Interpolation based fuzzy automaton for human-robot interaction

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 42, Issue 16, 2009, Pages 317-322

  Kovács, Szilveszter ^a,b   Vincze, Dávid ^a   Gácsi, Márta ^c   Miklósi, Ádám ^c   Korondi, Péter ^d  

^a UNIVERSITY OF MISKOLC   (Hungary)

^b TECHNICAL UNIVERSITY OF KO ICE   (Slovakia)

^c EÖTVÖS LORÁND UNIVERSITY   (Hungary)

^d BUDAPEST UNIVERSITY OF TECHNOLOGY AND ECONOMICS   (Hungary)

Author keywords

Behaviour based Control; Fuzzy Automaton; Fuzzy Rule Interpolation; Human Robot Interaction

Indexed keywords

FUZZY INFERENCE; FUZZY RULES; INTERPOLATION; KNOWLEDGE REPRESENTATION; MAN MACHINE SYSTEMS; OPTIMIZATION;

BEHAVIOUR-BASED CONTROLS; COMPUTATIONAL DEMANDS; EMBEDDED IMPLEMENTATION; FUZZY AUTOMATON; FUZZY RULE INTERPOLATION; HUMAN ROBOT INTERACTION (HRI); NUMERICAL OPTIMIZATIONS; SPECIES INTERACTIONS;

HUMAN ROBOT INTERACTION;

EID: 85006744662     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20090909-4-JP-2010.00055     Document Type: Conference Paper

References (22)

1. J.C. Bezdek (1981). Pattern Recognition with Fuzzy Objective Function, Plenum Press, New York.
2. K. Dautenhahn (2007). Methodology and themes of humanrobot interaction: A growing research field. Int. J. of Advanced Robotic Systems, 4, 103-108.
3. Z. C. Johanyák, D. Tikk, S. Kovács and K.K. Wong (2006). Fuzzy Rule Interpolation Matlab Toolbox - FRI Toolbox, Proc. of the IEEE World Congress on Computational Intelligence (WCCI'06), 15th Int. Conf. on Fuzzy Systems (FUZZ-IEEE'06), July 16-21, Vancouver, BC, Canada, pp. 1427-1433.
4. F. Klawonn (1994). Fuzzy Sets and Vague Environments, Fuzzy Sets and Systems, 66, pp.207-221.
5. Sz. Kovács (1996). New Aspects of Interpolative Reasoning, Proceedings of the 6th. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Granada, Spain, pp. 477-482.
6. Sz. Kovács and L.T. Kóczy (1997a). Approximate Fuzzy Reasoning Based on Interpolation in the Vague Environment of the Fuzzy Rule base as a Practical Alternative of the Classical CRI, Proceedings of the 7th International Fuzzy Systems Association World Congress, Prague, Czech Republic, 144-149.
7. Sz. Kovács and L.T. Kóczy (1997b). The use of the concept of vague environment in approximate fuzzy reasoning, Fuzzy Set Theory and Applications, Tatra Mountains Mathematical Publications, Mathematical Institute Slovak Academy of Sciences, Bratislava, Slovak Republic, vol.12, pp. 169-181.
8. Sz. Kovács (2000). "Interpolative Fuzzy Reasoning and Fuzzy Automaton in Adaptive System Applications", Proceedings of the IIZUKA2000, 6th International Conference on Soft Computing, October 1-4, Iizuka, Fukuoka, Japan, pp.777-784.
9. Sz. Kovács (2002). "Fuzzy Reasoning and Fuzzy Automata in User Adaptive Emotional and Information Retrieval Systems", Proceedings of the 2002 IEEE International Conference on Systems, Man and Cybernetics, October 6-9, Hammamet, Tunisia, 02CH37349C, ISBN: 0-7803-7438-X, WP1N5, p.6.
10. Sz. Kovács (2005). Interpolative Fuzzy Reasoning in Behaviour-based Control, Advances in Soft Computing, Vol. 2, Computational Intelligence, Theory and Applications, Bernd Reusch (Ed.), Springer, Germany, ISBN 3-540-22807-1, pp.159-170.
11. P. M. Larsen (1980). Industrial application of fuzzy logic control. Int. J. of Man Machine Studies, (12) 4, 3-10.
12. E. H. Mamdani and S. Assilian (1975). An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. of Man Machine Studies, (7), 1-13.
13. Á. Miklósi (2007). Dog behaviour, evolution and cognition. Oxford University Press.
14. Masahiro Mori (1970). The Uncanny Valley, Energy, 7 (4), pp. 33-35.
15. P. Pirjanian (1999). "Behavior Coordination Mechanisms - State-of-the-art", Tech-report IRIS-99-375, Institute for Robotics and Intelligent Systems, School of Engineering, University of Southern California, http://wwwrobotics.usc.edu/~paolo/publications/ bcm.ps.gz, Oct.
16. M. Potal (2008). Overcoming the uncanny valley. IEEE Compuer Graphics and Applications, 4 11-17.
17. D. Shepard (1968). A two dimensional interpolation function for irregularly spaced data, Proc. 23rd ACM Internat. Conf., pp. 517-524.
18. M. Sugeno (1985). An introductory survey of fuzzy control. Information Science, (36), 59-83.
19. T. Takagi and M. Sugeno (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. on SMC, (15), 116-132.
20. K W Wong, D Tikk, T D Gedeon and L T Kóczy (2006). Fuzzy rule Interpolation for Multidimensional Input Spaces With Applications: A Case Study, IEEE T FUZZY SYST 13: (6) pp.809-819.
21. L. A. Zadeh (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. on SMC, (3), 28-44.
22. The FRI Toolbox is available at: http://fri.gamf.hu