Multi-shot ASP solving with clingo
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Abstract (arXiv)
We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP system clingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query-answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities. clingo accomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end, clingo offers a new application programming interface that is conveniently accessible via scripting languages.
Key facts. Cited by 115 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 May 2017.
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Citing papers per year: 2021: 14; 2022: 10; 2023: 19; 2024: 18; 2025: 25; 2026: 29.
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- 2024CLMASP: Coupling Large Language Models with Answer Set Programming for Robotic Task Planning5 Jun 2024
- 2024Symbol Correctness in Deep Neural Networks Containing Symbolic Layers6 Feb 2024
- 2023Bridging Logic and Learning: A Neural-Symbolic Approach for Enhanced Reasoning in Neural Models (ASPER)18 Dec 2023
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- 2026Learning to Assemble Novel Structures with Unfamiliar Parts under Semantic Constraints13 Aug 2026
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- 2026How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming23 Jul 2026
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