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22
A Model Seeker: Extracting Global Constraint Models From Positive Examples
"... Abstract. We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, whi ..."
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Abstract. We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, which finds a ranked list of matching constraints given one or more sample call patterns. We have tested the modeler with 230 examples, ranging from 4 to 6,500 variables, using between 1 and 7,000 samples. These examples come from a variety of domains, including puzzles, sportsscheduling, packing & placement, and design theory. When comparing against manually specified “canonical ” models for the examples, we achieve a hit rate of 50%, processing the complete benchmark set in less than one hour on a laptop. Surprisingly, in many cases the system finds usable candidate lists even when working with a single, positive example. 1
A Constraint Seeker: Finding and Ranking Global Constraints from Examples
, 2011
"... Abstract. In this paper we describe a Constraint Seeker application which provides a web interface to search for global constraints in the global constraint catalog, given positive and negative, fully instantiated (ground) examples. Based on the given instances the tool returns a ranked list of matc ..."
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Abstract. In this paper we describe a Constraint Seeker application which provides a web interface to search for global constraints in the global constraint catalog, given positive and negative, fully instantiated (ground) examples. Based on the given instances the tool returns a ranked list of matching constraints, the rank indicating whether the constraint is likely to be the intended constraint of the user. We give some examples of use cases and generated output, describe the different elements of the search and ranking process, discuss the role of constraint programming in the different tools used, and provide evaluation results over the complete global constraint catalog. The Constraint Seeker is an example for the use of generic metadata provided in the catalog to solve a specific problem. 1
Constraint Acquisition via Partial Queries
"... We learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm that, given a negative example, focuses onto a constraint of the target network in a number of queries logari ..."
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Cited by 6 (5 self)
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We learn constraint networks by asking the user partial queries. That is, we ask the user to classify assignments to subsets of the variables as positive or negative. We provide an algorithm that, given a negative example, focuses onto a constraint of the target network in a number of queries logarithmic in the size of the example. We give information theoretic lower bounds for learning some simple classes of constraint networks and show that our generic algorithm is optimal in some cases. Finally we evaluate our algorithm on some benchmarks.
Soft Constraints of Difference and Equality
"... In many combinatorial problems one may need to model the diversity or similarity of sets of assignments. For example, one may wish to maximise or minimise the number of distinct values in a solution. To formulate problems of this type we can use soft variants of the well known AllDifferent and AllEq ..."
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In many combinatorial problems one may need to model the diversity or similarity of sets of assignments. For example, one may wish to maximise or minimise the number of distinct values in a solution. To formulate problems of this type we can use soft variants of the well known AllDifferent and AllEqual constraints. We present a taxonomy of six soft global constraints, generated by combining the two latter ones and the two standard cost functions, which are either maximised or minimised. We characterise the complexity of achieving arc and bounds consistency on these constraints, resolving those cases for which NPhardness was neither proven nor disproven. In particular, we explore in depth the constraint ensuring that at least k pairs of variables have a common value. We show that achieving arc consistency is NPhard, however bounds consistency can be achieved in polynomial time through dynamic programming. Moreover, we show that the maximum number of pairs of equal variables can be approximated by a factor of 1 2 with a linear time greedy algorithm. Finally, we provide a fixed parameter tractable algorithm with respect to the number of values appearing in more than two distinct domains. Interestingly, this taxonomy shows that enforcing equality is harder than enforcing difference. 1.
Automatic design of robot behaviors through constraint network acquisition
 In Proceedings of the 20th IEEE International Conference on Tools for Artificial Intelligence (IEEEICTAI’08
, 2008
"... Control architectures, such as the LAAS architecture [1], CLARATY [12] and HARPIC [9], have been developped to provide autonomy to robots. To achieve a robot’s task, these control architectures plan sequences of sensorimotor behaviors. Currently carried out by roboticians, the design of sensorimotor ..."
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Control architectures, such as the LAAS architecture [1], CLARATY [12] and HARPIC [9], have been developped to provide autonomy to robots. To achieve a robot’s task, these control architectures plan sequences of sensorimotor behaviors. Currently carried out by roboticians, the design of sensorimotor behaviors is a truly complex task that can require many hours of hard work and intensive computations. In this paper, we propose a Constraint Programmingbased framework to interact with roboticians during the sensorimotor behaviors design. A constraint network acquisition platform and a CSPBased planner are used to automatically design sensorimotor behaviors. Moreover, our architecture exploits the propagation properties of the acquired CSPs to supervise the execution of a given sensorimotor behavior. Some experimental results are presented to validate our approach. 1
Exploiting automatically inferred constraintmodels for building identification in satellite imagery
, 2007
"... The building identification (BID) problem is based on a process that uses publicly available information to automatically assign addresses to buildings in satellite imagery. In previous work, we have shown the advantages of casting the BID problem as a Constraint Satisfaction Problem (CSP) using the ..."
