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  • 51.
    Schulte, Christian
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Tack, Guido
    Weakly Monotonic Propagators2009In: PRINCIPLES AND PRACTICE OF CONSTRAINT PROGRAMMING / [ed] Gent IP, 2009, Vol. 5732, p. 723-730Conference paper (Refereed)
    Abstract [en]

    Today's models for propagation-based constraint solvers require propagators as implementations of constraints to be at least contracting and monotonic. These models do not comply with reality: today's constraint, programming systems actually use non-monotonic propagators. This paper introduces the first realistic model of constraint propagation by assuming it propagator to be weakly monotonic (complying with the constraint it implements). Weak monotonicity is shown to be the minimal property that guarantees constraint propagation to be sound and complete. The important insight is that weak monotonicity makes propagation in combination. with search well behaved. A case study suggests that non-monotonicity call be seen as all opportunity for more efficient propagation.

  • 52. Scott, J. D.
    et al.
    Flener, P.
    Pearson, J.
    Schulte, Christian
    KTH, School of Electrical Engineering and Computer Science (EECS), Electronics.
    Design and implementation of bounded-length sequence variables2017In: 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, CPAIOR 2017, Springer, 2017, Vol. 10335, p. 51-67Conference paper (Refereed)
    Abstract [en]

    We present the design and implementation of bounded -length sequence (BLS) variables for a CP solver. The domain of a BLS variable is represented as the combination of a set of candidate lengths and a sequence of sets of candidate characters. We show how this representation, together with requirements imposed by propagators, affects the implementation of BLS variables for a copying CP solver, most importantly the closely related decisions of data structure, domain restriction operations, and propagation events. The resulting implementation outperforms traditional bounded-length string representations for CP solvers, which use a fixed-length array of candidate characters and a padding symbol.

  • 53. Tack, Guido
    et al.
    Schulte, Christian
    KTH, School of Information and Communication Technology (ICT), Electronic, Computer and Software Systems, ECS.
    Smolka, Gert
    Generating propagators for finite set constraints2006In: Principles And Practice Of Constraint Programming - CP 2006 / [ed] Benhamou, F, 2006, Vol. 4204, p. 575-589Conference paper (Refereed)
    Abstract [en]

    Ideally, programming propagators as implementations of constraints should be an entirely declarative specification process for a large class of constraints: a high-level declarative specification is automatically translated into an efficient propagator. This paper introduces the use of existential monadic second-order logic as declarative specification language for finite set propagators. The approach taken in the paper is to automatically derive projection propagators (involving a single variable only) implementing constraints described by formulas. By this, the paper transfers the ideas of indexicals to finite set constraints while considerably increasing the level of abstraction available with indexicals. The paper proves soundness and completeness of the derived propagators and presents a run-time analysis, including techniques for efficiently executing projectors for n-ary constraints.

  • 54. Van Roy, P.
    et al.
    Brand, P.
    Duchier, D.
    Haridi, Seif
    KTH, Superseded Departments, Microelectronics and Information Technology, IMIT.
    Schulte, Christian
    KTH, Superseded Departments, Microelectronics and Information Technology, IMIT.
    Henz, M.
    Logic programming in the context of multiparadigm programming: the Oz experience2003In: Theory and Practice of Logic Programming, ISSN 1471-0684, E-ISSN 1475-3081, Vol. 3, p. 717-763Article in journal (Refereed)
    Abstract [en]

