Disjunctive Temporal Problems (DTPs) with Preferences (DTPPs) extend DTPs with piece-wise constant preference functions associated to each constraint of the form l ≤ x − y ≤ u, where x,y are (real or integer) variables, and l,u are numeric constants. The goal is to find an assignment to the variables of the problem that maximizes the sum of the preference values of satisfied DTP constraints, where such values are obtained by aggregating the preference functions of the satisfied constraints in it under a “max” semantic. The state-of-the-art approach in the field, implemented in the native DTPP solver Maxilitis, extends the approach of the native DTP solver Epilitis. In this paper we present alternative approaches that translate DTPPs to Maximum Satisfiability of a set of Boolean combination of constraints of the form l⋈x − y⋈u, ⋈ ∈{<,≤}, that extend previous work dealing with constant preference functions only. We prove correctness and completeness of the approaches. Results obtained with the Satisfiability Modulo Theories (SMT) solvers Yices and MathSAT on randomly generated DTPPs and DTPPs built from real-world benchmarks, show that one of our translation is competitive to, and can be faster than, Maxilitis (This is an extended and revised version of Bourguet et al. 2013).

中文翻译：

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基于翻译的解决带有偏好的析取时间问题的方法

分离性颞问题（DTPS）与偏好（DTPPs）延伸，以与关联到所述形式的每个约束逐段恒定偏爱功能DTPS升≤ X - ÿ ≤ ü，其中X，ÿ是（真实的或整数）变量，并升，u是数字常量。目的是找到对问题变量的赋值，该赋值最大化满足DTP约束的偏好值的总和，其中通过在“最大”语义下聚合满足条件的约束的偏好函数来获得这些值。在本机DTPP求解器Maxilitis中实施的最新技术，扩展了本地DTP解决者癫痫的方法。在本文中，我们本替代接近于翻译DTPPs到最大可满足一组的形式的约束的布尔组合的升⋈ X - ÿ ⋈ ü，⋈∈{<，≤}，延伸以前的工作处理常数偏好函数只。我们证明了这些方法的正确性和完整性。使用满意度模块理论（SMT）求解器Yices和MathSAT在根据实际基准构建的随机生成的DTPP和DTPP上获得的结果表明，我们的翻译之一比Maxilitis具有竞争力，并且可以比Maxilitis更快 （这是Bourguet等人2013年的扩展和修订版本）。