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Uncertainty and change
 Handbook of Constraint Programming, chapter 21
, 2006
"... Constraint Programming (CP) has proven to be a very successful technique for reasoning about assignment problems, as evidenced by the many applications described elsewhere in this book. Much of its success is due to the simple and elegant underlying formulation: describe the world in terms of decisi ..."
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Cited by 27 (4 self)
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Constraint Programming (CP) has proven to be a very successful technique for reasoning about assignment problems, as evidenced by the many applications described elsewhere in this book. Much of its success is due to the simple and elegant underlying formulation: describe the world in terms of decision variables that must be assigned values, place clear and explicit restrictions on the values that may be assigned simultaneously, and then find a set of assignments to all the variables that obeys those restrictions. Thus, CP makes two assumptions about the problems it tackles: 1. There is no uncertainty in the problem definition: each problem has a crisp and complete description. 2. Problems are not dynamic: they do not change between the initial description and the final execution of the solution. Unfortunately, these two assumptions do not hold for many practical and important applications. For example, scheduling production in a factory is, in practice, fundamentally dynamic and uncertain: the full set of jobs to be scheduled is not known in advance, and continues to grow as existing jobs are being completed; machines break down; raw material
Alea – Grid Scheduling Simulation Environment
"... Abstract. This work concentrates on the design of a system intended for study of advanced scheduling techniques for planning various types of jobs in a Grid environment. The solution is able to deal with common problems of the job scheduling in Grids like heterogeneity of jobs and resources, and dyn ..."
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Cited by 11 (2 self)
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Abstract. This work concentrates on the design of a system intended for study of advanced scheduling techniques for planning various types of jobs in a Grid environment. The solution is able to deal with common problems of the job scheduling in Grids like heterogeneity of jobs and resources, and dynamic runtime changes such as arrivals of new jobs. Our new simulator called Alea is based on the GridSim simulation toolkit which we extended to provide a simulation environment that supports simulation of varying Grid scheduling problems. To demonstrate the features of the GridSim environment, we implemented an experimental centralised Grid scheduler which uses advanced scheduling techniques for schedule generation. By now local search based algorithms and some dispatching rules were tested. The scheduler is capable to handle both static and dynamic situation. In the static case, all jobs are known in advance while the dynamic situation means that jobs appear in the system during simulation. In this case generated schedule is changing through time as some jobs are already finished while the new ones are arriving. Comparison of FCFS, local search and dispatching rules is presented for both cases and we demonstrate that the new local search based algorithm provides the best schedule while keeping the running time acceptable.
Interaction between reactive and deliberative tasks for online decisionmaking
"... To get actual autonomous engines or systems, it is necessary to equip them with online decisionmaking mechanisms: computation of decisions that fit the current situation, performed in parallel with real execution. However, such a computation introduces a contradiction between requirements on the q ..."
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Cited by 7 (2 self)
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To get actual autonomous engines or systems, it is necessary to equip them with online decisionmaking mechanisms: computation of decisions that fit the current situation, performed in parallel with real execution. However, such a computation introduces a contradiction between requirements on the quality of the decision and on the time at which it will be delivered. This contradiction is all the stronger as decisionmaking may require intensive computing whose duration is not very well controlled. The solutions that are proposed in the literature to overcome this contradiction are often unsatisfactory, either from the point of view of the quality requirements, or from the one of the temporal requirements, most of the time from the one of the organisation and validation of the whole decisionmaking mechanism.
Constraint Programming for Path Planning with Uncertainty Solving the Optimal Search Path Problem
"... Abstract. The optimal search path (OSP) problem is a singlesided detection search problem where the location and the detectability of a moving object are uncertain. A solution to this NPhard problem is a path on a graph that maximizes the probability of finding an object that moves according to a ..."
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Cited by 6 (6 self)
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Abstract. The optimal search path (OSP) problem is a singlesided detection search problem where the location and the detectability of a moving object are uncertain. A solution to this NPhard problem is a path on a graph that maximizes the probability of finding an object that moves according to a known motion model. We developed constraint programming models to solve this probabilistic path planning problem for a single indivisible searcher. These models include a simple but powerful branching heuristic as well as strong filtering constraints. We present our experimentation and compare our results with existing results in the literature. The OSP problem is particularly interesting in that it generalizes to various probabilistic search problems such as intruder detection, malicious code identification, search and rescue, and surveillance. 1
Algorithms for stochastic csps
 In Proceedings of the 12th International Conference on the Principles and Practice of Constraint Programming
, 2006
"... Abstract. The Stochastic CSP (SCSP) is a framework recently introduced by Walsh to capture combinatorial decision problems that involve uncertainty and probabilities. The SCSP extends the classical CSP by including both decision variables, that an agent can set, and stochastic variables that follow ..."
