rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Combinatorial Search: From Algorithms to Systems
  name:
    name part: Hamadi, Youssef.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2013
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 139 p. 30 illus., 16 illus. in color. online resource.
  abstract: Although they are believed to be unsolvable in general, tractability results suggest that some practical NP-hard problems can be efficiently solved. Combinatorial search algorithms are designed to efficiently explore the usually large solution space of these instances by reducing the search space to feasible regions and using heuristics to efficiently explore these regions. Various mathematical formalisms may be used to express and tackle combinatorial problems, among them the constraint satisfaction problem (CSP) and the propositional satisfiability problem (SAT). These algorithms, or constraint solvers, apply search space reduction through inference techniques, use activity-based heuristics to guide exploration, diversify the searches through frequent restarts, and often learn from their mistakes. In this book the author focuses on knowledge sharing in combinatorial search, the capacity to generate and exploit meaningful information, such as redundant constraints, heuristic hints, and performance measures, during search, which can dramatically improve the performance of a constraint solver. Information can be shared between multiple constraint solvers simultaneously working on the same instance, or information can help achieve good performance while solving a large set of related instances. In the first case, information sharing has to be performed at the expense of the underlying search effort, since a solver has to stop its main effort to prepare and commu nicate the information to other solvers; on the other hand, not sharing information can incur a cost for the whole system, with solvers potentially exploring unfeasible spaces discovered by other solvers. In the second case, sharing performance measures can be done with little overhead, and the goal is to be able to tune a constraint solver in relation to the characteristics of a new instance – this corresponds to the selection of the most suitable algorithm for solving a given instance. The book is suitable f…
  table of contents: Chap. 1 - Introduction -- Chap. 2 - Boosting Distributed Constraint Networks -- Chap. 3 - Parallel Tree Search for Satisfiability -- Chap. 4 - Parallel Local Search for Satisfiability -- Chap. 5 - Learning Variables Dependencies -- Chap. 6 - Continuous Search -- Chap. 7 - Autonomous Search -- Chap. 8 - Conclusion and Perspectives.
  note: by Youssef Hamadi.
  subject:
    topic: Computer science
  subject:
    topic: Computers
  subject:
    topic: Computer science
    topic: Mathematics
  subject:
    topic: Artificial intelligence
  subject:
    topic: Mathematical optimization
  subject:
    topic: Computational intelligence
  subject:
    topic: Computer Science
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Computational Intelligence
  subject:
    topic: Theory of Computation
  subject:
    topic: Optimization
  subject:
    topic: Discrete Mathematics in Computer Science
  classification: Q334-342
  classification: TJ210.2-211.495
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783642414824
  identifier: http://dx.doi.org/10.1007/978-3-642-41482-4
  record info:
    record creation date: 131029
    record change date: 20151204143458.0
    record identifier: 978-3-642-41482-4