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Solving and learning a tractable class of soft temporal constraints: Theoretical and experimental results

机译：解决和学习一类易处理的软时间约束：理论和实验结果

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摘要

Often we need to work in scenarios where events happen over time and preferences are associated with event distances and durations. Soft temporal constraints allow one to describe in a natural way problems arising in such scenarios.rnIn general, solving soft temporal problems requires exponential time in the worst case, but there are interesting subclasses of problems which are polynomially solvable. In this paper we identify one of such subclasses, that is, simple fuzzy temporal problems with semi-convex preference functions, giving tractability results. Moreover, we describe two solvers for this class of soft temporal problems, and we show some experimental results. The random generator used to build the problems on which tests are performed is also described. We also compare the two solvers highlighting the tradeoff between performance and robustness.rnSometimes, however, temporal local preferences are difficult to set, and it may be easier instead to associate preferences to some complete solutions of the problem. To model everything in a uniform way via local preferences only, and also to take advantage of the existing constraint solvers which exploit only local preferences, we show that machine learning techniques can be useful in this respect. In particular, we present a learning module based on a gradient descent technique which induces local temporal preferences from global ones. We also show the behavior of the learning module on randomly-generated examples.
机译：通常，我们需要在事件随时间发生并且喜好与事件距离和持续时间相关联的情况下工作。软时间约束允许人们以自然的方式描述在这种情况下出现的问题。通常，解决软时间问题在最坏的情况下需要指数时间，但是存在有趣的问题子类，这些子类可以多项式求解。在本文中，我们确定了其中一个子类，即具有半凸偏好函数的简单模糊时间问题，从而给出了可处理性结果。此外，我们针对此类软时间问题描述了两种求解器，并给出了一些实验结果。还描述了用于生成要执行测试的问题的随机生成器。我们还比较了这两个求解器，突出显示了性能和鲁棒性之间的折衷。但是，有时，难以设置时间局部偏好，而将偏好与问题的某些完整解决方案关联起来可能会更容易。为了仅通过局部偏好以统一的方式对所有事物建模，并且还利用仅利用局部偏好的现有约束求解器，我们证明了机器学习技术在这方面可能是有用的。特别是，我们提出了一种基于梯度下降技术的学习模块，该技术可从全局偏好中引入局部时态偏好。我们还将在随机生成的示例中显示学习模块的行为。

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