159 research outputs found

    Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

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    Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques

    An Initial Approach to Explaining SLA Inconsistencies

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    An SLA signed by all interested parties must be created carefully, avoiding contradictions between terms, because their terms could carry penalties in case of failure. However, this consistency checking may become a challenging task depending on the complexity of the agreement. As a consequence, an automated way of checking the consistency of an SLA document and returning the set of inconsistent terms of the agreement would be very appealing from a practical point of view. For instance, it enables the development of software tools that make the creation of correct SLAs and the consistency checking of imported SLAs easier for users. In this paper, we present the problem of explaining WSAgreement inconsistencies as a constraint satisfaction problem (CSP), and then we use a CSP solver together with an explanation engine to check the consistency and return the inconsistent terms. Furthermore, a proof-of-concept using Choco solver in conjunction with the Palm explanation engine has been developed

    Managing Complex Scheduling Problems with Dynamic and Hybrid Constraints.

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    The task of scheduling can often be a difficult one because of the inherent complexity of real-world problems. In the field of Artificial Intelligence, many representations and algorithms have been developed to automate the scheduling process. Many state of the art scheduling systems deal with this complexity by making assumptions that simplify the algorithms, but in doing so, miss some opportunities to improve performance. Scheduling problems are temporal in nature, and so they often contain constraints that change over time. Many scheduling systems assume that the problems they are solving are all independent, and so they ignore the similarities between subsequent sets of scheduling constraints. Additionally, scheduling problems often contain a mixture of finite-domain and temporal constraints. Many of the systems that can solve problems of this type do so by creating finite-domain variables to represent the constraints, but then ignore the distinction between the different types of variables when searching for a solution. In this dissertation, I identify opportunities to improve performance by exploiting structure where it has previously been overlooked. Following this approach, I develop a set of techniques that apply to a wide variety of situations that can arise in real-world scheduling problems. First, I consider dynamic scheduling problems with constraints that change over time. To address such problems, I introduce a new representation called the Dynamic Disjunctive Temporal Problem, along with several techniques to improve both efficiency and stability when solving one. Second, I consider scheduling problems in which a mixture of finite-domain and temporal variables can interact through hybrid constraints. I introduce the Hybrid Scheduling Problem to represent such problems, and I present a set of techniques that capitalize on the distinction between variable types to improve efficiency across the problem space. Finally, I conclude by proposing several ways that the dynamic and hybrid representations and techniques can be combined. To compare many of the techniques presented throughout this dissertation in the context of structured, real-world problems, I use them to solve scheduling problems based on actual air traffic control constraints recorded from the Dallas/Fort Worth International Airport.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57625/2/pschwart_1.pd

    A Collection of Constraint Programming Models for the Three-Dimensional Stable Matching Problem with Cyclic Preferences

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    We introduce five constraint models for the 3-dimensional stable matching problem with cyclic preferences and study their relative performances under diverse configurations. While several constraint models have been proposed for variants of the two-dimensional stable matching problem, we are the first to present constraint models for a higher number of dimensions. We show for all five models how to capture two different stability notions, namely weak and strong stability. Additionally, we translate some well-known fairness notions (i.e. sex-equal, minimum regret, egalitarian) into 3-dimensional matchings, and present how to capture them in each model. Our tests cover dozens of problem sizes and four different instance generation methods. We explore two levels of commitment in our models: one where we have an individual variable for each agent (individual commitment), and another one where the determination of a variable involves pairing the three agents at once (group commitment). Our experiments show that the suitability of the commitment depends on the type of stability we are dealing with. Our experiments not only led us to discover dependencies between the type of stability and the instance generation method, but also brought light to the role that learning and restarts can play in solving this kind of problems

    The Crescent Student Newspaper, April 16, 1997

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    Student Newspaper of George Fox University.https://digitalcommons.georgefox.edu/the_crescent/2176/thumbnail.jp
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