    Oz is a multiparadigm language that supports logic programming as one of its major paradigms. A multiparadigm language is designed to support different programming paradigms (logic, functional, constraint, object-oriented, sequential, concurrent, etc.) with equal ease. This paper has two goals: to give a tutorial of logic programming in Oz; and to show how logic programming fits naturally into the wider context of multiparadigm programming. Our experience shows that there are two classes of problems, which we call algorithmic and search problems, for which logic programming can help formulate practical solutions. Algorithmic problems have known efficient algorithms. Search problems do not have known efficient algorithms but can be solved with search. The Oz support for logic programming targets these two problem classes specifically, using the concepts needed for each. This is in contrast to the Prolog approach, which targets both classes with one set of concepts, which results in less than optimal support for each class. We give examples that can be run interactively on the Mozart system, which implements Oz, To explain the essential difference between algorithmic and search programs, we define the Oz execution model. This model subsumes both concurrent logic programming (committed-choice-style) and search-based logic programming (Prolog-style). Furthermore, as consequences of its multiparadigm nature, the model supports new abilities such as first-class top levels, deep guards, active objects, and sophisticated control of the search process. Instead of Horn clause syntax, Oz has a simple, fully compositional, higher-order syntax that accommodates the abilities of the language. We give a brief history of Oz that traces the development of its main ideas and we summarize the lessons learned from this work. Finally, we give many entry points into the Oz literature.

  • 55.
    Younas, Irfan
    et al.
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Kamrani, Farzad
    Swedish Defence Research Agency, Stockholm, Sweden.
    Schulte, Christian
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Ayani, Rassul
    KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
    Efficient Genetic Algorithms for Optimal Assignment of Tasks to Teamsof AgentsManuscript (preprint) (Other academic)
    Abstract [en]

    The problem of optimally assigning agents (resources) to a given set oftasks is known as the Assignment Problem (AP). The classical AP and manyof its variations have been discussed extensively in the literature. In thispaper, we present a specific class of Assignment Problems (APs) in which eachtask is assigned to a group of collaborating agents. In this AP, collaborationof all agents is required to perform the task and an agent cannot individuallydo it.

    We present a mathematical model for this type of AP and use GeneticAlgorithm (GA) to solve the model, since there are no known polynomial timealgorithms for this class of APs. We show that for larger instances of the problem,the GA with one-point crossover operator cannot efficiently find nearoptimalsolutions. In general, the efficiency of the GA depends on the choiceof genetic operators (selection, crossover, mutation) and associated parameters.In order to design an efficient GA for finding near optimal assignment oftasks to collaborative teams, we focus on construction of crossover operators.We compare and analyze the efficiency of several well-known crossover operatorssuch as one-point, two-point, three-point, position-based and order-basedcrossover operators. We suggest modifications to these operators by addinga shuffled repair list to them and show that their efficiency is enhanced forsolving the presented AP. Furthermore, we introduce two new crossover operators,team-based and team-based shuffled list crossover operators, whichsolve large-scale models of our AP efficiently.

  • 56.
    Younas, Irfan
    et al.
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Kamrani, Farzad
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Schulte, Christian
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Ayani, Rassul
    KTH, School of Information and Communication Technology (ICT), Communication: Services and Infrastucture (Closed 20120101), Software and Computer Systems, SCS (Closed 20120101).
    Optimization of Task Assignment to Collaborating Agents2011In: IEEE SSCI 2011 - Symposium Series on Computational Intelligence - CISched 2011, Paris, France: IEEE Computational Intelligence Society , 2011, p. 17-24Conference paper (Refereed)
    Abstract [en]

    The classic task assignment problem (AP) assigns m agents to n tasks, where each task is assigned to exactly one agent. This problem and many of its variations, including the case where a task is assigned to a group of agents working independently, have been discussed extensively in the literature. We consider a specific class of task assignment problems where each task is assigned to a group of collaborating agents that work as a team. Thus, changing one of the group members may have a vital impact on the output of the group. We assume that each agent has a set of capabilities and each task has certain requirements. The objective is to assign agents to teams such that the gain is maximized.

    We suggest a Genetic Algorithm (GA) for finding a near optimal solution to this class of task assignment problems. To the best of our knowledge, this class of APs has not been considered in the literature, probably due to the difficulty of evaluating the performance of a team of agents. Recently, we have developed a formal method for measuring performance of a team which is used in this paper to formulate the objective function of our GA. We analyze the quality of the obtained solution by comparing the result of our GA with (a) the exact solution of some smaller problems, and (b) with the results of the exact solution of specific cases that can be obtained by the Hungarian algorithm. We provide experimental results on efficiency, stability, robustness and scalability of the solution obtained by our GA.

12 51 - 56 of 56
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