Abstract
Developing methods for approximation of compound concepts expressing the result of perception belongs to the main challenges of Perception Based Computing (PBC) [70]. The perceived concepts are expressed in natural language. We discuss the rough-granular approach to approximation of such concepts from sensory data and domain knowledge. This additional knowledge, represented by ontology of concepts, is used to make it feasible searching for features (condition attributes) relevant for the approximation of concepts on different levels of the concept hierarchy defined by a given ontology. We report several experiments of the proposed methodology for approximation of compound concepts from sensory data and domain knowledge. The approach is illustrated by examples relative to interactions of agents, ontology approximation, adaptive hierarchical learning of compound concepts and skills, behavioral pattern identification, planning, conflict analysis and negotiations, and perception-based reasoning. The presented results seem to justify the following claim of Lotfi A. Zadeh: “In coming years, granular computing is likely to play an increasingly important role in scientific theories-especially in human-centric theories in which human judgement, perception and emotions are of pivotal importance”. The question of how ontologies of concepts can be discovered from sensory data remains as one of the greatest challenges for many interdisciplinary projects on learning of concepts.
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References
Rough Set Exploration System (RSES). Available at: http://www.logic.mimuw.edu.pl/~rses
Axelrod, R.M.: The Complexity of Cooperation. Princeton University Press, Princeton (1997)
Bazan, J.: The Road simulator. Available at: http://www.logic.mimuw.edu.pl/~bazan/simulator
Bazan, J., et al.: Automatic planning of treatment of infants with respiratory failure through rough set modeling. In: Greco, S., et al. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, Springer, Heidelberg (2006)
Bazan, J., et al.: Risk pattern identification in the treatment of infants with respiratory failure through rough set modeling. In: Proceedings of IPMU’2006, Paris, France, 2006, July 2-7, 2006, pp. 2650–2657. Éditions E.D.K., Paris (2006)
Bazan, J., Skowron, A., Swiniarski, R.: Rough sets and vague concept approximation: From sample approximation to adaptive learning. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 39–62. Springer, Heidelberg (2006)
Bazan, J.G., Peters, J.F., Skowron, A.: Behavioral pattern identification through rough set modelling. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 688–697. Springer, Heidelberg (2005)
Bazan, J.G., Skowron, A.: Classifiers based on approximate reasoning schemes. In: Dunin-Kȩplicz, B., et al. (eds.) Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS’2004). Advances in Soft Computing, pp. 191–202. Springer, Heidelberg (2005)
Behnke, S.: Hierarchical Neural Networks for Image Interpretation. LNCS, vol. 2766. Springer, Heidelberg (2003)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, Oxford (1999)
Breiman, L.: Statistical modeling: The two cultures. Statistical Science 16(3), 199–231 (2001)
Brown, F.: Boolean Reasoning. Kluwer Academic Publishers, Dordrecht (1990)
Cassimatis, N.L.: A cognitive substrate for achievinbg human-level intelligence. AI Magazine 27, 45–56 (2006)
Cassimatis, N.L., Mueller, E.T., Winston, P.H.: Achieving human-level intelligence through integrated systems and research. AI Magazine 27, 12–14 (2006)
Desai, A.: Adaptive complex enterprices. Comm. ACM 48, 32–35 (2005)
Dietterich, T.G.: Hierarchical reinforcement learning with the MAXQ value function decomposition. Artificial Intelligence 13(5), 227–303 (2000)
Doherty, P., et al.: Knowledge Representation Techniques: A Rough Set Approach. Studies in Fuzziness and Soft Computing, vol. 202. Springer, Heidelberg (2006)
