this paper describes a new approach for solving disjunctive temporal problems such as the open shop and job shop scheduling domains. Much previous research in systematic search approaches for these problems has focuse...
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ISBN:
(纸本)9783642042430
this paper describes a new approach for solving disjunctive temporal problems such as the open shop and job shop scheduling domains. Much previous research in systematic search approaches for these problems has focused oil developing problem specific constraint propagators and ordering heuristics. Indeed, the common belief is that many of these problems are too difficult to solve without such domain specific models. We introduce a simple constraint model that combines a generic adaptive heuristic with naive propagation, and show that it often outperforms state-of-the-art solvers for both open shop and job shop problems.
Complete row and column symmetry breaking in constraint models using the lex leader method is generally prohibitively costly. Double lex, which is derived from lex leader, is commonly used in practice as all incomplet...
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ISBN:
(纸本)9783642042430
Complete row and column symmetry breaking in constraint models using the lex leader method is generally prohibitively costly. Double lex, which is derived from lex leader, is commonly used in practice as all incomplete symmetry-breaking method for row and column symmetries. Double lex is based on a row-wise canonical variable ordering. However, this choice is arbitrary. We investigate other canonical orderings and focus on one in particular: snake ordering. From this we derive a corresponding incomplete set of symmetry breaking constraints, snake lex. Experimental data comparing double lex and snake lex shows that snake lex is Substantially better than double lex in in many cases.
Stochastic constraintprogramming is an extension of constraintprogramming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision...
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ISBN:
(纸本)9783642042430
Stochastic constraintprogramming is an extension of constraintprogramming for modelling and solving combinatorial problems involving uncertainty. A solution to such a problem is a policy tree that specifies decision variable assignments in each scenario. Several Solution methods have been proposed but none seems practical for large multi-stage problems. We propose all incomplete approach: specifying a policy tree indirectly by a parameterised function, whose parameter values are found by evolutionary search. On some problems this method is orders of magnitude faster than a state-of-the-art scenario-based approach, and it also provides a very compact representation of policy trees.
Accurately estimating the distribution of solutions to a problem, should such solutions exist, provides efficient search heuristics. the purpose of this paper is to propose new ways of computing such estimates, with d...
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ISBN:
(纸本)9783642042430
Accurately estimating the distribution of solutions to a problem, should such solutions exist, provides efficient search heuristics. the purpose of this paper is to propose new ways of computing such estimates, with different degrees of accuracy and complexity. We build oil the Expectation-Maximizatiou Belief-Propagation (EMPB) framework proposed by Hsu et al. to solve constraint Satisfaction Problems (CSPs). We propose two general approaches within the EMBP framework: we firstly derive update rules at;the constraint level while enforcing domain consistency and then derive update rules globally, at the problem level. the contribution of this paper is two-fold: first, we derive new generic update rules suited to tackle ally CSP;second, we propose all efficient EMP-inspired approach, thereby improving this method and making it competitive withthe state of the art. We evaluate these approaches experimentally and demonstrate their effectiveness.
Stochastic constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. the only solution approach to date compiles down SCSPs into classi...
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ISBN:
(纸本)9783642042430
Stochastic constraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a P-Space task. the only solution approach to date compiles down SCSPs into classical CSPs. this allows the rouse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. this paper tries to overcome some of these drawbacks by automatically synthesizing filtering algorithms for global chance-constraints. these filtering algorithms are parameterized by propagators for the deterministic version of the chance-constraints. this approach allows the rouse of existing propagators in current, constraint solvers and it enhances constraint propagation. Experiments show the benefits of this novel approach.
In interactive decision-making settings, such as product configuration, users are stating preferences, or foreground constraints, over a set of possible solutions, as defined by background constraints. When the foregr...
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ISBN:
(纸本)9783642042430
In interactive decision-making settings, such as product configuration, users are stating preferences, or foreground constraints, over a set of possible solutions, as defined by background constraints. When the foreground constraints introduce inconsistencies withthe background constraints, we wish to find explanations that help the user converge to a solution. In order to provide satisfactory explanations, it call be useful to know one or several subsets of conflicting constraints;Such a subset is called a conflict. When computing such conflicts is intractable in an interactive context, we call choose to compile the problem so as to allow faster response times. In this paper we propose a new representation, which implicitly encompasses all conflicts possibly introduced by a user's choices. We claim that it call help in Situations where extra information about conflicts is needed, such as when explanations of inconsistency are required.
We define Realtime Online solving of Quantified constraint Satisfaction Problems (QCSPs) as a, model for realtime online CSP solving. We use a combination of propagation, lookahead and heuristics and show how all thre...
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ISBN:
(纸本)9783642042430
We define Realtime Online solving of Quantified constraint Satisfaction Problems (QCSPs) as a, model for realtime online CSP solving. We use a combination of propagation, lookahead and heuristics and show how all three improve performance. For adversarial opponents we show that we can achieve promising results through good lookahead and heuristics and that a version of alpha beta pruning performs strongly. For random opponents, we show that we can frequently achieve solutions even on problems which lack a winning strategy and that. we can improve our success rate by using Existential Quantified Generalised Arc Consistency;a, lower level of consistency than SQGAC, to maximise pruning without removing solutions. We also consider the power of the universal opponent and show that through good heuristic selection we can generate a significantly stronger player than a static heuristic provides.
constraint propagation solvers interleave propagation, removing impossible values from variable domains, with search. the solver state is modified during propagation. But search requires the solver to return to a prev...
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ISBN:
(纸本)9783642042430
constraint propagation solvers interleave propagation, removing impossible values from variable domains, with search. the solver state is modified during propagation. But search requires the solver to return to a previous state. Hence a, propagation solver must determine how to maintain state during propagation and forward and backward search. this paper sets out the possible ways in which a propagation solver call choose to maintain state, and the restrictions that such choices place on the resulting system. Experiments illustrate the result of various choices for the three principle state components of a solver: variables, propagators, and dependencies between them. this paper also provides the first realistic comparison of trailing versus copying for state restoration.
When interval methods handle systems of equations over the reals, two main types of filtering/contraction algorithms are used to reduce the search space. When the system is well-constrained, interval Newton algorithms...
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ISBN:
(纸本)9783642042430
When interval methods handle systems of equations over the reals, two main types of filtering/contraction algorithms are used to reduce the search space. When the system is well-constrained, interval Newton algorithms behave like a. global constrain over the whole n x n system. Also, filtering algorithms issued from constrahit programming perform an AC3-like propagation loop, where the constraints are iteratively handled one by one by a revise procedure. Applying a revise procedure amounts in contracting a 1 x 1 subsystem. this paper investigates the possibility of defining contracting well-constrained subsystems of size k (1 <= k <= n). We theoretically define the Box-k-consistency as a generalization of the state-of-the-art Box-consistency. Well-constrained subsystems act as global constraints that can bring additional filtering w.r.t. interval Newton and 1 x 1 standard subsystems. Also, the filtering performed inside a subsystem allows the solving process to learn interesting multi-dimensional branching points;i.e., to bisect several variable domains simultaneously. Experiments highlight gains in cpU time w.r.t. state-of-the-art algorithms oil decomposed and structured systems.
We present a memoisation technique for constraint-based local search based on the observation that penalties with respect to some interchangeable elements need only be calculated once. We apply the technique to constr...
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