11 research outputs found

    A Concurrent Language for Argumentation: Preliminary Notes

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    While agent-based modelling languages naturally implement concurrency, the currently available languages for argumentation do not allow to explicitly model this type of interaction. In this paper we introduce a concurrent language for handling process arguing and communicating using a shared argumentation framework (reminding shared constraint store as in concurrent constraint). We introduce also basic expansions, contraction and revision procedures as main bricks for enforcement, debate, negotiation and persuasion

    Set Unification

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    The unification problem in algebras capable of describing sets has been tackled, directly or indirectly, by many researchers and it finds important applications in various research areas--e.g., deductive databases, theorem proving, static analysis, rapid software prototyping. The various solutions proposed are spread across a large literature. In this paper we provide a uniform presentation of unification of sets, formalizing it at the level of set theory. We address the problem of deciding existence of solutions at an abstract level. This provides also the ability to classify different types of set unification problems. Unification algorithms are uniformly proposed to solve the unification problem in each of such classes. The algorithms presented are partly drawn from the literature--and properly revisited and analyzed--and partly novel proposals. In particular, we present a new goal-driven algorithm for general ACI1 unification and a new simpler algorithm for general (Ab)(Cl) unification.Comment: 58 pages, 9 figures, 1 table. To appear in Theory and Practice of Logic Programming (TPLP

    Constraint propagation in Mozart

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    This thesis presents constraint propagation in Mozart which is based on computational agents called propagators. The thesis designs, implements, and evaluates propagator-based propagation engines. A propagation engine is split up in generic propagation services and domain specific domain solvers which are connected by a constraint programming interface. Propagators use filters to perform constraint propagation. The interface isolates filters from propagators such that they can be shared among various systems. This thesis presents the design and implementation of a finite integer set domainsolver for Mozart which reasons over bound and cardinality approximations of sets.The solver cooperates with a finite domain solver to improve its propagation and expressiveness. This thesis promotes constraints to first-class citizens and thus, provides extra control over constraints. Novel programming techniques taking advantage of the first-class status of constraints are developed and illustrated.Diese Dissertation beschreibt Constraint-Propagierung in Mozart, die auf Berechnungsagenten, Propagierer genannt, basiert. Die Dissertation entwirft, implementiert und evaluiert Propagierer-basierte Propagierungsmaschinen. Eine Propagierungsmaschine ist aufgeteilt in generische Propagierungsdienste und domänenspezifische Domänenlöser, die durch eine Schnittstelle zur Constraint-Programmierung miteinander verbunden sind. Propagierer benutzen Filter, um Constraints zu propagieren. Die Schnittstelle isoliert Filter von Propagierern, so dass Programmkodes von Filtern von verschiedenen Systemen genutzt werden können. Diese Dissertation präsentiert den Entwurf und die Implementierung eines Domänenlösers über endliche Mengen von ganzen Zahlen für Mozart, die über Mengen- und Kardinalitätsschranken approximiert werden. Dieser kooperiert mit einem Löser über endlichen Bereichen, um die Propagierung und die Ausdrucksfähigkeit zu verbessern. Diese Dissertation erhebt Constraints zu emanzipierten Datenstrukturen und stellt auf dieseWeise zusätzliche Steuerungsmöglichkeiten über Constraints zur Verfügung. Des Weiteren werden neuartige Programmiertechniken für emanzipierte Constraints entwickelt und demonstriert

    Constraint propagation in Mozart

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    This thesis presents constraint propagation in Mozart which is based on computational agents called propagators. The thesis designs, implements, and evaluates propagator-based propagation engines. A propagation engine is split up in generic propagation services and domain specific domain solvers which are connected by a constraint programming interface. Propagators use filters to perform constraint propagation. The interface isolates filters from propagators such that they can be shared among various systems. This thesis presents the design and implementation of a finite integer set domainsolver for Mozart which reasons over bound and cardinality approximations of sets.The solver cooperates with a finite domain solver to improve its propagation and expressiveness. This thesis promotes constraints to first-class citizens and thus, provides extra control over constraints. Novel programming techniques taking advantage of the first-class status of constraints are developed and illustrated.Diese Dissertation beschreibt Constraint-Propagierung in Mozart, die auf Berechnungsagenten, Propagierer genannt, basiert. Die Dissertation entwirft, implementiert und evaluiert Propagierer-basierte Propagierungsmaschinen. Eine Propagierungsmaschine ist aufgeteilt in generische Propagierungsdienste und domänenspezifische Domänenlöser, die durch eine Schnittstelle zur Constraint-Programmierung miteinander verbunden sind. Propagierer benutzen Filter, um Constraints zu propagieren. Die Schnittstelle isoliert Filter von Propagierern, so dass Programmkodes von Filtern von verschiedenen Systemen genutzt werden können. Diese Dissertation präsentiert den Entwurf und die Implementierung eines Domänenlösers über endliche Mengen von ganzen Zahlen für Mozart, die über Mengen- und Kardinalitätsschranken approximiert werden. Dieser kooperiert mit einem Löser über endlichen Bereichen, um die Propagierung und die Ausdrucksfähigkeit zu verbessern. Diese Dissertation erhebt Constraints zu emanzipierten Datenstrukturen und stellt auf dieseWeise zusätzliche Steuerungsmöglichkeiten über Constraints zur Verfügung. Des Weiteren werden neuartige Programmiertechniken für emanzipierte Constraints entwickelt und demonstriert

