From backdoor key to backdoor completability: improving a known measure of hardness for the satisfiable CSP

Many studies have been conducted on the complexity of Constraint Satisfaction Problem (CSP) classes. However, there exists little theoretical work on the hardness of individual CSP instances. In this context, the backdoor key fraction (BKF) [17] was introduced as a quantifier of problem hardness for individual satisfiable instances with regard to backtracking search. In our paper, after highlighting the weaknesses of the BKF, we propose a better characterization of the hardness of an individual satisfiable CSP instance based on the ratio between the size of the solution space and that of the search space. We formally show that our measure is negatively correlated with instance hardness. We also show through experiments that this measure evaluates more accurately the hardness of individual instances than the BKF

A specification-based QoS-aware design framework for service-based applications

Effective and accurate service discovery and composition rely on complete specifications of service behaviour, containing inputs and preconditions that are required before service execution, outputs, effects and ramifications of a successful execution and explanations for unsuccessful executions. The previously defined Web Service Specification Language (WSSL) relies on the fluent calculus formalism to produce such rich specifications for atomic and composite services. In this work, we propose further extensions that focus on the specification of QoS profiles, as well as partially observable service states. Additionally, a design framework for service-based applications is implemented based on WSSL, advancing state of the art by being the first service framework to simultaneously provide several desirable capabilities, such as supporting ramifications and partial observability, as well as non-determinism in composition schemas using heuristic encodings; providing explanations for unexpected behaviour; and QoS-awareness through goal-based techniques. These capabilities are illustrated through a comparative evaluation against prominent state-of-the-art approaches based on a typical SBA design scenario

Weak, Strong and Dynamic Controllability of Access-Controlled Workflows Under Conditional Uncertainty

A workflow (WF) is a formal description of a business process in which single atomic work units (tasks), organized in a partial order, are assigned to processing entities (agents) in order to achieve some business goal(s). A workflow management system must coordinate the execution of tasks and WF instances. Usually, the assignment of tasks to agents is accomplished by external constraints not represented in a WF. An access-controlled workflow (ACWF) extends a classical WF by explicitly representing agent availability for each task and authorization constraint. Authorization constraints model which users are authorized for which tasks depending on \u201cwho did what\u201d. Recent research has addressed temporal controllability of WFs under conditional and temporal uncertainty. However, controllability analysis for ACWFs under conditional uncertainty has never been addressed before. In this paper, we define weak, strong and dynamic controllability of ACWFs under conditional uncertainty, we present algorithmic approaches to address each of these types of controllability, and we synthesize execution strategies that specify which user has been (or will be) assigned to which task

The power of propagation:when GAC is enough

Considerable effort in constraint programming has focused on the development of efficient propagators for individual constraints. In this paper, we consider the combined power of such propagators when applied to collections of more than one constraint. In particular we identify classes of constraint problems where such propagators can decide the existence of a solution on their own, without the need for any additional search. Sporadic examples of such classes have previously been identified, including classes based on restricting the structure of the problem, restricting the constraint types, and some hybrid examples. However, there has previously been no unifying approach which characterises all of these classes: structural, language-based and hybrid. In this paper we develop such a unifying approach and embed all the known classes into a common framework. We then use this framework to identify a further class of problems that can be solved by propagation alone

Structural Engineering Design Support By Constraint Satisfaction

Design tasks in structural engineering have always involved the use of constraints to formulate design requirements. Most existing algorithms for constraint satisfaction require input consisting of binary constraints on variables that have discrete values. Such restrictions limit their use in structural engineering since typical structural design tasks involve discrete and numerical variables. This paper provides an approach for decision support through approximating solution spaces of such constraint problems by local consistency. The approach is demonstrated for the selection of appropriate wind bracing for single story steel-framed buildings involving more than hundred variables