Constraint Programming the Constraint Programming Working Group Acm-mit Sdcr Workshop

machines for CP languages have recently become available, and initial performance results are encouraging [40]. 4 Promising Directions Constraint programming has by now shown that constraints can be used not only to represent knowledge but also as a way to guide search, prune useless branches, lter queries, and describe process communication and synchronization. With this is mind, we may identify several directions for research that are promising for systems, programming environments, models and application packages. More realistic constraint systems and languages. We need to develop more automatic and systematic ways to acquire and model domain-speci c and problem-speci c knowledge, developing a richer paradigm to cope with the properties and uncertainties of real-world information. Of course, representation and reasoning are always two sides of the same coin. As we consider new classes of constraints, we must also consider new methods to compute with them; automating the modeling process will itself require capturing some very sophisticated reasoning skills. Moreover, better theoretical and empirical understanding is needed of the relationship between real-world problem parameters and search methods. An important issue is that of overconstrained constraint problems [71], since most real-life problems are indeed over-constrained. Thus either the constraint domain, or the language itself, should be exible enough to be able to deal with such situations and solve them in some satisfactory way. For example, the constraints and constraint solving algorithms could take into account the presence of preferences of some sort [5,10,47], and/or the language could allow for user-guided constraint retraction [23,4] and intelligible explanations for failure. This of course would bring the constraint satisfaction and programming tasks closer to the issues present in optimization problems, since in the presence of preferences one has to decide the best way to choose and/or retract constraints. Thus special attention has to be paid to the interrelation between AI and OR techniques for such tasks. In particular, we must take advantage of the coexistence, in the constraint satisfaction world, of di erent methods (e.g. systematic and stochastic search) and di erent disciplines (e.g. arti cial intelligence and operations research). E cient modeling. Constraint satisfaction knowledge can be represented very declaratively, without regard to how it is to be used. However, modeling a speci c problem is not a trivial task, especially since how it is modeled can dramatically a ect how well our algorithms perform. We need to automate the process of moving from problem descriptions natural to the problem domain to problem descriptions designed for e cient solution. A variety of problem-solving techniques are now available to us, but synthesizing appropriate algorithms for speci c tasks should be automated [92]. In addition, robust constraint computation must cope with change in the world and in models, and with noise (e.g., in data), and uncertainity (e.g., in parameter values). Towards constraint-based distributed systems. Another challenge for constraint programming systems is related to the role of such systems in network-wide programming. This type of programming is likely to be of growing importance given the fact that the recent wider di usion of the Internet and the popularity of the \World Wide Web" (WWW) protocols are e ectively providing a new platform that is standard and ubiquitous, and allows a new class of 18 highly sophisticated distributed applications. Features of constraints like the ability of describ-ing intraand inter-process communication and synchronization are more and more importantin practical applications which consist of distributed environments where both local problemsolving and global synchronization and coordination is needed. This is added to the fact thatmany CP systems already o er many other characteristics that make them well suited in thiscontext. These include dynamic memory management, well behaved structure and pointer ma-nipulation, robustness, dynamic compilation to architecture independent bytecode, dynamicdatabases, search facilities, grammars, code motion, and sophisticated meta-programming. Anumber of distributed concurrent constraint systems are currently being worked on, applicationdevelopment libraries are being o ered, and network and WWW applications are being reported[119]. It appears that CP is a promising foundation for most aspects of the next generation ofdistributed systems, where all the advantages of constraints may coexist and thus lead to simple,elegant and practically usable environments.Another interesting related application domain is 3D graphics and Virtual Reality. Manyinteractions between objects (e.g. attachments, minimal distances, non-collision, etc) or gen-eral integrity rules (such as energy conservation laws) can be considered as constraints, andimplemented e ciently as such. This generalizes in an obvious way 2D geometrical constraints.Basically, constraints can be used to enforce hidden relations between objects and thus makesure that the simulated virtual world does not depart too much from our real one.Towards faster, more e cient systems. While the performance and computing resourceeconomy of current CP systems has proved to be adequate in signi cant industrial applications,competing very favorably with other techniques and approaches, it appears that there still re-main many avenues for improvement, which would make the technology even more competitive.It is expected that improving execution speed and reducing further resource consumption canimprove the acceptance of the approach for general purpose programming as well as encourag-ing the inclusion of constraint programming techniques, constructs, and libraries in conventionallanguages. Interesting techniques to be further explored include advanced compilation based onglobal analysis and (automatic) program and solver parallelization. In fact, parallelization isbecoming more and more interesting since multiprocessing hardware is starting to be in manycases the default installation platform (for example, for departmental servers where multiproces-sors using fast, inexpensive, o -the-shelf processors are often replacing mainframes at a fractionof their cost). Also, multiprocessor workstations are not unusual any more. It appears likelythat this trend towards increased use of parallelism will continue as multiprocessor architecturesare better understood, interconnection network performance increases with new technologies(specially if the promise of optical interconnect is nally delivered), and feature size diminishesallowing placement of several processors on the same chip.Constraint databases. Many challenges in constraint databases are yet to be addressed.Speci c directions of work include: constraint modeling, canonical forms and algebras; datamodels and query languages; indexing and approximation-based ltering; constraint algebraalgorithms and global optimization; systems and case studies. In addition robust widely availableimplementations of these ideas need to be developed.User interfaces. In user interface applications, there is a constant need for new constraint sat-isfaction algorithms that can handle a wider range of constraints that arise in such applications,and algorithms and data structures with improved space and time e ciency.19 The development of better (performance) debugging techniques and more useful visualiza-tion paradigms for several constraint domains and solving algorithms also o ers an interestingresearch direction. Currently, at least one European project has started working on the devel-opment of both assertion-based and visualization based debugging techniques for CLP systems.Among the issues that should be addressed are ways of describing the desired constraints ata higher level of abstraction (closer to the domain of interest); studying the models users haveof constraint systems, and as needed evolving those systems to allow for clearer and more easilyunderstood user models.Acknowledgments. We gratefully acknowledge very valuable and timely comments fromThomas Fruhwirth, John Hooker, Michael Maher, Catuscia Palamidessi, Peter Revesz, MarkWallace, Peter van Beek and Roland Yap. Particular thanks to Vineet Gupta for invaluablelast-minute assistance.References[1] F. Afrati, S. Cosmadakis, S. Grumbach, and G. Kuper. Linear versus polynomial con-straints in database query languages. In A. 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