不完全闭包：不完全或错误数据下的可靠约束推理
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
摘要：
约束编程（CP）已被证明是一种有效的范式，从不同的领域建模和解决困难的组合满意度和优化问题。商业世界产生的许多此类问题都充斥着数据的不确定性。适应不确定性的现有CP方法不太适合由于不完整和错误数据而产生的不确定性，因为它们不能建立可靠的模型和解决方案，以保证解决用户感知到的真正问题。其他领域，如可靠计算，提供模型和相关方法的组合来处理这些类型的不确定数据，但是缺乏独立于模型表征解析方法的表达框架。我们提出了一个统一的框架，在模型和解决方案中扩展了CP形式主义，以解决具有不完整或错误数据的定义不明确的组合问题。确定性闭包框架将来自不同领域的建模和求解方法汇集到CP范式中，为不确定约束问题提供可靠和有效的方法。我们证明了在网络诊断的案例研究框架的适用性。我们定义的决议形式，给出通用模板，及其相关的操作语义，以获得可靠的解决方案的实际解决方案的方法。
英文摘要：
Constraint Programming (CP) has proved an effective paradigm to model and solve difficult combinatorial satisfaction and optimisation problems from disparate domains. Many such problems arising from the commercial world are permeated by data uncertainty. Existing CP approaches that accommodate uncertainty are less suited to uncertainty arising due to incomplete and erroneous data, because they do not build reliable models and solutions guaranteed to address the user's genuine problem as she perceives it. Other fields such as reliable computation offer combinations of models and associated methods to handle these types of uncertain data, but lack an expressive framework characterising the resolution methodology independently of the model. We present a unifying framework that extends the CP formalism in both model and solutions, to tackle ill-defined combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from different fields into the CP paradigm to provide reliable and efficient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We define resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.
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