A Fine-Grained Complexity View on Propositional Abduction - Algorithms and Lower Bounds

A Fine-Grained Complexity View on Propositional Abduction - Algorithms and Lower Bounds

Victor Lagerkvist, Mohamed Maizia, Johannes Schmidt

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence
Main Track. Pages 4562-4569. https://doi.org/10.24963/ijcai.2025/508

The Boolean satisfiability problem (SAT) is a well-known example of monotonic reasoning, of intense practical interest due to fast solvers, complemented by rigorous fine-grained complexity results. However, for non-monotonic reasoning, e.g., abductive reasoning, comparably little is known outside classic complexity theory. In this paper we take a first step of bridging the gap between monotonic and non-monotonic reasoning by analyzing the complexity of intractable abduction problems under the seemingly overlooked but natural parameter n: the number of variables in the knowledge base. We obtain several positive results for SigmaP2- as well as NP- and coNP-complete fragments, which implies the first example of beating exhaustive search for a SigmaP2-complete problem (to the best of our knowledge). We complement this with lower bounds and for many fragments rule out improvements under the (strong) exponential-time hypothesis.
Keywords:
Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning
Constraint Satisfaction and Optimization: CSO: Constraint satisfaction
Constraint Satisfaction and Optimization: CSO: Satisfiabilty
Knowledge Representation and Reasoning: KRR: Diagnosis and abductive reasoning