SUNNY-CP and the MiniZinc Challenge

In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the MiniZinc Challenge---the annual international competition for CP solvers---where it won two gold medals in 2015 and 2016. Under consideration in Theory and Practice of Logic Programming (TPLP)Comment: Under consideration in Theory and Practice of Logic Programming (TPLP

A Multicore Tool for Constraint Solving

*** To appear in IJCAI 2015 proceedings *** In Constraint Programming (CP), a portfolio solver uses a variety of different solvers for solving a given Constraint Satisfaction / Optimization Problem. In this paper we introduce sunny-cp2: the first parallel CP portfolio solver that enables a dynamic, cooperative, and simultaneous execution of its solvers in a multicore setting. It incorporates state-of-the-art solvers, providing also a usable and configurable framework. Empirical results are very promising. sunny-cp2 can even outperform the performance of the oracle solver which always selects the best solver of the portfolio for a given problem

A Regular Matching Constraint for String Variables

Using a regular language as a pattern for string matching is nowadays a common-and sometimes unsafe-operation, provided as a built-in feature by most programming languages. A proper constraint solver over string variables should support most of the operations over regular expressions and related constructs. However, state-of-the-art string solvers natively support only the membership relation of a string variable to a regular language. Here we take a step forward by defining a specialised propagator for the match operation, returning the leftmost position where a pattern can match a given string. Empirical evidences show the effectiveness of our approach, implemented within the constraint programming framework, and tested against state-of-the-art string solvers.</p

Riappropriarsi del tempo, per abitare lo spazio urbano: quali sfide educative?

The paper aims to investigate the characteristics of metropolitan life nowadays. The study outlines the educational need to rediscover the identifying function of the city, especially for younger generations, focusing in particular on the analysis of space and time categories. In a synchronic perspective, it aims to find an appropriate time to inhabit the city and to develop a sense of belonging.In a diachronic perspective, the goal is recovering the history and stories that live in the city, in a dialectical interplay between permanence and change, based on memory. The author envisages a process of education for rediscovering the stories preserved in the architectural structures and their meanings, in order to bridge the gap between urban space and time. In this way, the urban space can become a narrative place and a source of identity for the new generations.Il contributo intende indagare i tratti che assume l’esistenza metropolitana nell’epoca odierna.Soffermandosi in particolare sull’analisi delle categorie dello spazio e del tempo, si sottolinea l’esigenza educativa di riscoprire la funzione identitaria della città, in modo particolare per le nuove generazioni. In una prospettiva sincronica, si auspica la possibilità di trovare un tempo opportuno per abitare la città e sviluppare senso di appartenenza. In una prospettiva diacronica, si profila invece il recupero della storia e delle storie che abitano nella città, in un gioco dialettico tra persistenza e mutamento, sul filo della memoria. Per sanare la frattura fra spazi e tempi urbani si traccia un percorso formativo di riscoperta delle trame narrative custodite nelle strutture architettoniche e nella polisemia dei loro significati: in questo modo lo spazio urbano può diventare luogo del racconto e fonte di identità per le nuove generazioni

An Extensive Evaluation of Portfolio Approaches for Constraint Satisfaction Problems

In the context of Constraint Programming, a portfolio approach exploits the complementary strengths of a portfolio of different constraint solvers. The goal is to predict and run the best solver(s) of the portfolio for solving a new, unseen problem. In this work we reproduce, simulate, and evaluate the performance of different portfolio approaches on extensive benchmarks of Constraint Satisfaction Problems. Empirical results clearly show the benefits of portfolio solvers in terms of both solved instances and solving time

SUNNY: a Lazy Portfolio Approach for Constraint Solving

*** To appear in Theory and Practice of Logic Programming (TPLP) *** Within the context of constraint solving, a portfolio approach allows one to exploit the synergy between different solvers in order to create a globally better solver. In this paper we present SUNNY: a simple and flexible algorithm that takes advantage of a portfolio of constraint solvers in order to compute --- without learning an explicit model --- a schedule of them for solving a given Constraint Satisfaction Problem (CSP). Motivated by the performance reached by SUNNY vs. different simulations of other state of the art approaches, we developed sunny-csp, an effective portfolio solver that exploits the underlying SUNNY algorithm in order to solve a given CSP. Empirical tests conducted on exhaustive benchmarks of MiniZinc models show that the actual performance of SUNNY conforms to the predictions. This is encouraging both for improving the power of CSP portfolio solvers and for trying to export them to fields such as Answer Set Programming and Constraint Logic Programming

A Constraint-Based Model for Fast Post-Disaster Emergency Vehicle Routing

Disasters like terrorist attacks, earthquakes, hurricanes, and volcano eruptions are usually unpredictable events that affect a high number of people. We propose an approach that could be used as a decision support tool for a post-disaster response that allows the assignment of victims to hospitals and organizes their transportation via emergency vehicles. By exploiting the synergy between Mixed Integer Programming and Constraint Programming techniques, we are able to compute the routing of the vehicles so as to rescue much more victims than both heuristic based and complete approaches in a very reasonable time

sunny-as2: Enhancing SUNNY for Algorithm Selection

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY enables to schedule, from a portfolio of solvers, a subset of solvers to be run on a given CP problem. This approach has proved to be effective for CP problems, and its parallel version won many gold medals in the Open category of the MiniZinc Challenge -- the yearly international competition for CP solvers. In 2015, the ASlib benchmarks were released for comparing AS systems coming from disparate fields (e.g., ASP, QBF, and SAT) and SUNNY was extended to deal with generic AS problems. This led to the development of sunny-as2, an algorithm selector based on SUNNY for ASlib scenarios. A preliminary version of sunny-as2 was submitted to the Open Algorithm Selection Challenge (OASC) in 2017, where it turned out to be the best approach for the runtime minimization of decision problems. In this work, we present the technical advancements of sunny-as2, including: (i) wrapper-based feature selection; (ii) a training approach combining feature selection and neighbourhood size configuration; (iii) the application of nested cross-validation. We show how sunny-as2 performance varies depending on the considered AS scenarios, and we discuss its strengths and weaknesses. Finally, we also show how sunny-as2 improves on its preliminary version submitted to OASC

On the Evaluation of (Meta-)solver Approaches

Meta-solver approaches exploit many individual solvers to potentially build a better solver. To assess the performance of meta-solvers, one can adopt the metrics typically used for individual solvers (e.g., runtime or solution quality) or employ more specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual best performance). In this paper, based on some recently published works, we provide an overview of different performance metrics for evaluating (meta-)solvers by exposing their strengths and weaknesses