109 research outputs found
Proceedings of the CP 2006 Doctoral Programme. The Doctoral Programme of the Twelfth International Conference on Principles and Practice of Constraint Programming, CP 2006, Nantes, France, September 24–29, 2006.
No full textThe Doctoral Programme of the Twelfth International Conference on Principles and Practice of Constraint Programming, CP 2006, Nantes, France, September 24–29, 2006.
No full textCP 2006 invites Ph.D. students to apply for the Doctoral Program, a forum held during the CP conference which provides an opportunity for a group of Ph.D. students to achieve visibility and discuss their research interests and career objectives with each other and established researchers in Constraint Programming and its related fields. After successful Doctoral Programmes in previous years, it is being run again this year for the sixth time.
The aims of the Doctoral Programme are the following:
* to provide a forum for Ph.D. students to present their current research, and receive feedback from other students and senior researchers;
* to promote contacts among Ph.D. students and senior researchers working in the same area;
* to exchange research experience;
* to support Ph.D. students with information and advice on academic, research and industrial careers;
* and to financially support its participants.
The program will consist of students' presentations and/or posters, and tutorials given by senior researchers in the field. In addition, each student will be matched to a mentor who is a senior researcher with similar research interests and who can advise the student on his/her research progress
Hybrid Modelling for Robust Solving
No full textWe study a balanced academic curriculum problem and an industrial steel mill slab design problem. These problems can be modelled in different ways, using both Integer Linear Programming (ILP) and Constraint Programming (CP) techniques. We consider the utility of each model. We also propose integrating the models to create hybrids that benefit from the complementary strengths of each model. Experimental results show that hybridization significantly increases the domain pruning and decreases the run-time on many instances. Furthermore, a CP/ILP hybrid model gives a more robust performance in the face of varying instance data
Slide: A Useful Special Case of the Cardpath Constraint
No full textWe study the cardpath constraint. This ensures a given constraint
holds a number of times down a sequence of variables. We show that slide, a special case of cardpath where the slid constraint must hold always, can be used to encode a wide range of sliding sequence constraints including cardpath itself. We consider how to propagate slide and provide a complete propagator for cardpath . Since propagation is NP-hard in general, we identify special cases where propagation takes polynomial time. Our experiments demonstrate that using slide to encode global constraints can be as efficient and effective as specialised propagators
The Fifth Workshop on Modelling and Solving Problems with Constraints. Held at the Nineteenth International Joint Conference on Artificial Intelligence ( IJCAI 2005 ), Edindurgh, Scotland, 31 July, 2005.
No full textConstraint Programming (CP) is a powerful technology to solve combinatorial problems which are ubiquitous in academia and industry. The last ten years have witnessed significant research devoted to modelling and solving problems with constraints. CP is now a mature field and has been successfully used for tackling a wide range of real-life complex applications.
As constraint solving is intractable in general, problems can become difficult to solve as their size increase. Therefore, there is always a need for more efficient solvers to cope with ever difficult problems. Techniques such as the design of specialised filtering algorithms for recurring constraints, sophisticated search techniques, heuristics to guide the search, symmetry breaking have significant impact on the time spent to solve problems. Efficiency can be improved also by bridging the gap between CP and the other communities such as Operations Research, Local Search, SAT, Planning, and Machine Learning.
Formulating an effective model for a given problem often requires trying alternate models and using ``modelling tricks'' such as redundant modelling and channelling. This could be a challenge even for modelling experts. The increasing use of CP necessitates higher level modelling languages to facilitate the exploitation of the available technology and to make CP reachable to a wider user base. The hope is that the next generation modelling languages will assist modellers by for instance helping acquire and validate constraints, automatically generating alternate models and selecting the most appropriate one for the application in hand, and synthesising propagators for complex constraints.
It is desirable to extend the classical framework for modelling and solving with constraints to adapt to some real-life scenarios. For instance, many problems contain uncertainty and thus the user may require robust solutions. In some cases, problems are over-constrained and the user has preferences for which constraints to relax. Explanations can be necessary to understand the solution process. Real-life problems are often optimisation problems and the users might want to improve the quality of their solutions as quickly as possible.
The rapidly growing use of CP in industrial applications makes it crucial to fill the gap between the user's needs and the answers provided by the technology. Developing more efficient ways to solve constraints, assisting the users in the modelling phase, and extending the classical modelling and solving framework to capture real-life scenarios are important steps towards a better applicability of CP technology to real-life problems. This one-day workshop will address modelling and solving jointly, looking for ways to enrich the efficiency, usability and the expressiveness of the CP tools. It will interest both academics in the AI community working on constraint reasoning, and people in industry using CP technology to solve problems.
Workshop topics include (but are not limited to):
* filtering algorithms
* synthesising propagators
* symmetry and constraints
* search algorithms and heuristics
* local and hybrid search
* modelling
* constraint acquisition and validation
* model generation and selection
* preferences
* optimization and over-constrained problems
* uncertainty and robustness
* explanations
* real-life applications
This workshop is the fifth in the series, following the successful earlier workshops held alongside ECAI 2000, IJCAI 2001, ECAI 2002, and ECAI 2004. There have also been related workshops at CP 2001/2002/2003/2004, IJCAI 1999/2003 and ECAI 1998.
URL: http://homes.ieu.edu.tr/~bhnich/ijcai05ws
Filtering algorithms for the multiset ordering constraint
No full textAbstractConstraint programming (CP) has been used with great success to tackle a wide variety of constraint satisfaction problems which are computationally intractable in general. Global constraints are one of the important factors behind the success of CP. In this paper, we study a new global constraint, the multiset ordering constraint, which is shown to be useful in symmetry breaking and searching for leximin optimal solutions in CP. We propose efficient and effective filtering algorithms for propagating this global constraint. We show that the algorithms maintain generalised arc-consistency and we discuss possible extensions. We also consider alternative propagation methods based on existing constraints in CP toolkits. Our experimental results on a number of benchmark problems demonstrate that propagating the multiset ordering constraint via a dedicated algorithm can be very beneficial
Combining Symmetry Breaking with Other Constraints: lexicographic ordering with sums
No full textWe introduce a new global constraint which combines together the lexicographic ordering constraint with two sum constraints
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