Author name cluster

Jacopo Mauro

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

2 author rows

JAIR Journal 2023 Journal Article

On the Evaluation of (Meta-)solver Approaches

Roberto Amadini
Maurizio Gabbrielli
Tong Liu
Jacopo Mauro

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.

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IJCAI Conference 2022 Conference Paper

sunny-as2: Enhancing SUNNY for Algorithm Selection (Extended Abstract)

Tong Liu
Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

SUNNY is a k-nearest neighbors based Algorithm Selection (AS) approach that schedules and runs a number of solvers for a given unforeseen problem. In this work we present sunny-as2, an enhancement of SUNNY for generic AS scenarios that advances the original approach with wrapper-based feature selection, neighborhood-size configuration and a greedy approach to speed-up the training phase. Empirical evidence shows that sunny-as2 is competitive w. r. t. state-of-the-art AS approaches.

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JAIR Journal 2021 Journal Article

sunny-as2: Enhancing SUNNY for Algorithm Selection

Tong Liu
Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

SUNNY is an Algorithm Selection (AS) technique originally tailored for Constraint Programming (CP). SUNNY is based on the k-nearest neighbors algorithm and enables one 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. 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-as, a prototypical algorithm selector based on SUNNY for ASlib scenarios. A major improvement of sunny-as, called sunny-as2, was then 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, by detailing through several empirical evaluations and by providing new insights. Its current version, built on the top of the preliminary version submitted to OASC, is able to outperform sunny-as and other state-of-the-art AS methods, including those who did not attend the challenge.

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IJCAI Conference 2015 Conference Paper

A Multicore Tool for Constraint Solving

Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

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.

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LOPSTR Conference 2015 Conference Paper

Why CP Portfolio Solvers Are (under)Utilized? Issues and Challenges

Roberto Amadini
Maurizio Gabbrielli
Jacopo Mauro

Abstract It is well recognized that a single, arbitrarily efficient solver can be significantly outperformed by a portfolio solver exploiting a combination of possibly slower on-average different solvers. Despite the success of portfolio solvers within the context of solving competitions, they are rarely used in practice. In this paper we give an overview of the main limitations that hinder the practical adoption and development of portfolio solvers within the Constraint Programming (CP) paradigm, discussing also possible ways to overcome them and potential extensions outside the CP field.

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I&C Journal 2014 Journal Article

Aeolus: A component model for the cloud

Roberto Di Cosmo
Jacopo Mauro
Stefano Zacchiroli
Gianluigi Zavattaro

We introduce the Aeolus component model, which is specifically designed to capture realistic scenarios arising when configuring and deploying distributed applications in the so-called cloud environments, where interconnected components can be deployed on clusters of heterogeneous virtual machines, which can be in turn created, destroyed, and connected on-the-fly. The full Aeolus model is able to describe several component characteristics such as dependencies, conflicts, non-functional requirements (replication requests and load limits), as well as the fact that component interfaces to the world might vary depending on the internal component state. When the number of components needed to build an application grows, it becomes important to be able to automate activities such as deployment and reconfiguration. This corresponds, at the level of the model, to the ability to decide whether a desired target system configuration is reachable, which we call the achievability problem, and producing a path to reach it. In this work we show that the achievability problem is undecidable for the full Aeolus model, a strong limiting result for automated configuration in the cloud. We also show that the problem becomes decidable, but Ackermann-hard, as soon as one drops non-functional requirements. Finally, we provide a polynomial time algorithm for the further restriction of the model where support for inter-component conflicts is also removed.

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I&C Journal 2013 Journal Article

The expressive power of CHR with priorities

Maurizio Gabbrielli
Jacopo Mauro
Maria Chiara Meo

Constraint Handling Rules (CHR) is a committed-choice declarative language which has been originally designed for writing constraint solvers and which is nowadays a general purpose language. Recently the language has been extended by introducing user-definable (static or dynamic) rule priorities. The resulting language allows a better control over execution while retaining a declarative and flexible style of programming. In this paper we study the expressive power of this language. We first show that, in the presence of priorities, differently from the case of standard CHR, considering more than two atoms in the heads of rules does not augment the expressive power of the language. Next we show that also dynamic priorities do not augment the expressive power w. r. t. static priorities. These results are proved by providing explicitly a translation of one language into another one, which preserves a reference semantics. Finally we show that CHR with priorities is strictly more expressive than standard CHR (under the theoretical operational semantics). This result is obtained by adapting to the CHR case a notion of language encoding which allows to compare Turing powerful languages.

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