# Nonmonotonic reasoning: from complexity to algorithms

Nonmonotonic reasoning: from complexity to algorithms The purpose of this paper is to outline various results regarding the computational complexity and the algorithms of nonmonotonic entailment in different coherence‐based approaches. Starting from a (non necessarily consistent) belief base E and a pre‐order on E, we first present different mechanisms for selecting preferred consistent subsets. Then we present different entailment principles in order to manage these multiple subsets. The crossing point of each generation mechanism m and each entailment principle p defines an entailment relation $$(E, \leqslant )\left| \sim \right.^{p,m} \Phi$$ which we study from the computational complexity point of view. The results are not very encouraging since the complexity of all these nonmonotonic entailment relations is, in most restricted languages, larger than the complexity of monotonic entailment. So, we decided to extend Binary Decision Diagrams technics, which are well suited to the task of solving NP‐hard logic‐based problems. Both theoretical and experimental results are described along this line in the last sections. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

# Nonmonotonic reasoning: from complexity to algorithms

, Volume 22 (4) – Oct 4, 2004
30 pages

/lp/springer-journals/nonmonotonic-reasoning-from-complexity-to-algorithms-WVks9VSYcw
Publisher
Springer Journals
Subject
Computer Science; Computer Science, general; Artificial Intelligence (incl. Robotics); Mathematics, general; Complexity
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1023/A:1018939502485
Publisher site
See Article on Publisher Site

### Abstract

The purpose of this paper is to outline various results regarding the computational complexity and the algorithms of nonmonotonic entailment in different coherence‐based approaches. Starting from a (non necessarily consistent) belief base E and a pre‐order on E, we first present different mechanisms for selecting preferred consistent subsets. Then we present different entailment principles in order to manage these multiple subsets. The crossing point of each generation mechanism m and each entailment principle p defines an entailment relation $$(E, \leqslant )\left| \sim \right.^{p,m} \Phi$$ which we study from the computational complexity point of view. The results are not very encouraging since the complexity of all these nonmonotonic entailment relations is, in most restricted languages, larger than the complexity of monotonic entailment. So, we decided to extend Binary Decision Diagrams technics, which are well suited to the task of solving NP‐hard logic‐based problems. Both theoretical and experimental results are described along this line in the last sections.

### Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Oct 4, 2004

### References

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