Duda, R., Hart, P., Stork, R.: Pattern Classification. John Wiley & Sons, New York (2002)
Dunin-Kȩplicz, B., et al. (eds.): Monitoring, Security, and Rescue Tasks in Multiagent Systems (MSRAS’2004). Advances in Soft Computing. Springer, Heidelberg (2005)
Fahle, M., Poggio, T.: Perceptual Learning. The MIT Press, Cambridge (2002)
Forbus, K.D., Hinrisch, T.R.: Companion congnitive systems: A step toward human-level ai. AI Magazine 27, 83–95 (2006)
Forbus, K.D., Hinrisch, T.R.: Engines of the brain: The computational instruction set of human cognition. AI Magazine 27, 15–31 (2006)
Frege, G.: Grundgesetzen der Arithmetik, 2. Verlag von Hermann Pohle, Jena (1903)
Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, Heidelberg (2001)
Gell-Mann, M.: The Quark and the Jaguar - Adventures in the Simple and the Complex. Brown and Co., London (1994)
Ghallab, M., Nau, D., Traverso, P.: Automated Planning: Theory and Practice. Elsevier, Amsterdam (2004)
Jankowski, A., Skowron, A.: A wistech paradigm for intelligent systems. In: Peters, J.F., et al. (eds.) Transactions on Rough Sets VI. LNCS, vol. 4374, pp. 94–132. Springer, Heidelberg (2007)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement learning: A survey. Journal of Artificial Intelligence Research 4, 227–303 (1996)
Kraus, S.: Strategic Negotiations in Multiagent Environments. MIT Press, Cambridge (2001)
Langley, P.: Cognitive architectures and general intelligent systems. AI Magazine 27, 33–44 (2006)
Leśniewski, S.: Grungzüge eines neuen Systems der Grundlagen der Mathematik. Fundamenta Mathematicae 14, 1–81 (1929)
Liu, J.: Autonomous Agents and Multi-Agent Systems: Explorations in Learning, Self-Organization and Adaptive Computation. World Scientific Publishing, Singapore (2001)
Liu, J., Jin, X., Tsui, K.C.: Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling. Springer, Heidelberg (2005)
Luck, M., McBurney, P., Preist, C.: Agent Technology. Enabling Next Generation Computing: A Roadmap for Agent Based Computing. In: AgentLink (2003)
Łukasiewicz, J.: Die logischen Grundlagen der Wahrscheinlichkeitsrechnung, Kraków 1913. In: Borkowski, L. (ed.) Jan Łukasiewicz - Selected Works, pp. 16–63. North Holland, Amsterdam (1970)
McGovern, A.: Autonomous Discovery of Temporal Abstractions from Interaction with an Environment. PhD thesis, University of Massachusetts, Amherst (2002)
Miikkulainen, R., et al.: Computational Maps in the Visual Cortex. Springer, Heidelberg (2005)
Nguyen, H.S.: Approximate boolean reasoning: Foundations and applications in data mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets V. LNCS, vol. 4100, pp. 344–523. Springer, Heidelberg (2006)
Nguyen, H.S., et al.: Layered learning for concept synthesis. In: Peters, J.F., et al. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 187–208. Springer, Heidelberg (2004)
Nguyen, S.H., Nguyen, T.T., Nguyen, H.S.: Rough set approach to sunspot classification. In: Ślęzak, D., et al. (eds.) RSFDGrC 2005. LNCS (LNAI), vol. 3642, pp. 263–272. Springer, Heidelberg (2005)
Nguyen, T.T., Skowron, A.: Rough set approach to domain knowledge approximation. In: Wang, G., et al. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 221–228. Springer, Heidelberg (2003)
Pal, S.K., Polkowski, L., Skowron, A. (eds.): Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies. Springer, Heidelberg (2004)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. System Theory, Knowledge Engineering and Problem Solving, vol. 9. Kluwer Academic Publishers, Dordrecht (1991)
Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177(1), 3–27 (2007)
Pawlak, Z., Skowron, A.: Rough sets: Some extensions. Information Sciences 177(1), 28–40 (2007)
Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Information Sciences 177(1), 41–73 (2007)
Peters, J.F.: Approximation spaces for hierarchical intelligent behavioural system models. In: D.-Kepliçz, B., et al. (eds.) Monitoring, Security and Rescue Techniques in Multiagent Systems. Advances in Soft Computing, pp. 13–30. Physica-Verlag, Heidelberg (2004)
