the proceedings contain 84 papers. the special focus in this conference is on principles and practice of constraintprogramming. the topics include: Hybrid benders decomposition algorithms in constraint logic programm...
ISBN:
(纸本)3540428631
the proceedings contain 84 papers. the special focus in this conference is on principles and practice of constraintprogramming. the topics include: Hybrid benders decomposition algorithms in constraint logic programming;a hybrid framework integrating mixed integer programming and constraint logic programming;towards stochastic constraintprogramming;global cut framework for removing symmetries;capturing structure with satisfiability;phase transitions and backbones of 3-sat and maximum 3-sat;solving non-binary CSPS using the hidden variable encoding;a filtering algorithm for the stretch constraint;network flow problems in constraintprogramming;pruning for the minimum constraint family and for the number of distinct values constraint family;components for state restoration in tree search;constraint-based verification of client-server protocols;lower bounds for non-binary constraint optimization problems;a general scheme for multiple lower bound computation in constraint optimization;solving disjunctive constraints for interactive graphical applications;non-overlapping constraints between convex polytopes;formal models of heavy-tailed behavior in combinatorial search;in search of a phase transition in the ac-matching problem;a CLP approach to the protein side-chain placement problem;solving constraints over floating-point numbers;optimal pruning in parametric differential equations;an efficient filtering technique combining domain partition and arc consistency;the expressive power of binary linear programming;the traveling tournament problem description and benchmarks;structural constraint-based modeling and reasoning with basic configuration cells;solving Boolean satisfiability using local search guided by unit clause elimination and fast optimal instruction scheduling for single-issue processors with arbitrary latencies.
this paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set programming (ASP). the framework, called Learning fro...
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this paper contributes to the area of inductive logic programming by presenting a new learning framework that allows the learning of weak constraints in Answer Set programming (ASP). the framework, called Learning from Ordered Answer Sets, generalises our previous work on learning ASP programs without weak constraints, by considering a new notion of examples as ordered pairs of partial answer sets that exemplify which answer sets of a learned hypothesis (together with a given background knowledge) are preferred to others. In this new learning task inductive solutions are searched within a hypothesis space of normal rules, choice rules, and hard and weak constraints. We propose a new algorithm, ILASP2, which is sound and complete with respect to our new learning framework. We investigate its applicability to learning preferences in an interview scheduling problem and also demonstrate that when restricted to the task of learning ASP programs without weak constraints, ILASP2 can be much more efficient than our previously proposed system.
While there are many AI algorithms designed for finding solutions in a constraint Satisfaction Problems, finding similar solutions of a CSP requires entirely new and different methods. the method we propose here is ba...
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We introduce a new global constraint for modeling and solving network flow problems in constraintprogramming. We describe the declarative and operational semantics of the flow constraint and illustrate its use throug...
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this paper presents some lessons that can be drawn, from the point of view of the constraint reasoning and constraintprogramming community, from trying to model and to solve as best as possible the mission management...
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We present a constraint-based model and planning engine for manufacturing process planning. By exploiting the expressive power of constraintprogramming (cp), all relevant, sometimes conflicting pieces of domain knowl...
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In constraint Optimization Problems (COP) the objective function induces a total order on the solution set. However, in many real-life applications, more functions, possibly conflicting, should be optimized at the sam...
ISBN:
(纸本)3540428631
In constraint Optimization Problems (COP) the objective function induces a total order on the solution set. However, in many real-life applications, more functions, possibly conflicting, should be optimized at the same time, and solutions are ranked by means of a Partial Order.
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