Abstract
We discuss basic notions of Perception Based Computing (PBC). Perception is characterized by sensory measurements and ability to apply them to reason about satisfiability of complex vague concepts used, e.g., as guards for actions or invariants to be preserved by agents. Such reasoning is often referred as adaptive judgment. Vague concepts can be approximated on the basis of sensory attributes rather than defined exactly. Approximations usually need to be induced by using hierarchical modeling. Computations require interactions between granules of different complexity, such as elementary sensory granules, granules representing components of agent states, or complex granules representing classifiers that approximate concepts. We base our approach to interactive computations on generalized information systems and rough sets. We show that such systems can be used for modeling advanced forms of interactions in hierarchical modeling. Unfortunately, discovery of structures for hierarchical modeling is still a challenge. On the other hand, it is often possible to acquire or approximate them from domain knowledge. Given appropriate hierarchical structures, it becomes feasible to perform adaptive judgment, starting from sensory measurements and ending with conclusions about satisfiability degrees of vague target guards. Thus, our main claim is that PBC should enable users (experts, researchers, students) to submit domain knowledge, by means of a dialog. It should be also possible to submit hypotheses about domain knowledge to be checked semi-automatically. PBC should be designed more like laboratories helping users in their research rather than fully automatic data mining or knowledge discovery toolkit. In particular, further progress in understanding visual perception – as a special area of PBC – will be possible, if it becomes more open for cooperation with experts from neuroscience, psychology or cognitive science. In general, we believe that PBC will soon become necessity in many research areas.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
References
Arbib, M.A.: The Metaphorical Brain 2: Neural Networks and Beyond. Willey & Sons, Chichester (1989)
Bara, B.G.: Cognitive Science. A Developmental Approach to the Simulation of the Mind. Lawrence Erlbaum Associates, Hove (1995)
Barwise, J., Seligman, J.: Information Flow: The Logic of Distributed Systems. Cambridge University Press, Cambridge (1997)
Bazan, J.: Hierarchical classifiers for complex spatio-temporal concepts. In: Peters, J.F., Skowron, A., Rybiński, H. (eds.) Transactions on Rough Sets IX. LNCS, vol. 5390, pp. 474–750. Springer, Heidelberg (2008)
Bower, J.M., Bolouri, H. (eds.): Computational Modeling of Genetic and Biochemical Networks. MIT Press, Cambridge (2001)
Chakraborty, M.K., Pagliani, P.: Geometry Of Approximation: Rough Set Theory: Logic, Algebra and Topology of Conceptual Patterns. Springer, Heidelberg (2008)
Goldin, D., Smolka, S., Wegner, P. (eds.): Interactive Computation: The New Paradigm. Springer, Heidelberg (2006)
Jankowski, J., Skowron, A.: Wisdom technology: A Rough-granular approach. In: Marciniak, M., Mykowiecka, A. (eds.) Bolc Festschrift. LNCS, vol. 5070, pp. 3–41. Springer, Heidelberg (2009)
Khan, M.A., Banerjee, M.: A study of multiple-source approximation systems. In: Peters, J.F., Skowron, A., Słowiński, R., Lingras, P., Miao, D., Tsumoto, S. (eds.) Rough Sets XII. LNCS, vol. 6190, pp. 46–75. Springer, Heidelberg (2010)
Maar, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W.H. Freeman, New York (1982)
Mendel, J.M., Wu, D.: Perceptual Computing: Aiding People in Making Subjective Judgments. John Wiley & IEEE Press (2010)
Newell, A.: Unified Theories of Cognition. Harvard University Press, Cambridge (1990)
Pawlak, Z.: Rough sets. International Journal of Computing and Information Sciences 18, 341–356 (1982)
Pawlak, Z.: Rough sets. In: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Science 177, 3–27 (2007); Rough sets: Some extensions. Information Science 177, 28–40 (2007); Rough sets and boolean reasoning. Information Science 177, 41–73 (2007)
Pedrycz, W., Skowron, A., Kreinovich, V. (eds.): Handbook of Granular Computing. John Wiley & Sons, Chichester (2008)
Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)
Skowron, A., Stepaniuk, J.: Informational granules and rough-neural computing. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words, pp. 43–84. Springer, Heidelberg (2003)
Skowron, A., Stepaniuk, J.: Approximation spaces in rough-granular computing. Fundamenta Informaticae 100, 141–157 (2010)
Skowron, A., Stepaniuk, J.: Rough granular computing based on approximation spaces (Extended version of [19] submitted to the special issue of Theoretical Computer Science on Rough-Fuzzy Computing)
Skowron, A., Suraj, Z.: Discovery of concurrent data models from experimental tables: A rough set approach. In: Proceedings of First International Conference on Knowledge Discovery and Data Mining, pp. 288–293. AAAI Press, Menlo Park (1995)
Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules. In: Szczuka, M. (ed.) RSCTC 2010. LNCS, vol. 6086, pp. 730–739. Springer, Heidelberg (2010)
Skowron, A., Wasilewski, P.: Information systems in modeling interactive computations on granules (Extended version of [22] submitted to the special issue of Theoretical Computer Science on Rough-Fuzzy Computing)
Ślęzak, D., Toppin, G.: Injecting domain knowledge into a granular database engine – A position paper. In: CIKM 2010, Toronto, Ontario, Canada, October 26-30 (2010)
Sun, R.: Prolegomena to Integrating cognitive modeling and social simulation. In: Sun, R. (ed.) From Cognitive Modeling to Social Simulation, pp. 3–26. Cambridge University Press, Cambridge (2006)
Taatgen, N., Lebiere, C., Anderson, J.: Modeling paradigms in ACT-R 29. In: Sun, R. (ed.) Cognition and Multi-Agent Interaction. From Cognitive Modeling to Social Simulation, pp. 29–52. Cambridge University Press, Cambridge (2006)
Thagard, P.: Mind: Introduction to Cognitive Science, 2nd edn. MIT Press, Cambridge (2005)
Zadeh, L.A.: Computing with words and perceptions – A paradigm shift. In: Proceedings of the IEEE International Conference on Information Reuse and Integration (IRI 2009), Las Vegas, Nevada, USA, IEEE Systems, Man, and Cybernetics Society (2009)
Zadeh, L.A.: Generalized theory of uncertainty (GTU) – principal concepts and ideas. Computational Statistics & Data Analysis 51(1), 15–46 (2006)
Zadeh, L.A.: Precisiated natural language (PNL). AI Magazine 25(3), 74–91 (2004)
Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)
Zadeh, L.A.: From computing with numbers to computing with words – From manipulation of measurements to manipulation of perceptions. IEEE Transactions on Circuits and Systems 45(1), 105–119 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Skowron, A., Wasilewski, P. (2010). An Introduction to Perception Based Computing. In: Kim, Th., Lee, Yh., Kang, BH., Ślęzak, D. (eds) Future Generation Information Technology. FGIT 2010. Lecture Notes in Computer Science, vol 6485. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17569-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-17569-5_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17568-8
Online ISBN: 978-3-642-17569-5
eBook Packages: Computer ScienceComputer Science (R0)