Specific-to-general learning for temporal events with application to learning event definitions from video

Journal of Artificial Intelligence Research

Volumn 17, Issue , 2002, Pages 379-449

  Fern, Alan ^a   Givan, Robert ^a   Siskind, Jeffrey Mark ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER VISION; MARKOV PROCESSES; MATHEMATICAL MODELS; NEURAL NETWORKS; POLYNOMIAL APPROXIMATION; SECURITY OF DATA;

COMPLEXITY BOUNDS; SPECIFIC-TO-GENERAL LEARNING; TEMPORAL LOGIC; VIDEO SEQUENCES;

LEARNING SYSTEMS;

EID: 10044252615     PISSN: 10769757     EISSN: None     Source Type: Journal    
DOI: 10.1613/jair.1050     Document Type: Article

References (38)

1. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Proceedings of the Eleventh International Conference on Data Engineering, pp. 3-14.
2. Alien, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.
3. Angluin, D. (1987). Learning regular sets from queries and counterexamples. Information and Computation, 75, 87-106.
4. Bacchus, F., & Kabanza, F. (2000). Using temporal logics to express search control knowledge for planning. Artificial Intelligence, 16, 123-191.
5. Bobick, A. F., & Ivanov, Y. A. (1998). Action recognition using probabilistic parsing. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 196-202, Santa Barbara, CA.
6. Borchardt, G. C. (1985). Event calculus. In Proceedings of the Ninth International Joint Conference on Artificial Intelligence, pp. 524-527, Los Angeles, CA.
7. Brand, M. (1997a). The inverse Hollywood problem: From video to scripts and Storyboards via causal analysis. In Proceedings of the Fourteenth National Conference on Artificial Intelligence, pp. 132-137, Providence, RI.
8. Brand, M. (1997b). Physics-based visual understanding. Computer Vision and Image Understanding, 65(2), 192-205.
9. Brand, M., & Essa, I. (1995). Causal analysis for visual gesture understanding. In Proceedings of the AAAI Fall Symposium on Computational Models for Integrating Language and Vision.
10. Brand, M., Oliver, N., & Pentland, A. (1997). Coupled hidden Markov models for complex action recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
11. Cohen, P. (2001). Fluent learning: Elucidating the structure of episodes. In Proceedings of the Fourth Symposium on Intelligent Data Analysis.
12. Cohen, W. (1994). Grammatically biased learning: Learning logic programs using an explicit antecedent description lanugage. Artificial Intelligence, 68, 303-366.
13. Cohen, W., & Hirsh, H. (1994). Learning the CLASSIC description logic: Theoretical and experimental results. In Proceedings of the Fourth International Conference on Principles of Knowledge Representation and Reasoning, pp. 121-133.
14. De Raedt, L., & Dehaspe, L. (1997). Clausal discovery. Machine Learning, 26, 99-146.
15. Dehaspe, L., & De Raedt, L. (1996). DLAB: A declarative language bias formalism. In Proceedings of the Ninth International Syposium on Methodologies for Intelligent Systems, pp. 613-622.
16. Fikes, R., & Nilsson, N. (1971). STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3/4).
17. Hoppner, F. (2001). Discovery of temporal patterns - Learning rules about the qualitative behaviour of time series. In Proceedings of the Fifth European Conference on Principles and Practice of Knowledge Discovery in Databases.
18. Kam, P., & Fu, A. (2000). Discovering temporal patterns for interval-based events. In Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery.
19. Klingspor, V., Morik, K., & Rieger, A. D. (1996). Learning concepts from sensor data of a mobile robot. Artificial Intelligence, 23(2/3), 305-332.
20. Lang, K., Pearlmutter, B., & Price, R. (1998). Results of the Abbadingo one DFA learning competition and a new evidence-driven state merging algorithm. In Proceedings of the Fourth International Colloquium on Grammatical Inference.
21. Lavrac, N., Dzeroski, S., & Grobelnik, M. (1991). Learning nonrecursive definitions of relations with LINUS. In Proceedings of the Fifth European Working Session on Learning, pp. 265-288.
22. Mann, R., & Jepson, A. D. (1998). Toward the computational perception of action. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 794-799, Santa Barbara, CA.
23. Mannila, H., Toivonen, H., & Verkamo, A. I. (1995). Discovery of frequent episodes in sequences. In Proceedings of the First International Conference on Knowledge Discovery and Data Mining.
24. Mitchell, T. (1982). Generalization as search. Artificial Intelligence, 18(2), 517-42.
25. Morales, E. (1997). Pal: A pattern-based first-order inductive system. Machine Learning, 26, 227-252.
26. Muggleton, S. (1995). Inverting entailment and Progol. Machine Intelligence, 14, 133-188.
27. Muggleton, S., & Feng, C. (1992). Efficient induction of logic programs. In Muggleton, S. (Ed.), Inductive Logic Programming, pp. 281-298. Academic Press.
28. Muggleton, S., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19/20, 629-679.
29. Pinhanez, C., & Bobick, A. (1995). Scripts in machine understanding of image sequences. In Proceedings of the AAAI Fall Symposium Series on Computational Models for Integrating Language and Vision.
30. Plotkin, G. D. (1971). Automatic Methods of Inductive Inference. Ph.D. thesis, Edinburgh University.
31. Regier, T. P. ( 1992). The Acquisition of Lexical Semantics for Spatial Terms: A Connectionist Model of Perceptual Categorization. Ph.D. thesis, University of California at Berkeley.
32. Roth, D., & Yih, W. (2001). Relational learning via propositional algorithms: An information extraction case study. In Proeedings of the Seventeenth International Joint Conference on Artificial Intelligence.
33. Shoham, Y. (1987). Temporal logics in AI: Semantical and ontological considerations. Artificial Intelligence, 33(1), 89-104.
34. Siskind, J. M. (2000). Visual event classification via force dynamics. In Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 149-155, Austin, TX.
35. Siskind, J. M. (2001). Grounding the lexical semantics of verbs in visual perception using force dynamics and event logic. Journal of Artificial Intelligence Research, 15, 31-90.
36. Siskind, J. M., & Morris, Q. (1996). A maximum-likelihood approach to visual event classification. In Proceedings of the Fourth European Conference on Computer Vision, pp. 347-360, Cambridge, UK. Springer-Verlag.
37. Talmy, L. (1988). Force dynamics in language and cognition. Cognitive Science, 12, 49-100.
38. Yamoto, J., Ohya, J., & Ishii, K. (1992). Recognizing human action in time-sequential images using hidden Markov model. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 379-385.