The External Interface for Extending WASP

Dodaro, Carmine; Ricca, Francesco

doi:10.1017/s1471068418000558

Cited by 14 publications

(12 citation statements)

References 50 publications

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“…Furthermore, it offers the integration of computable functions, similar to the mechanism (using terms preceded by '@') sketched at the beginning of Section 4. Unlike this, a full-fledged Python API is offered by the ASP solver wasp (Dodaro and Ricca 2020). This has interesting applications to heuristic control and propagators for integrity constraints (Dodaro et al .…”

Section: Discussionmentioning

confidence: 99%

How to Build Your Own ASP-based System?!

Kaminski¹,

Romero²,

Schaub³

et al. 2021

Theory and Practice of Logic Programming

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Answer Set Programming, or ASP for short, has become a popular and sophisticated approach to declarative problem solving. Its popularity is due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. However, in contrast to ASP’s ease of use, the high degree of sophistication of the underlying technology makes it even hard for ASP experts to put ideas into practice whenever this involves modifying ASP’s machinery. For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system clingo can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how metaprogramming can be used for extending ASP. This is a rather light approach that relies on clingo’s reification feature to use ASP itself for expressing new functionalities. The second part of this tutorial uses traditional programming (in Python) for manipulating clingo via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is clingo’s new Application class that allows us to draw on clingo’s infrastructure by customizing processes similar to the one in clingo. For instance, we may apply manipulations to programs’ abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. A cross-sectional structure, spanning meta as well as application programming, is clingo’s intermediate format, aspif, that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several nontrivial case studies. In particular, we show how clingo can be extended by difference constraints and how guess-and-check programming can be implemented with both meta and application programming.

show abstract

Section: Discussionmentioning

confidence: 99%

How to Build Your Own ASP-based System?!

Kaminski¹,

Romero²,

Schaub³

et al. 2021

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

show abstract

“…Furthermore, it offers the integration of computable function, similar to the mechanism (using terms preceded by '@') sketched at the beginning of Section 4. Unlike this, a full-fledged Python API is offered by the ASP solver wasp (Dodaro and Ricca 2020). Interesting applications of it to heuristic control and propagators for integrity constraints are described in (Dodaro et al 2016) and (Cuteri et al 2020), respectively.…”

Section: Discussionmentioning

confidence: 99%

How to build your own ASP-based system?!

Kaminski¹,

Romero²,

Schaub³

et al. 2020

Preprint

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Answer Set Programming (ASP) has become a popular and quite sophisticated approach to declarative problem solving. This is arguably due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. Unlike this, the high degree of sophistication of the underlying technology makes it increasingly hard for ASP experts to put ideas into practice.For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system clingo can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how meta programming can be used for extending ASP. This is a rather light approach that relies on clingo's reification feature to use ASP itself for expressing new functionalities. Unlike this, the major part of this tutorial uses traditional programming (in Python) for manipulating clingo via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is clingo's new Application class that allows us to draw on clingo's infrastructure by customizing processes similar to the one in clingo. For instance, we may engage manipulations to programs' abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. Another cross-sectional structure, spanning meta as well as application programming, is clingo's intermediate format, aspif, that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several non-trivial case-studies. In particular, we show how clingo can be extended by difference constraints and how guess-and-check programming can be implemented with both meta and application programming.

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“…The experience above calls for the need of some extra features of ASP systems, for example, extending their solving capabilities with custom heuristics and propagators; some work in this direction is currently ongoing (see (Dodaro and Ricca 2018) and references therein). However, the applicability of these approaches in our context is not straightforward.…”

Section: Discussionmentioning

confidence: 99%

“…Other approaches are related to the use of methods that "guide" the reasoning, the generation of logic programs, or other optimizations (Law et al 2015(Law et al , 2016Chabierski et al 2017;Dodaro and Ricca 2018); most of them are still at a preliminary stage.…”

Section: Related Workmentioning

confidence: 99%

A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

Calimeri¹,

Cauteruccio²,

Cinelli³

et al. 2019

Theory and Practice of Logic Programming

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Deductive formalisms have been strongly developed in recent years; among them, answer set programming (ASP) gained some momentum and has been lately fruitfully employed in many real-world scenarios. Nonetheless, in spite of a large number of success stories in relevant application areas, and even in industrial contexts, deductive reasoning cannot be considered the ultimate, comprehensive solution to artificial intelligence; indeed, in several contexts, other approaches result to be more useful. Typical bioinformatics tasks, for instance classification, are currently carried out mostly by machine learning (ML)-based solutions. In this paper, we focus on the relatively new problem of analyzing the evolution of neurological disorders. In this context, ML approaches already demonstrated to be a viable solution for classification tasks; here, we show how ASP can play a relevant role in the brain evolution simulation task. In particular, we propose a general and extensible framework to support physicians Theory and Practice of Logic Programming 81 and researchers at understanding the complex mechanisms underlying neurological disorders. The framework relies on a combined use of ML and ASP, and is general enough to be applied in several other application scenarios, which are outlined in the paper.

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The External Interface for Extending WASP

Cited by 14 publications

References 50 publications

How to Build Your Own ASP-based System?!

How to Build Your Own ASP-based System?!

How to build your own ASP-based system?!

A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders

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