{"type":"ScholarlyArticle","title":"Plausibility Reasoning via Projected Answer Set Counting - A Hybrid Approach","authors":[{"name":"Johannes K. Fichte","slug":"johannes-k-fichte","given":"Johannes K.","family":"Fichte","affiliation":"TU Wien , Vienna , Austria"},{"name":"Markus Hecher","slug":"markus-hecher","given":"Markus","family":"Hecher","affiliation":"TU Wien , Vienna , Austria"},{"name":"Mohamed A. Nadeem","slug":"mohamed-a-nadeem","given":"Mohamed A.","family":"Nadeem","affiliation":"TU Dresden , Dresden , Germany"}],"abstract":"Answer set programming is a form of declarative programming widely used to solve difficult search problems. Probabilistic applications however require to go beyond simple search for one solution and need counting. One such application is plausibility reasoning, which provides more fine-grained reasoning mode between simple brave and cautious reasoning. When modeling with ASP, we oftentimes introduce auxiliary atoms in the program. If these atoms are functionally independent of the atoms of interest, we need to hide the auxiliary atoms and project the count to the atoms of interest resulting in the problem projected answer set counting. In practice, counting becomes quickly infeasible with standard systems such as clasp. In this paper, we present a novel hybrid approach for plausibility reasoning under projections, thereby relying on projected answer set counting as basis. Our approach combines existing systems with fast dynamic programming, which in our experiments shows advantages over existing ASP systems.","doi":"10.24963/ijcai.2022/363","url":"https://ijcai.org/proceedings/2022/363","pdf":"https://ijcai.org/proceedings/2022/0363.pdf","markdown":"https://ijcai.org/proceedings/2022/363.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/363","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Knowledge Representation and Reasoning","track":"Main Track","number":363,"pages":{"first":2620,"last":2626},"keywords":["Knowledge Representation and Reasoning: Computational Complexity of Reasoning","Knowledge Representation and Reasoning: Applications","Knowledge Representation and Reasoning: Argumentation","Knowledge Representation and Reasoning: Non-monotonic Reasoning","Knowledge Representation and Reasoning: Logic Programming"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986942","length_sec":107,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985085","length_sec":315,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Abseher et al., 2017] Michael Abseher, Nysret Musliu, and Stefan Woltran. htd -A Free, Open-Source Framework for (Customized) Tree Decompositions and Beyond. 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