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Extra info for Data-driven Generation of Policies
None of the learning algorithm effect estimators are depicted because they do not scale to such a large database. This graph shows TOSCA outperforming DSEE_OSCA. This graph is important because the trie in TOSCA should lose efficiency as the number of attributes increases (the trie’s depth equals the number of attributes). However, this graph shows that decrease in the trie’s efficiency does not affect the ability of the trie to offer TOSCA a speedup over DSEE_OSCA. The effect of varying the size of the domains of the action attributes is shown in Fig.
It is possible to implement " with a fixed number of selection operations on K , and 2. t; G/ D 0 whenever there does not exist any tuple in K whose action attributes match t. 2 Data Selection Effect Estimators 21 A data selection effect estimator differs from a normal effect estimator in that it depends explicitly on selection from event KB K . 5). K / to denote the selection of all the tuples in K that have the values described by t for the corresponding attributes. 4 (Data Ratio Effect Estimator).
In Dan Geiger and Prakash Pundalik Shenoy, editors, Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 124–131, San Francisco, August 1–3 1997. Morgan Kaufmann Publishers. 4. Michael Lederman Littman. Algorithms for Sequential Decision Making. PhD thesis, Department of Computer Science, Brown University, Providence, RI, February 1996. 5. M. L. Puterman. Markov decision processes: Discrete Stochastic Dynamic Programming. , New York, 1994. 6. John Tsitsiklis and Benjamin van Roy.