By Heinrich H. Bülthoff, Lewis L. Chuang (auth.), Byoung-Tak Zhang, Mehmet A. Orgun (eds.)

This quantity constitutes the refereed court cases of the eleventh Pacific Rim convention on synthetic Intelligence, PRICAI 2010, held in Daegu, Korea, in August/September 2010. The forty eight revised complete papers offered including 21 brief papers during this quantity have been conscientiously reviewed and chosen from 191 submissions. the amount concentrates on AI theories, applied sciences and their purposes within the parts of social and monetary significance for nations within the Pacific Rim.

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Wespi, A. ) RAID 2001. LNCS, vol. 2212, pp. 85–103. Springer, Heidelberg (2001) 6. : Rank-based systems: A simple approach to belief revision, belief update, and reasoning about evidence and actions. In: 3rd Intern. Conf. on Principles of Knowledge Representation and Reasoning, Cambridge, MA, pp. 661–672 (1992) 7. : Conditional plausibility measures and bayesian networks. J. Artif. Int. Res. 14(1), 359–389 (2001) 8. : Bayesian Networks and Decision Graphs. Information Science and Statistics. Springer, Heidelberg (June 2007) 9.

In: The 22nd AAAI Conference on Artificial Intelligence, pp. 373–378 (2007) 3. : Min-based causal possibilistic networks: Handling interventions and analyzing the possibilistic counterpart of Jeffrey’s rule of conditioning. In: Proceedings 19th European Conference on Artificial Intelligence - ECAI 2010, Lisbon Portugal, p. 6 (August 2010) 4. : On the logic of iterated belief revision. Artif. Intel. 89, 1–29 (1996) 5. : Aggregation and correlation of intrusion-detection alerts. , Wespi, A. ) RAID 2001.

In the following we propose a graphical counterpart for the conditioning operation for handling observations in causal OCF-based networks. We restrict ourself to OCF-based networks where DAG’s are trees, where a node can have at most one parent. 2 Graphical Counterpart of Conditioning for Handling Observations In order to perform conditioning directly on the graph, conditioning is viewed as a sequence of two operations: i) A combination operation (which combines the original ranking function with the one associated with the observation Ai =ai ), and ii) A normalization operation (for normalizing the ranking function obtained after the combination step in case where this latter becomes sub-normalized).

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