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.

Constraint 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/