Context aware life pattern prediction using fuzzy-state Q-learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4541 LNCS, Issue , 2007, Pages 188-195

  Feki, Mohamed Ali ^a   Lee, Sang Wan ^b   Bien, Zeungnam ^b   Mokhtari, Mounir ^a  

^a INSTITUT NATIONAL DES TÉLÉCOMMUNICATIONS   (France)

^b Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

Assistive environment; Context aware; Fuzzy state Q learning; Prediction

Indexed keywords

FUZZY RULES; INFORMATION SYSTEMS; LEARNING SYSTEMS; QUERY PROCESSING; VIRTUAL REALITY;

ASSISTIVE ENVIRONMENTS; AUTONOMOUS CONTEXT RECOGNITION; CONTEXT AWARENESS; FUZZY-STATE Q-LEARNING; REAL-WORLD ARTIFACTS;

PATTERN RECOGNITION;

EID: 37249008757     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73035-4_20     Document Type: Conference Paper

References (21)

1. Orr, R.J., Abowd, G.D.: The Smart Floor: A Mechanism for Natural User Identification and Tracking. In: Proceedings of 2000 Conference on Human Factors in Computing Systems (CHI 2000), ACM Press, NY (2000)
2. Mozer, M.C.: The Neural Network House: An Environment that Adapts to its Inhavitants. In: Proc. of the American Association for Artificial Intelligence Spring Symposium on Intelligent Environments, pp. 110-114 (1998)
3. Lesser, et al.: The Intelligent Home Testbed. In: Proc. of Autonomy Control Software Workshop (January 1999)
4. House_n Living Laboratory Introduction http://architecture.mit.edu/ house_n/web/publications
5. Das, S.K., Cook, D.J., Bhattacharya, A., Hierman, E., Lin, T.Y.: The Role of Prediction Algorithms in the MAVHome Smart Home Architecture. IEEE Wireless Communications, Special Issue Smart Homes 9(6), 77-84 (2002)
6. Nurmi, P., Martin, M., Flanagan, J.A.: Enabling Proactiveness through Context Prediction. In: Proceedings of the Workshop on Context Awareness for Proactive Systems, Helsinki (2005)
7. Petzold, J., Bagci, F., et al.: Global and Local State Context Prediction. Artificial Intelligence in Mobile Systems 2003 (AIMS 2003) in Conjunction with the Fifth International Conference on Ubiquitous Computing 2003, Seattle, USA (2003)
8. Adams, L., Hunt, L., Moore, M.: The "Aware- System" - Prototyping an Augmentative Communication Interface, presented at RESNA 2003 (2003)
9. Aim, N., Arnott, J.L., Newell, A.F.: Prediction and Conversational Momentum in an Augmentative Communication System. Communications of the ACM 35, 46-57 (1992)
10. Dey, A.K.: Understanding and Using Context. Personal and Ubiquitous Computing, Special issue on Situated Interaction and Ubiquitous Computing (2001)
11. J. E. Bardram, UbiHealth 2003: The 2nd International Workshop on Ubiquitous Computing for Pervasive Healthcare Applications, Seattle, Washington, October 12, part of the UbiComp 2003 Conference http://www.healthcare. pervasive.dk/ubicomp2003/papers/
12. Schmidt, A.: Ubiquitous Computing - Computing in Context. Ph.D. Dissertation, Department of Computer Science, Lancaster University (November 2002)
13. Gu, T., Wang, X.H., Pung, H.K., Zhang, D.Q.: An ontology-based context model in intelligent environments. In: Proceedings of the Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS'04) (January 2004)
14. Chen,H., et al.: Intelligent Agents Meet the Semantic Web in Smart Spaces. Article, IEEE Internet Computing (November 2004)
15. Mayrhofer, R.: An Architecture for Context Prediction. PhD thesis, Johannes Kepler University of Linz, Austria (October 2004)
16. Yoichiro, M.: Modified Q-Learning Method with Fuzzy State Division and Adaptive Rewards. In: Proc. of IEEE International Conference on Fuzzy Systems, pp. 1556-1561 (2002)
17. Christopher, J.C.H.W., Peter, D.: Q-Learning. Machine Learning 8, 279-292 (1992)
18. Bezdek, J.C.: Fuzziness vs. Probability - Again. IEEE Trans. Fuzzy Systems 2(1), 1-3 (1994)
19. B. Kosko.: The probability Mopnopoly. IEEE Transactions on Fuzzy Systems. vol. 2(1) (1994)
20. Hamid, R.B.: Fuzzy Q-learning: A New Approach for Fuzzy Dynamic Programming. IEEE World Congress on Computational Intelligence, pp. 486-491 (1994)
21. Suh, I.H., Kim, J.-H., Frank Rhee, C.-H.: Fuzzy Q-learning for Autonomous Robot Systems. In: Proc. of IEEE International Conference on Neural Network, pp. 1738-1743 (1997)