Constraint Retraction in CLP(FD): Formal Framework and Performance Results

Constraint retraction can be described, in general, as the possibility of deleting a previously stated piece of information. This is obviously very convenient in many programming frameworks, especially in those that involve some level of interaction between the user and the system, or also in those concerning rescheduling or replanning. Nevertheless, constraint retraction is usually not provided in current constraint programming environments. This is mainly due to its high complexity and also to its non-monotonic nature, which would make most of such systems much more complex to reason with. In this paper we avoid these problems by considering a specific constraint programming framework, called clpFD, that is, constraint logic programming (CLP) over finite domain (FD) constraints. We propose an algorithm which deletes a constraint from a set of FD constraints, while maintaining partial arc-consistency, which is usual in this programming framework. What is crucial is that the retraction operation we propose is incremental, in that it follows the chain of dependencies among variables which are set by the nature of the FD constraints, and by doing so it updates only the part of the constraint set which is affected by the deletion. We also detail how constraint retraction can be incorporated in the FD constraint solver and we evaluate its behavior within the clpFD system. Experimental results on usual benchmarks, on classes of problems of increasing connectivity, and also on a real-life problem show that in almost all cases the use of our retraction algorithm provides great speed-up with respect to standard methods while not slowing down the clpFD system when no retraction is performed. This provides the system with an efficient way of retracting constraints while not changing its performance when the user does not want to use this new feature.     

一种基于模型的特征交互检测方法

为了适应业务的不断更新,许多软件系统通过向公共的基础系统插入新的扩展来实现演化.这种演化策略虽然有利于并行开发和部署,但也面临着扩展间可能发生非预期特征交互的问题.目前,形式化方法在检测特征交互问题方面仍然是最有效的方法之一.这类方法着眼于检测扩展之间是否会发生冲突.虽然在小规模实验上较为成功,但是它们也面临着一些挑战.例如:扩展的非单调性、扩展组合的激增以及扩展模型可能无法获知的问题.实际上,许多特征交互都源于新扩展对基系统和已有扩展造成的不恰当影响.基于这种认识,集中关注由于扩展的不恰当影响所导致的交互冲突问题,提出了如何从已知的特征交互实例来分析产生冲突的原因的具体方法,并说明了如何制定约束以限制扩展中易导致冲突的行为,从而预防同一类行为可能导致的各种冲突.该方法被应用到电信系统特征交互的分析上,实验结果表明,大部分特征交互中导致冲突的行为都可以被检测出来.该方法不仅能够保证原有基系统或扩展模型的稳定、有效,避免扩展组合带来的问题,而且它无须公布扩展的模型细节.     

Local Logics, Non-Monotonicity and Defeasible Argumentation

In this paper we present an embedding of abstract argumentation systems into the framework of Barwise and Seligmans logic of information flow. We show that, taking P.M. Dungs characterization of argument systems, a local logic over states of a deliberation may be constructed. In this structure, the key feature of non-monotonicity of commonsense reasoning obtains as the transition from one local logic to another, due to a change in certain background conditions. Each of Dungs extensions of argument systems leads to a corresponding ordering of background conditions. The relations among extensions becomes a relation among partial orderings of background conditions. This introduces a conceptual innovation in Barwise and Seligmans representation of commonsense reasoning.