Download Algorithmic Decision Theory: 4th International Conference, by Toby Walsh PDF

By Toby Walsh

This ebook constitutes the completely refereed convention lawsuits of the 4th overseas convention on Algorithmic determination concept , ADT 2015, held in September 2015 in Lexington, united states. The 32 complete papers awarded have been rigorously chosen from seventy six submissions. The papers are geared up in topical sections resembling personal tastes; manipulation, studying and different concerns; application and determination thought; argumentation; bribery and keep watch over; social selection; allocation and different difficulties; doctoral consortium.

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Extra resources for Algorithmic Decision Theory: 4th International Conference, ADT 2015, Lexington, KY, USA, September 27-30, 2015, Proceedings

Example text

Dm be the corresponding strict ordering of clusters of equivalent outcomes, with m ≤ 2n . If m = 1, a single-leaf tree (no decision nodes, just a box node) represents this preorder. This tree has depth 0 and so, the assertion holds. Let us assume then that m > 1, and let us define D = D1 ∪ . . ∪ D m/2 and D = D \ D . Let ϕD be a formula such that models of D are 1 We overload the symbols T and T by using them both for the order on the leaves of T and the corresponding preorder on the outcomes from CD(I).

A compact P-tree over I is a tree such that 1. every node is labeled with a Boolean formula over I, and 2. every non-leaf node t labeled with ϕ has either two outgoing edges, with the left one meant to be taken by outcomes that satisfy ϕ and the right one by those that do not (Fig. 3a), or one outgoing edge pointing – straight-down (Fig. 3b), which indicates that the two subtrees of t are identical and the formulas labeling every pair of corresponding nodes in the two subtrees are the same, – left (Fig.

5,6]), but have not received a substantial attention as a formalism for preference representation in AI. We studied formally the issue of compact representations of P-trees, established its relationship to other preference languages such as lexicographic preference trees, possibilistic logic and answer-set optimization. For several preference reasoning problems on P-trees we derived their computational complexity. P-trees are quite closely related to possibilistic logic theories or preference expressions in answer-set optimization.

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