 10–14, 2013. Proceedings. Lecture Notes in Computer Science, vol. 8251, pp. 1–12 (Springer, 2013)
A. Jankowski, A. Skowron, R.W. Swiniarski, Interactive complex granules. Fundamenta Informaticae 133, 181–196 (2014)
A. Jankowski, A. Skowron, R.W. Swiniarski, Perspectives on uncertainty and risk in rough sets and interactive rough-granular computing. Fundamenta Informaticae 129, 69–84 (2014)
D. Kahneman, Maps of bounded rationality: psychology for behavioral economics. Am. Econ. Rev. 93, 1449–1475 (2002)
L. Kari, G. Rozenberg, The many facets of natural computing. Commun. ACM 51, 72–83 (2008)
F. Lamnabhi-Lagarrigue, M.D. Di Benedetto, E. Schoitsch, Introduction to the special theme cyber-physical systems. Ercim News 94, 6–7 (2014)
P. Martin-Löf, Intuitionistic Type Theory (Notes by Giovanni Sambin of a series of lectures given in Padua, June 1980) (Bibliopolis, Napoli, Italy, 1984)
J.M. Mendel, L.A. Zadeh, E. Trillas, R.Yager, J. Lawry, H. Hagras, S. Guadarrama, What computing with words means to me. IEEE Comput. Intell. Mag. pp. 20–26 (February 2010)
S.H. Nguyen, J. Bazan, A. Skowron, H.S. Nguyen, Layered learning for concept synthesis. Trans. Rough Sets I: J. Subline LNCS 3100, 187–208 (2004)
A. Omicini, A. Ricci, M. Viroli. The multidisciplinary patterns of interaction from sciences to computer science, in Goldin et al. [7], pp. 395–414
S.K. Pal, L. Polkowski, A. Skowron (eds.), Rough-Neural Computing: Techniques for Computing with Words (Cognitive Technologies, Springer, Heidelberg, 2004)
Z. Pawlak, A. Skowron, Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)
Z. Pawlak, Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)
Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, System Theory, Knowledge Engineering and Problem Solving, vol. 9 (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1991)
J. Pearl, Heuristics: Intelligent Search Strategies for Computer Problem Solving (The Addison Wesley, Moston, MA, 1984)
J. Pearl, Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009)
W. Pedrycz, S. Skowron, V. Kreinovich (eds.), Handbook of Granular Computing (Wiley, Hoboken, NJ, 2008)
L. Polkowski, A. Skowron, Rough mereological approach to knowledge-based distributed ai, in J.K. Lee, J. Liebowitz, J.M. Chae (eds.), Critical Technology, Proc, Third World Congress on Expert Systems, February 5–9, Soeul, Korea (Cognizant Communication Corporation, New York, 1996), pp. 774–781
L. Polkowski, A. Skowron, Rough mereology: a new paradigm for approximate reasoning. Int. J. Approximate Reasoning 15(4), 333–365 (1996)
L. Polkowski, A. Skowron, Rough mereological calculi of granules: a rough set approach to computation. Comput. Intell. Int. J. 17(3), 472–492 (2001)
I. Rahwan, G.R. Simari, Argumentation in Artificial Intelligence (Springer, Berlin, 2009)
G. Rozenberg, T. Bäck, J. Kok (eds.), Handbook of Natural Computing (Springer, 2012)
L. Schäfers, Parallel Monte-Carlo Tree Search for HPC Systems and its Application to Computer Go (Logos Verlag, Berlin, 2014)
P. Shevchenko (ed.), Modelling Operational Risk Using Bayesian Inference (Springer, 2011)
A. Skowron, A. Jankowski, P. Wasilewski, Risk management and interactive computational systems. J. Adv. Math. Appl. 1, 61–73 (2012)
A. Skowron, J. Stepaniuk. Information granules and rough-neural computing, in Pal et al. [22], pp. 43–84
A. Skowron, J. Stepaniuk, R. Swiniarski, Modeling rough granular computing based on approximation spaces. Inf. Sci. 184, 20–43 (2012)
A. Skowron, P. Wasilewski, Information systems in modeling interactive computations on granules. Theor. Comput. Sci. 412(42), 5939–5959 (2011)
A. Skowron, P. Wasilewski, Interactive information systems: toward perception based computing. Theor. Comput. Sci. 454, 240–260 (2012)
P. Slovik, Cournède: Macroeconomic Impact of Basel III, Working Papers, vol. 844 (OECD Economics Publishing, OECD Economics Department, 2011) http://www.oecd.org/eco/Workingpapers
J. Stepaniuk, Rough-Granular Computing in Knowledge Discovery and Data Mining (Springer, Heidelberg, 2008)
R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction (The MIT Press, 1998)
L.P. Thiele, The Heart of Judgment: Practical Wisdom, Neuroscience, and Narrative (Cambridge University Press, Cambridge, UK, 2010)
V. Vapnik, Statistical Learning Theory (Wiley, New York, NY, 1998)
L. Valiant, Probably Approximately Correct. Nature’s Algorithms for Learning and Prospering in a Complex World (Basic Books, A Member of the Perseus Books Group, New York, 2013)
A. Zadeh, Computing with Words: Principal Concepts and Ideas, Studies in Fuzziness and Soft Computing, vol. 277 (Springer, Heidelberg, 2012)
L.A. Zadeh, Fuzzy sets and information granularity, in Advances in Fuzzy Set Theory and Applications (North-Holland, Amsterdam, 1979), pp. 3–18
L.A. Zadeh, Fuzzy Logic = Computing With Words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)
L.A. Zadeh, From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. IEEE Trans. Circuits Syst. 45, 105–119 (1999)
L.A. Zadeh, Foreword, in Pal et al. [22], pp. IX–XI
L.A. Zadeh, A new direction in AI: toward a computational theory of perceptions. AI Mag. 22(1), 73–84 (2001)
N. Zhong, J.H. Ma, R.H. Huang, J.M. Liu, Y.Y. Yao, Y.X. Zhang, J. Chen, Research challenges and perspectives on Wisdom Web of Things (W2T). J. Supercomput. 64, 862–882 (2013)
Acknowledgments
This work was partially supported by the Polish National Science Centre (NCN) grants DEC-2011/01/D/ST6/06981, DEC-2013/09/B/ST6/01568 as well as by the Polish National Centre for Research and Development (NCBiR) under the grant DZP/RID-I-44/8/NCBR/2016.
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Skowron, A., Jankowski, A. (2016). Towards W2T Foundations: Interactive Granular Computing and Adaptive Judgement. In: Zhong, N., Ma, J., Liu, J., Huang, R., Tao, X. (eds) Wisdom Web of Things. Web Information Systems Engineering and Internet Technologies Book Series. Springer, Cham. https://doi.org/10.1007/978-3-319-44198-6_3
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