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Relation variables in qualitative spatial reasoning
 Proc. of 27th German Annual Conference on Artificial Intelligence (KI’04), volume 3238 of LNAI
, 2004
"... Abstract. We study an alternative to the prevailing approach to modelling qualitative spatial reasoning (QSR) problems as constraint satisfaction problems. In the standard approach, a relation between objects is a constraint whereas in the alternative approach it is a variable. By being declarative, ..."
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Abstract. We study an alternative to the prevailing approach to modelling qualitative spatial reasoning (QSR) problems as constraint satisfaction problems. In the standard approach, a relation between objects is a constraint whereas in the alternative approach it is a variable. By being declarative, the relationvariable approach greatly simplifies integration and implementation of QSR. To substantiate this point, we discuss several specific QSR algorithms from the literature which in the relationvariable approach reduce to the customary constraint propagation algorithm enforcing generalised arcconsistency. 1
Constraintbased qualitative simulation
 In Proc. of 12th International Symposium on Temporal Representation and Reasoning (TIME’05
, 2005
"... We consider qualitative simulation involving a finite set of qualitative relations in presence of complete knowledge about their interrelationship. We show how it can be naturally captured by means of constraints expressed in temporal logic and constraint satisfaction problems. The constraints relat ..."
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We consider qualitative simulation involving a finite set of qualitative relations in presence of complete knowledge about their interrelationship. We show how it can be naturally captured by means of constraints expressed in temporal logic and constraint satisfaction problems. The constraints relate at each stage the ‘past ’ of a simulation with its ‘future’. The benefit of this approach is that it readily leads to an implementation based on constraint technology that can be used to generate simulations and to answer queries about them. 1
Infinite qualitative simulations by means of constraint programming
 In CP’06
, 2006
"... Abstract. We introduce a constraintbased framework for studying infinite qualitative simulations concerned with contingencies such as time, space, shape, size, abstracted into a finite set of qualitative relations. To define the simulations we combine constraints that formalize the background knowl ..."
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Abstract. We introduce a constraintbased framework for studying infinite qualitative simulations concerned with contingencies such as time, space, shape, size, abstracted into a finite set of qualitative relations. To define the simulations we combine constraints that formalize the background knowledge concerned with qualitative reasoning with appropriate interstate constraints that are formulated using linear temporal logic. We implemented this approach in a constraint programming system (ECL i PS e) by drawing on the ideas from bounded model checking. The implementation became realistic only after several rounds of optimizations and experimentation with various heuristics. The resulting system allows us to test and modify the problem specifications in a straightforward way and to combine various knowledge aspects. To demonstrate the expressiveness and simplicity of this approach we discuss in detail two examples: a navigation problem and a simulation of juggling.
A Combined Approach for Constraints over Finite Domains and Arrays?
"... Abstract. Arrays are ubiquitous in the context of software verification. However, effective reasoning over arrays is still rare in CP, as local reasoning is dramatically illconditioned for constraints over arrays. In this paper, we propose an approach combining both global symbolic reasoning and l ..."
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Abstract. Arrays are ubiquitous in the context of software verification. However, effective reasoning over arrays is still rare in CP, as local reasoning is dramatically illconditioned for constraints over arrays. In this paper, we propose an approach combining both global symbolic reasoning and local consistency filtering in order to solve constraint systems involving arrays (with accesses, updates and size constraints) and finitedomain constraints over their elements and indexes. Our approach, named fdcc, is based on a combination of a congruence closure algorithm for the standard theory of arrays and a CP solver over finite domains. The tricky part of the work lies in the bidirectional communication mechanism between both solvers. We identify the significant information to share, and design ways to master the communication overhead. Experiments on random instances show that fdcc solves more formulas than any portfolio combination of the two solvers taken in isolation, while overhead is kept reasonable.