    Global constraints as graph properties on structured network of elementary constraints of the same type

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    This report introduces a classification scheme for the global constraints. This classification is based on four basic ingredients from which one can generate almost all existing global constraints and come up with new interesting constraints. Global constraints are defined in a very concise way, in term of graph properties that have to hold, where the graph is a structured network of same elementary constraints. Since this classification is based on the internal structure of the global constraints it is also a strong hint for the pruning algorithms of the global constraints

    Proceedings of the Workshop on the Reuse of Web based Information

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    The proceedings are currently available online at: http://www-rocq.inria.fr/~vercoust/REUSE/WWW7-reuse.html where individual papers can be downloaded. However, this URL must not be regarded as permanent.These are the Proceeding of theWorkshop on the Reuse of Web Information that was held in conjunction with the Seventh International World Wide Web Conference, Brisbane, 14 April 19998

    Dynamically weakened constraints in bounded search for constraint optimisation problems

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    Combinatorial optimisation problems, where the goal is to an optimal solution from the set of solutions of a problem involving resources, constraints on how these resources can be used, and a ranking of solutions are of both theoretical and practical interest. Many real world problems (such as routing vehicles or planning timetables) can be modelled as constraint optimisation problems, and solved via a variety of solver technologies which rely on differing algorithms for search and inference. The starting point for the work presented in this thesis is two existing approaches to solving constraint optimisation problems: constraint programming and decision diagram branch and bound search. Constraint programming models problems using variables which have domains of values and valid value assignments to variables are restricted by constraints. Constraint programming is a mature approach to solving optimisation problems, and typically relies on backtracking search algorithms combined with constraint propagators (which infer from incomplete solutions which values can be removed from the domains of variables which are yet to be assigned a value). Decision diagram branch and bound search is a less mature approach which solves problems modelled as dynamic programming models using width restricted decision diagrams to provide bounds during search. The main contribution of this thesis is adapting decision diagram branch and bound to be the search scheme in a general purpose constraint solver. To achieve this we propose a method in which we introduce a new algorithm for each constraint that we wish to include in our solver and these new algorithms weaken individual constraints, so that they respect the problem relaxations introduced while using decision diagram branch and bound as the search algorithm in our solver. Constraints are weakened during search based on the problem relaxations imposed by the search algorithm: before search begins there is no way of telling which relaxations will be introduced. We attempt to provide weakening algorithms which require little to no changes to existing propagation algorithms. We provide weakening algorithms for a number of built-in constraints in the Flatzinc specifi- cation, as well as for global constraints and symmetry reduction constraints. We implement a solver in Go and empirically verify the competitiveness of our approach. We show that our solver can be parallelised using Goroutines and channels and that our approach scales well. Finally, we also provide an implementation of our approach in a solver which is tailored towards solving extremal graph problems. We use the forbidden subgraph problem to show that our approach of using decision diagram branch and bound as a search scheme in a constraint solver can be paired with canonical search. Canonical search is a technique for graph search which ensures that no two isomorphic graphs are returned during search. We pair our solver with the Nauty graph isomorphism algorithm to achieve this, and explore the relationship between branch and bound and canonical search

    Solving hard subgraph problems in parallel

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    This thesis improves the state of the art in exact, practical algorithms for finding subgraphs. We study maximum clique, subgraph isomorphism, and maximum common subgraph problems. These are widely applicable: within computing science, subgraph problems arise in document clustering, computer vision, the design of communication protocols, model checking, compiler code generation, malware detection, cryptography, and robotics; beyond, applications occur in biochemistry, electrical engineering, mathematics, law enforcement, fraud detection, fault diagnosis, manufacturing, and sociology. We therefore consider both the ``pure'' forms of these problems, and variants with labels and other domain-specific constraints. Although subgraph-finding should theoretically be hard, the constraint-based search algorithms we discuss can easily solve real-world instances involving graphs with thousands of vertices, and millions of edges. We therefore ask: is it possible to generate ``really hard'' instances for these problems, and if so, what can we learn? By extending research into combinatorial phase transition phenomena, we develop a better understanding of branching heuristics, as well as highlighting a serious flaw in the design of graph database systems. This thesis also demonstrates how to exploit two of the kinds of parallelism offered by current computer hardware. Bit parallelism allows us to carry out operations on whole sets of vertices in a single instruction---this is largely routine. Thread parallelism, to make use of the multiple cores offered by all modern processors, is more complex. We suggest three desirable performance characteristics that we would like when introducing thread parallelism: lack of risk (parallel cannot be exponentially slower than sequential), scalability (adding more processing cores cannot make runtimes worse), and reproducibility (the same instance on the same hardware will take roughly the same time every time it is run). We then detail the difficulties in guaranteeing these characteristics when using modern algorithmic techniques. Besides ensuring that parallelism cannot make things worse, we also increase the likelihood of it making things better. We compare randomised work stealing to new tailored strategies, and perform experiments to identify the factors contributing to good speedups. We show that whilst load balancing is difficult, the primary factor influencing the results is the interaction between branching heuristics and parallelism. By using parallelism to explicitly offset the commitment made to weak early branching choices, we obtain parallel subgraph solvers which are substantially and consistently better than the best sequential algorithms
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