Catálogo de publicaciones - libros

We present the design and implementation of a Minesweeper game, augmented with a digital assistant. The assistant uses constraint programming techniques to help the player, and is able to play the game by itself. It predicts safe moves, and gives probabilistic information when safe moves cannot be found.

The problem of reconfiguring power distribution systems to reduce power losses has been extensively studied because of its significant economic impact. A variety of approximation computational models have recently been proposed. We describe a constraint programming model for this problem, using the system. To handle real world reconfiguration systems we implemented and integrated into an efficient constraint propagation system for the real numbers. We show how the CP approach leads to a simpler model and allows more flexible control of reconfiguration parameters. We analyze the performance of our system in canonical distribution networks of up to 60 nodes. We describe how the adaptability of the search engine allows defining effective strategies for tackling a real distribution system reconfiguration of around 600 nodes.

Strasheela provides a means for the composer to create a symbolic score by formally describing it in a rule-based way. The environment defines a rich music representation for complex polyphonic scores. Strasheela enables the user to define expressive compositional rules and then to apply them to the score. Compositional rules can restrict many aspects of the music – including the rhythmic structure, the melodic structure and the harmonic structure – by constraining the parameters (e.g. duration or pitch) of musical events according to some numerical or logical relation. Strasheela combines this expressivity with efficient search strategies.

Strasheela is implemented in the Oz programming language. The Strasheela user writes an Oz program which applies the Strasheela music representation. The program searches for one or more solution scores which fulfil all compositional rules applied to the score.

In this paper we describe an application that solves the problem of aircraft sequencing in airports using a single runway. In this problem, the air traffic controller must compute a landing (take off) time for each plane in the or airport. The cost is associated with the difference between the plane preferred time (for landing or taking off) and the time assigned to it. There is also a minimum separation time between planes that must be respected to avoid accidents. We have implemented an application using with finite domain constraints, GUIs to interact with the user, and a propagator with a simple, but very helpful operation to cut domains. The basis of the application is the engine that implements the model of the problem; it is easily extensible through the implementation of new distributors. This paper shows how the powerful features of could be exploited to implement practical applications.

This article shows a practical solution to , a problem of combinatorial optimization. Apart from stating the problem formally and proposing a constraint model, the article describes the heuristics designed to find solutions. The application was developed using ; different distribution strategies were implemented based on the already mentioned heuristics. The experimental partial results turned out to be competitive for real problems (180 articles, 25 evaluators).

Here we introduce , a graphical tool for a time-tabling constraint engine (). Since the core of is text-based and provides little user-interaction, finding an appropriate solution for large problems (1000-2000 variables) can be a very time consuming process requiring the constant supervision of a constraint programming expert. uses an strategy. Such strategy subdivides the problem and checks the consistency of the resulting subdivisions before incrementally unifying them. This has shown to be useful in finding inconsistencies and discovering over-constrained situations. Our incremental solution is based on hierarchical groupings defined at the problem domain level. This allows users to direct the timetabling engine in finding partial solutions that are meaningful in practice. We discuss the lessons learned from using in real settings, as well as the experiences of coupling to the timetabling engine.