mlsanityData to 7 Oct 2026

How to build your own ASP-based system?!

arXiv 2008.06692 ↗First posted 15 Aug 2020cs.AI cs.PLcited by 20

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Abstract (arXiv)

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.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Aug 2020.

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Citing papers per year: 2021: 2; 2022: 1; 2023: 6; 2024: 2; 2025: 7; 2026: 2.

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  1. 2021Answer-Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classificationcited by 9
  2. 2021Harnessing Incremental Answer Set Solving for Reasoning in Assumption-Based Argumentationcited by 4
  3. 2025ASP-driven User-interaction with Clinguincited by 2
  4. 2025The ASP-based Nurse Scheduling System at the University of Yamanashi Hospitalcited by 2
  5. 2023Rethinking Answer Set Programming Templatescited by 1
  6. 2023Current and Future Challenges in Knowledge Representation and Reasoningcited by 1
  7. 2023Explanations for Answer Set Programmingcited by 1
  8. 2025Splitting Answer Set Programs with respect to Intensionality Statements (Extended Version)cited by 1
  9. 2025Smart Expansion Techniques for ASP-based Interactive Configurationcited by 1
  10. 2022plingo: A system for probabilistic reasoning in clingo based on lpmlncited by 0
  11. 2023Bounded Combinatorial Reconfiguration with Answer Set Programmingcited by 0
  12. 2023DELPHIC: Practical DEL Planning via Possibilities (Extended Version)cited by 0
8 more
  1. 2023Extending Answer Set Programming with Rational Numberscited by 0
  2. 2024Reasoning about Study Regulations in Answer Set Programmingcited by 0
  3. 2024Solving Epistemic Logic Programs using Generate-and-Test with Propagationcited by 0
  4. 2025Compiling Metric Temporal Answer Set Programmingcited by 0
  5. 2025ASP-FZN: A Translation-based Constraint Answer Set Solvercited by 0
  6. 2025Towards a Neurosymbolic Reasoning System Grounded in Schematic Representationscited by 0
  7. 2026Meta-Programming for Linear-time Temporal Answer Set Programmingcited by 0
  8. 2026EZSMT Version 3, Maturedcited by 0