Exploiting Justifications for Lazy Grounding of Answer Set Programs

Bart Bogaerts, Antonius Weinzierl

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

9 Citations (Scopus)


Answer set programming (ASP) is an established knowledge representation formalism. Lazy grounding avoids the so-called grounding bottleneck of ASP by interleaving grounding and solving; this technique was recently extended to work with conflict-driven clause learning. Unfortunately, it often happens that such a lazy grounding ASP system, at the fixpoint of the evaluation, arrives at an assignment that contains literals that are true but unjustified. The system then is unable to determine the actual causes of the situation and falls back to chronological backtracking, potentially wasting an exponential amount of time. In this paper, we show how top-down query mechanisms can be used to analyze the situation, learn a new clause or nogood, and backjump further in the search tree. Contributions include a rephrasing of lazy grounding in terms of justifications and algorithms to construct relevant justifications without grounding. Initial experiments indicate that the newly developed techniques indeed allow for an exponential speed-up.
Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJérôme Lang
Number of pages9
ISBN (Electronic)978-0-9992411-2-7
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Artificial Intelligence - Stockholm, Sweden
Duration: 13 Jul 201819 Jul 2018
Conference number: 27


ConferenceInternational Joint Conference on Artificial Intelligence
Abbreviated titleIJCAI
Internet address


  • Knowledge Representation and Reasoning: Non-monotonic Reasoning
  • Constraints and SAT: Constraints: Solvers and Tools
  • Knowledge Representation and Reasoning: Non-classical Logics for Knowledge Representation


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  • Finnish centre of excellence in computational inference research

    Xu, Y., Rintanen, J., Kaski, S., Anwer, R., Parviainen, P., Soare, M., Vuollekoski, H., Rezazadegan Tavakoli, H., Peltola, T., Blomstedt, P., Puranen, S., Dutta, R., Gebser, M., Mononen, T., Bogaerts, B., Tasharrofi, S., Yang, Z., Pesonen, H. & Weinzierl, A.


    Project: Academy of Finland: Other research funding

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