XCSP3-core: A Format for Representing Constraint Satisfaction/Optimization Problems

In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us to represent constraint satisfaction/optimization problems. The interest of XCSP3-core is multiple: (i) focusing on the most popular frameworks (CSP and COP) and constraints, (ii) facilitating the parsing process by means of dedicated XCSP3-core parsers written in Java and C++ (using callback functions), (iii) and defining a core format for comparisons (competitions) of constraint solvers.Comment: arXiv admin note: substantial text overlap with arXiv:1611.0339

The Dungeon Variations Problem Using Constraint Programming

The video games industry generates billions of dollars in sales every year. Video games can offer increasingly complex gaming experiences, with gigantic (but consistent) open worlds, thanks to larger and larger teams of developers and artists. In this paper, we propose a constraint-based approach for procedural dungeon generation in an open world/universe context, in order to provide players with consistent, open worlds with an excellent quality of storytelling. Thanks to a global description capturing all the possible rooms and situations of a given dungeon, our approach allows enumerating variations of this global pattern, which can then be presented to the player for more diversity. We formalise this problem in constraint programming by exploiting a graph abstraction of the dungeon pattern structure. Every path of the graph represents a possible variation matching a given set of constraints. We introduce a new propagator extending the "connected" graph constraint, which allows considering directed graphs with cycles. We show that thanks to this model and the proposed new propagator, it is possible to handle scenarios at the forefront of the game industry (AAA+ games). We demonstrate that our approach outperforms non-specialised solutions consisting of filtering only the relevant solutions a posteriori. We then conclude and offer several exciting perspectives raised by this approach to the Dungeon Variations Problem

Models and methods for the modelling of forest managing

A gestão florestal é uma actividade de grande valor económico e importância ecológica. As áreas florestais geridas podem abranger regiões muito grandes e a sua gestão adequada é muito importante para um desenvolvimento eficaz, tanto em termos de planeamento económico como de recursos naturais, e gerir uma floresta implica tipicamente a aplicação de escolhas políticas em diferentes parcelas de terra, aqui referidas como Stands ou Management Units (Unidades de Gestão). Este documento analisa vários métodos de gestão florestal, juntamente com as suas variações disponíveis na literatura ao longo de 4 capítulos, sendo esses métodos o Unit Restriction Model and Area Restriction Model por Alan T. Murray, the Area Restriction Model with Stand-Clear-Cut variables por Constantino et al, the Path Algorithm and the Generalized Management Unit formulation por McDill et al and the Full Adjacent Unit formulation por Gharbi et al. Os resultados apresentados nos artigos originais são discutidos nas conclusões de cada capítulo. Os 2 últimos capítulos apresentam uma formulação de Constraint Programming do problema e a sua implementação utilizando a biblioteca Choco da linguagem de programação Java e apresentam também os resultados, um capítulo relacionado com a primeira implementação que trata apenas da optimização do Madeira Total Obtida e o outro alargando o problema, juntamente com um novo conjunto de dados, para lidar com a optimização multicritério. Para o fazer, os princípios de Constraint Programming são primeiro enumerados juntamente com uma breve história da tecnologia de Constraint Programming. Finalmente, outros possíveis desenvolvimentos são discutidos numa secção de Trabalho Futuro; Abstract: Forest management is an activity of prime economic and ecological importance. Managed forest areas can span very large regions and their proper management is paramount to an effective development, in terms both of economic and natural resources planning and managing a forest typically implies applying policy choices to different patches of land, here referred to as Stands or Management Units. This paper reviews several methods of forest management alongside their variations available in the literature throughout 4 chapters, those methods being the Unit Restriction Model and Area Restriction Model by Alan T. Murray, the Area Restriction Model with Stand-Clear-Cut variables by Constantino et al, the Path Algorithm and the Generalized Management Unit formulation by McDill et al and the Full Adjacent Unit formulation by Gharbi et al. The results as presented in the original papers are discussed in the conclusions of each chapter. The final 2 chapters present a Constraint Programming formulation of the problem and its implementation using the Choco framework of the Java programming language and showcases the results, one chapter relating to the first implementation that deals only with the optimization of total Wood Yield and the other broadening the problem, alongside a new dataset, to deal with multi-criteria optimization. In order to do this the principles of Constraint Programming are first enumerated along with a short history of Constraint Programming technology

Metaheuristics “In the Large”

Many people have generously given their time to the various activities of the MitL initiative. Particular gratitude is due to Adam Barwell, John A. Clark, Patrick De Causmaecker, Emma Hart, Zoltan A. Kocsis, Ben Kovitz, Krzysztof Krawiec, John McCall, Nelishia Pillay, Kevin Sim, Jim Smith, Thomas Stutzle, Eric Taillard and Stefan Wagner. J. Swan acknowledges the support of UK EPSRC grant EP/J017515/1 and the EU H2020 SAFIRE Factories project. P. GarciaSanchez and J. J. Merelo acknowledges the support of TIN201785727-C4-2-P by the Spanish Ministry of Economy and Competitiveness. M. Wagner acknowledges the support of the Australian Research Council grants DE160100850 and DP200102364.Following decades of sustained improvement, metaheuristics are one of the great success stories of opti- mization research. However, in order for research in metaheuristics to avoid fragmentation and a lack of reproducibility, there is a pressing need for stronger scientific and computational infrastructure to sup- port the development, analysis and comparison of new approaches. To this end, we present the vision and progress of the Metaheuristics “In the Large”project. The conceptual underpinnings of the project are: truly extensible algorithm templates that support reuse without modification, white box problem descriptions that provide generic support for the injection of domain specific knowledge, and remotely accessible frameworks, components and problems that will enhance reproducibility and accelerate the field’s progress. We argue that, via such principled choice of infrastructure support, the field can pur- sue a higher level of scientific enquiry. We describe our vision and report on progress, showing how the adoption of common protocols for all metaheuristics can help liberate the potential of the field, easing the exploration of the design space of metaheuristics.UK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) EP/J017515/1EU H2020 SAFIRE Factories projectSpanish Ministry of Economy and Competitiveness TIN201785727-C4-2-PAustralian Research Council DE160100850 DP20010236

Automated Reasoning

This volume, LNAI 13385, constitutes the refereed proceedings of the 11th International Joint Conference on Automated Reasoning, IJCAR 2022, held in Haifa, Israel, in August 2022. The 32 full research papers and 9 short papers presented together with two invited talks were carefully reviewed and selected from 85 submissions. The papers focus on the following topics: Satisfiability, SMT Solving,Arithmetic; Calculi and Orderings; Knowledge Representation and Jutsification; Choices, Invariance, Substitutions and Formalization; Modal Logics; Proofs System and Proofs Search; Evolution, Termination and Decision Prolems. This is an open access book

XCSP3 and its ecosystem

International audienceIn this paper, we present a summary of XCSP3, together with its ecosystem. XCSP3 is a format used to build integrated representations of combinatorial constrained problems. Interestingly, XCSP3 preserves the structure of models, by handling arrays of variables and groups/blocks of constraints, which makes it rather unique in the literature. Furthermore, the ecosystem of XCSP3 is well supplied: it includes companion tools (parsers and checkers), a website with a search engine for selecting and downloading instances, and competitions of solvers. The Java-based modeling API, called JvCSP3, is the last developed piece of this complete production chain