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The building identification (BID) problem is based on a process that uses publicly available information to automatically assign addresses to buildings in satellite imagery. In previous work, we have shown the advantages of casting the BID problem as a Constraint Satisfaction Problem (CSP) using the same generic constraintmodel to represent all problem instances. However, a generic model is unable to represent with the necessary precision the addressing variations throughout the world, limiting the applicability of our previous approach. In this paper, we describe the endtoend process used to solve the BID with a new modelgeneration technique that uses instancespecific information to automatically infer a representative constraint model of the BID. This inferred model is used by our custom constraint solver to identify buildings in satellite imagery more efficiently and with higher precision than using a single model. We evaluate our approach on El Segundo California, and empirically demonstrate its effectiveness for geographic areas larger than previously tested. We conclude with a discussion of the generality of our approach, and present directions for future work.
Solve a constraint problem without modeling it
 In 26th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2014, Limassol
"... Abstract—We study how to find a solution to a constraint problem without modeling it. Constraint acquisition systems such as Conacq or ModelSeeker are not able to solve a single instance of a problem because they require positive examples to learn. The recent QuAcq algorithm for constraint acquisiti ..."
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Abstract—We study how to find a solution to a constraint problem without modeling it. Constraint acquisition systems such as Conacq or ModelSeeker are not able to solve a single instance of a problem because they require positive examples to learn. The recent QuAcq algorithm for constraint acquisition does not require positive examples to learn a constraint network. It is thus able to solve a constraint problem without modeling it: we simply exit from QuAcq as soon as a complete example is classified as positive by the user. In this paper, we propose ASK&SOLVE, an elicitationbased solver that tries to find the best tradeoff between learning and solving to converge as soon as possible on a solution. We propose several strategies to speedup ASK&SOLVE. Finally we give an experimental evaluation that shows that our approach improves the state of the art. KeywordsElicitation based resolution; Constraint acquisition; Constraint Learning I.
Using the Global Constraint Seeker for Learning Structured Constraint Models: a First Attempt
"... Abstract. Considering problems that have a strong internal structure, this paper shows how to generate constraint models from a set of positive, flat samples (i.e., solutions) without knowing a priori neither the constraint candidates, nor the way variables are shared within constraints. We describe ..."
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Abstract. Considering problems that have a strong internal structure, this paper shows how to generate constraint models from a set of positive, flat samples (i.e., solutions) without knowing a priori neither the constraint candidates, nor the way variables are shared within constraints. We describe two key contributions to building such a model generator: (1) First, learning is modeled as a bicriteria optimization problem over ranked constraint candidates returned by the Constraint Seeker, where we optimize both the compactness of the model, and the rank (or appropriateness) of the selected constraints. (2) Second, filtering out irrelevant candidate models is achieved by using meta data of the global constraint catalog that describe links between constraints. Some initial experiments on a proofofconcept implementation show promising results. 1 Scope and Hypothesis Global constraints were initially introduced [3] in order to more efficiently handle the filtering associated with some recurring structured constraint networks [1]. An inherent disadvantage of the approach is that the introduction of global constraints does not
Automating Inference of OCL Business Rules from User Scenarios
, 2013
"... Abstract—User Scenarios have been advocated as an effective means to capture requirements by describing the systemtobe at the instance or example level. This instancelevel information is then used to infer a possible software specification consistent with the provided valid and invalid scenarios. ..."
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Abstract—User Scenarios have been advocated as an effective means to capture requirements by describing the systemtobe at the instance or example level. This instancelevel information is then used to infer a possible software specification consistent with the provided valid and invalid scenarios. So far existing approaches have often focused on the generation of static models but have omitted the inference of business rules that could complement the static models and improve the precision of the software specification. In this sense this paper provides a first set of invariant inference patterns that are applied on valid and invalid snapshots in order to generate OCL (Object Constraint Language) integrity constraints that the system should always satisfy. We strengthen the confidence of inferred results based on the user’s feedback of generated examples and counterexamples for the considered constraint. The approach is realized with a prologbased tool that could support the designer to effectively define OCL integrity constraints in a semiautomatic way.
Exploiting Problem Data to Enrich Models of Constraint Problems
, 2007
"... In spite of the effectiveness of Constraint Programming languages and tools, modeling remains an art and requires significant involvement from a CP expert. Our goal is to alleviate the load of the human user, and this paper is a first step in this direction. We propose a framework that enriches a ..."
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In spite of the effectiveness of Constraint Programming languages and tools, modeling remains an art and requires significant involvement from a CP expert. Our goal is to alleviate the load of the human user, and this paper is a first step in this direction. We propose a framework that enriches a ‘generic ’ constraint model of a domain area with a set of constraints that are applicable to a particular problem instance. The additional constraints used to enrich the model are selected from a library; and a set of rules determines their applicability given the input data from the instance at hand. We address application domains where problem instances slightly vary in terms of the applicable constraints, such as the Building Identification (BID) problem and we use Sudoku puzzles as a vehicle to illustrate the concepts and issues involved. To evaluate our approach, we apply it to these domains, using constraint propagation on the generic model to uncover additional information about the problem instance. Our initial results demonstrate our ability to create customized models whose accuracy is further improved with the use of constraint propagation. We also discuss results obtained by solving the newly inferred models, showing that the combination of rulebased constraint inference and constraint propagation is a step towards precise modeling. Finally, we discuss domains that can benefit from our approach (e.g., timetabling and machine translation) and present directions for future work.