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Cited by 5 (0 self)
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Abstract. The Stochastic CSP (SCSP) is a framework recently introduced by Walsh to capture combinatorial decision problems that involve uncertainty and probabilities. The SCSP extends the classical CSP by including both decision variables, that an agent can set, and stochastic variables that follow a probability distribution and can model uncertain events beyond the agent’s control. So far, two approaches to solving SCSPs have been proposed; backtrackingbased procedures that extend standard methods from CSPs, and scenariobased methods that solve SCSPs by reducing them to a sequence of CSPs. In this paper we further investigate the former approach. We first identify and correct a flaw in the forward checking (FC) procedure proposed by Walsh. We also extend FC to better take advantage of probabilities and thus achieve stronger pruning. Then we define arc consistency for SCSPs and introduce an arc consistency algorithm that can handle constraints of any arity. 1
T.: Conditional constraint satisfaction: logical foundations and complexity
 In: Proc. of IJCAI
, 2007
"... Conditional Constraint Satisfaction Problems (CCSPs) are generalizations of classical CSPs that support conditional activation of variables and constraints. Despite the interest emerged for CCSPs in the context of modelling the intrinsic dynamism of diagnosis, structural design, and product confi ..."
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Cited by 5 (1 self)
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Conditional Constraint Satisfaction Problems (CCSPs) are generalizations of classical CSPs that support conditional activation of variables and constraints. Despite the interest emerged for CCSPs in the context of modelling the intrinsic dynamism of diagnosis, structural design, and product configuration applications, a complete characterization of their computational properties and of their expressiveness is still missing. In fact, the aim of the paper is precisely to face these open research issues. First, CCSPs are formally characterized in terms of a suitable fragment of firstorder logic. Second, the complexity of some basic reasoning tasks for CCSPs is studied, by establishing completeness results for the first and the second level of the polynomial hierarchy. Finally, motivated by the hardness results, an island of tractability for CCSPs is identified, by extending structural decomposition methods originally proposed for CSPs. 1
Certainty Closure: Reliable Constraint Reasoning with Incomplete or Erroneous Data
"... Constraint Programming (CP) has proved an e ective paradigm to model and solve di cult 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 uncertai ..."
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Cited by 4 (2 self)
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Constraint Programming (CP) has proved an e ective paradigm to model and solve di cult 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 elds such as reliable computation o er 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 illde ned combinatorial problems with incomplete or erroneous data. The certainty closure framework brings together modelling and solving methodologies from di erent elds into the CP paradigm to provide reliable and e cient approches for uncertain constraint problems. We demonstrate the applicability of the framework on a case study in network diagnosis. We de ne resolution forms that give generic templates, and their associated operational semantics, to derive practical solution methods for reliable solutions.
Solving Dynamic Constraint Satisfaction Problems by Identifying Stable Features
, 2009
"... This paper presents a new analysis of dynamic constraint satisfaction problems (DCSPs) with finite domans and a new approach to solving them. We first show that even very small changes in a CSP, in the form of addition of constraints or changes in constraint relations, can have profound effects on s ..."
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Cited by 3 (1 self)
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This paper presents a new analysis of dynamic constraint satisfaction problems (DCSPs) with finite domans and a new approach to solving them. We first show that even very small changes in a CSP, in the form of addition of constraints or changes in constraint relations, can have profound effects on search performance. These effects are reflected in the amenability of the problem to different forms of heuristic action as well as overall quality of search. In addition, classical DCSP methods perform poorly on these problems because there are sometimes no solutions similar to the original one found. We then show that the same changes do not markedly affect the locations of the major sources of contention in the problem. A technique for iterated sampling that performs a careful assessment of this property and uses the information during subsequent search, performs well even when it only uses information based on the original problem in the DCSP sequence. The result is a new approach to solving DCSPs that is based on a robust strategy for ordering variables rather than on robust solutions.
From Unsolvable to Solvable: An Exploration of Simple Changes
"... This paper investigates how readily an unsolvable constraint satisfaction problem can be reformulated so that it becomes solvable. We investigate small changes in the definitions of the problem’s constraints, changes that alter neither the structure of its constraint graph nor the tightness of its c ..."
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Cited by 2 (0 self)
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This paper investigates how readily an unsolvable constraint satisfaction problem can be reformulated so that it becomes solvable. We investigate small changes in the definitions of the problem’s constraints, changes that alter neither the structure of its constraint graph nor the tightness of its constraints. Our results show that structured and unstructured problems respond differently to such changes, as do easy and difficult problems taken from the same problem class. Several plausible explanations for this behavior are discussed.
Modeling robustness in csps as weighted csps
 In Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems CPAIOR 2013
, 2013
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