Complexity results for preference aggregation over (m)CP-nets: Max and rank voting
نویسندگان
چکیده
Aggregating preferences over combinatorial domains has a plethora of applications in AI. Due to the exponential nature preferences, compact representations are needed, and conditional ceteris paribus preference networks (CP-nets) among most studied representation formalisms. Unlike problem outcome dominance individual CP-nets, which received an extensive complexity analysis literature, mCP-nets (and global voting/preference aggregation CP-nets) lacked such thorough characterization, despite this being reported multiple times literature as open problem. An initial for was carried out only recently, where Pareto majority semantics were studied. In paper, we further explore mCP-nets, giving precise when max rank voting schemes considered. particular, show that deciding under is Θ2P-complete, while optimal outcomes their existence complete Π2P Σ3P, respectively. We also that, voting, optimum Π2P-complete, Π2P-hard Σ3P. As apart from whether have outcomes, trivial problem, all other tasks considered tractable P. Interestingly, here these problems not P, but P-hard hence P-complete). Furthermore, known be feasible polynomial time, actually P-complete, well various CP-nets P-complete. These results provide interesting insights, P-complete (currently believed be) inherently sequential, they cannot benefit highly parallel computations.
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 2022
ISSN: ['2633-1403']
DOI: https://doi.org/10.1016/j.artint.2021.103636