preference handling
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Author(s):  
Michael Bernreiter ◽  
Jan Maly ◽  
Stefan Woltran

Qualitative Choice Logic (QCL) and Conjunctive Choice Logic (CCL) are formalisms for preference handling, with especially QCL being well established in the field of AI. So far, analyses of these logics need to be done on a case-by-case basis, albeit they share several common features. This calls for a more general choice logic framework, with QCL and CCL as well as some of their derivatives being particular instantiations. We provide such a framework, which allows us, on the one hand, to easily define new choice logics and, on the other hand, to examine properties of different choice logics in a uniform setting. In particular, we investigate strong equivalence, a core concept in non-classical logics for understanding formula simplification, and computational complexity. Our analysis also yields new results for QCL and CCL. For example, we show that the main reasoning task regarding preferred models is ϴ₂P-complete for QCL and CCL, while being Δ₂P-complete for a newly introduced choice logic.


Water ◽  
2017 ◽  
Vol 9 (12) ◽  
pp. 996 ◽  
Author(s):  
Gilberto Reynoso-Meza ◽  
Victor Alves Ribeiro ◽  
Elizabeth Carreño-Alvarado

2015 ◽  
Vol 63 (4) ◽  
pp. 633-652 ◽  
Author(s):  
Kaisa Miettinen ◽  
Dmitry Podkopaev ◽  
Francisco Ruiz ◽  
Mariano Luque

2011 ◽  
Vol 11 (4-5) ◽  
pp. 821-839 ◽  
Author(s):  
MARTIN GEBSER ◽  
ROLAND KAMINSKI ◽  
TORSTEN SCHAUB

AbstractPreference handling and optimization are indispensable means for addressing nontrivial applications in Answer Set Programming (ASP). However, their implementation becomes difficult whenever they bring about a significant increase in computational complexity. As a consequence, existing ASP systems do not offer complex optimization capacities, supporting, for instance, inclusion-based minimization or Pareto efficiency. Rather, such complex criteria are typically addressed by resorting to dedicated modeling techniques, likesaturation. Unlike the ease of common ASP modeling, however, these techniques are rather involved and hardly usable by ASP laymen. We address this problem by developing a general implementation technique by means of meta-prpogramming, thus reusing existing ASP systems to capture various forms of qualitative preferences among answer sets. In this way, complex preferences and optimization capacities become readily available for ASP applications.


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