Abstract
This paper argues that chances (risks or opportunities) can be discovered from our daily observations and background knowledge. A person can easily identify chances in a news article. In doing so, the person combines the new information in the article with some background knowledge. Hence, we develop a deductive system to discover relative chances of particular chance seekers. This paper proposes a chance discovery system that uses a general purpose knowledge base and specialised reasoning algorithms.
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References
Abe, A., 2003a. Abduction and analogy in chance discovery. In Ohsawa, Y. and McBurney, P., editors, Chance Discovery, pages 231–247. Springer-Verlag, Berlin Heidelberg.
Abe, A., 2003b. The role of abduction in chance discovery. New Gen. Comput., 21(1):61–71.
Kundu, K., Sessions, C., des Jardins, M. and Rheingans, P. 2002. Three-dimensional visualization of hierarchical task network plans, Proceedings of the Third International NASA Workshop on Planning and Scheduling for Space, Houston, Texas.
McBurney, P. and Parsons, S., 2003. Agent communications for chance discovery. In Ohsawa, Y. and McBurney, P., editors, Chance Discovery, pages 133–146. Springer-Verlag Berlin Heidelberg.
Nau, D. Cao, Y., Lotem, A. and Muñoz-Avila, H. 1999. SHOP: Simple Hierarchical Ordered Planner. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-99), pp. 968-973.
Ohsawa, Y., 2003a. Keygraph: Visualized structure among event clusters. In Ohsawa, Y. and McBurney, P., editors, Chance Discovery, pages 262–275. Springer-Verlag, Berlin Heidelberg.
Ohsawa, Y., 2003b. Modeling the process of chance discovery. In Ohsawa, Y. and McBurney, P., editors, Chance Discovery, pages 2–15. Springer-Verlag Berlin Heidelberg.
Ohsawa, Y., Benson, N. E., and Yachida, M., 1998. Keygraph: Automatic indexing by co-occurrence graph based on building construction metaphor. Proceedings of the Advances in Digital Libraries Conference, pages 12–18. IEEE Computer Society.
Ohsawa, Y. and McBurney, P., 2003. Preface. In Ohsawa, Y. and McBurney, P., editors, Chance Discovery. Springer-Verlag, Berlin Heidelberg.
Ohsawa, Y. and Yachida, M., 1999. Discover risky active faults by indexing an earthquake sequence. Proceedings of the Second International Conference on Discovery Science, pages 208–219. Springer-Verlag.
OpenCyc.org, 2002. OpenCyc documentation. http://www.openCyc.org/doc/.
Parsons, S. and McBurney, P., 2003. Logics of argumentation for chance discovery. In Ohsawa, Y. and McBurney, P., editors, Chance Discovery, pages 150–165. Springer-Verlag, Berlin Heidelberg.
Russell, S. and Norvig, P., 2002. Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence, 2nd edition.
Tawfik, A. Y. 2004. Inductive reasoning and chance discovery. Minds and Machines, Volume 14 (Issue 4):441– 451.
Tawfik, A. Y. and Khan, S., 2005. The Degeneration of Relevance in Dynamic Decision Networks with Sparse Evidence, Applied Intelligence, to appear.
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Wu, Z., Tawfik, A.Y. (2007). TOWARDS A CHANGE-BASED CHANCE DISCOVERY. In: Chen, CS., Filipe, J., Seruca, I., Cordeiro, J. (eds) Enterprise Information Systems VII. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-5347-4_15
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DOI: https://doi.org/10.1007/978-1-4020-5347-4_15
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