Algorithmic Decision Theory: Second International by Dorothea Baumeister, Gábor Erdélyi, Jörg Rothe (auth.), PDF

By Dorothea Baumeister, Gábor Erdélyi, Jörg Rothe (auth.), Ronen I. Brafman, Fred S. Roberts, Alexis Tsoukià s (eds.)

ISBN-10: 3642248721

ISBN-13: 9783642248726

ISBN-10: 364224873X

ISBN-13: 9783642248733

This e-book constitutes the refereed court cases of the second one overseas convention on Algorithmic choice idea, ADT 2011, held in Piscataway, NJ, united states, in October 2011. The 24 revised complete papers provided have been conscientiously reviewed and chosen from 50 submissions.

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Additional resources for Algorithmic Decision Theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings

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The authors were partly supported by ISF Grant 1101/07, the Paul Ivanier Center for Robotics Research and Production Management, and the Lynn and William Frankel Center for Computer Science. References 1. : A translation-based approach to contingent planning. In: IJCAI, pp. 1623–1628 (2009) 2. : Probabilistic planning via heuristic forward search and weighted model counting. J. Artif. Intell. Res (JAIR) 30, 565–620 (2007) 3. : The metric-ff planning system: Translating “ignoring delete lists” to numeric state variables.

Metric-FF supports only linear numerical expressions. I. Brafman and R. , we must multiply non-constants). Consequently, when there are more than two independent sub-goals, we basically require the achievement of each of them so that the product of their probabilities be sufficient. That is, if G = g1 ∧ ·√· · ∧ gm , and it must be achieved with probability θ, we pose the metric goal: P rg1 > m θ ∧ · · · ∧ P rgm > √ m θ. This is a stronger requirement then P rG > θ. Table 1 below shows the results of our experimental evaluation.

Set N1,c will be denoted by Nc . Our multiobjective branch and bound approach for the committee selection problem relies on the following property: Committee Selection Based on a Pairwise Dominance Relation 35 Proposition 2. For any feasible profit vector p = (p1 , . . , pC ), solution x = (x1 , . . , xn ) defined by – – – – xi xi xi xi = 1, = 0, = 1, = 0, ∀i = 1, . . , p1 ∀i = p1 + 1, . . , |N1 | ∀i = |Nc−1 | + 1, . . , |Nc−1 | + pc − pc−1 ∀c = 2, . . , C ∀i = |Nc−1 | + pc − pc−1 + 1, . .

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Algorithmic Decision Theory: Second International Conference, ADT 2011, Piscataway, NJ, USA, October 26-28, 2011. Proceedings by Dorothea Baumeister, Gábor Erdélyi, Jörg Rothe (auth.), Ronen I. Brafman, Fred S. Roberts, Alexis Tsoukià s (eds.)


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