Peters, J.F.: Rough ethology: Towards a biologically-inspired study of collective behaviour in intelligent systems with approximation spaces. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 153–174. Springer, Heidelberg (2005)
Peters, J.F., Henry, C., Ramanna, S.: Rough ethograms: A study of intelligent system behavior. In: Kłopotek, M.A., Wierzchoń, S., Trojanowski, K. (eds.) Intelligent Information Systems. Advances in Soft Computing, pp. 117–126. Springer, Heidelberg (2005)
Peters, J.F., Henry, C.: Reinforcement learning with approximation spaces. Fundamenta Informaticae 71(2-3), 323–349 (2006)
Poggio, T., Smale, S.: The mathematics of learning: Dealing with data. Notices of the AMS 50(5), 537–544 (2003)
Polkowski, L., Skowron, A.: Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning 15(4), 333–365 (1996)
Rasiowa, H.: Algebraic models of logics. Warsaw University, Warsaw (2001)
Schlenoff, C., et al.: Using 4d/rcs to address ai knowledge integration. AI Magazine 27, 71–81 (2006)
Skowron, A.: Rough sets in KDD (plenary talk). In: Shi, Z., Faltings, B., Musen, M. (eds.) 16th World Computer Congress (IFIP’2000): Proceedings of Conference on Intelligent Information Processing (IIP’2000), pp. 1–14. ishing House of Electronic Industry, Beijing (2000)URL, 1
Skowron, A.: Perception logic in intelligent systems. In: Blair, S., et al. (eds.) Proceedings of the 8th Joint Conference on Information Sciences (JCIS 2005), Salt Lake City, Utah, USA, July 21-26, 2005, pp. 1–5. X-CD Technologies: A Conference & Management Company, Toronto (2005)
Skowron, A.: Rough sets and vague concepts. Fundamenta Informaticae 64(1-4), 417–431 (2005)
Skowron, A.: Rough Sets in Perception-Based Computing (keynote talk). In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds.) PReMI 2005. LNCS, vol. 3776, pp. 21–29. Springer, Heidelberg (2005)
Skowron, A., Stepaniuk, J.: Information granules and rough-neural computing. In: Pal, S.K., Polkowski, L., Skowron, A. (eds.) Rough-Neural Computing: Techniques for Computing with Words. Cognitive Technologies, pp. 43–84. Springer, Heidelberg (2004)
Skowron, A., et al.: Calculi of approximation spaces. Fundamenta Informaticae 72(1-3), 363–378 (2006)
Ślęzak, D., et al. (eds.): RSFDGrC 2005. LNCS (LNAI), vol. 3642. Springer, Heidelberg (2005)
Stone, P.: Layered Learning in Multi-Agent Systems: A Winning Approach to Robotic Soccer. The MIT Press, Cambridge (2000)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (1998)
Swartout, W., et al.: Towards virtual humans. AI Magazine 27, 96–108 (2006)
Sycara, K.: Multiagent systems. AI Magazine, 79–92 (Summer 1998)
Urmson, C., et al.: High speed navigation of unrehearsed terrain: Red team technology for grand challenge 2004. Technical Report CMU-RI-TR-04-37, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (June 2004)
Van Wezel, W., Jorna, R., Meystel, A.: Planning in Intelligent Systems: Aspects, Motivations, and Methods. John Wiley & Sons, Hoboken (2006)
Vapnik, V.: Statistical Learning Theory. John Wiley & Sons, New York (1998)
Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)
Zadeh, L.A.: A new direction in AI: Toward a computational theory of perceptions. AI Magazine 22(1), 73–84 (2001)
Watkins, C.J.C.H.: Learning from Delayed Rewards. Ph.D. Thesis, supervisor: Richard Young, King’s College, University of Cambridge, UK (May 1989)
Wawrzyński, P.: Intensive Reinforcement Learning. Ph.D. dissertation, supervisor: Andrzej Pacut, Institute of Control and Computational Engineering, Warsaw University of Technology (May 2005)
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Jankowski, A., Skowron, A. (2007). Toward Rough-Granular Computing. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2007. Lecture Notes in Computer Science(), vol 4482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72530-5_1
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