Modeling Variations of First-Order Horn Abduction in Answer Set Programming
This work addresses the challenge of efficient abduction reasoning in AI, particularly for plan recognition, but it is incremental as it builds on existing ASP methods with specific optimizations.
The paper tackles the problem of abduction in First Order Horn logic theories by representing it in Answer Set Programming (ASP) to handle global constraints and objective functions, achieving more efficient performance than state-of-the-art solvers on the ACCEL benchmark for plan recognition in Natural Language Understanding.
We study abduction in First Order Horn logic theories where all atoms can be abduced and we are looking for preferred solutions with respect to three objective functions: cardinality minimality, coherence, and weighted abduction. We represent this reasoning problem in Answer Set Programming (ASP), in order to obtain a flexible framework for experimenting with global constraints and objective functions, and to test the boundaries of what is possible with ASP. Realizing this problem in ASP is challenging as it requires value invention and equivalence between certain constants, because the Unique Names Assumption does not hold in general. To permit reasoning in cyclic theories, we formally describe fine-grained variations of limiting Skolemization. We identify term equivalence as a main instantiation bottleneck, and improve the efficiency of our approach with on-demand constraints that were used to eliminate the same bottleneck in state-of-the-art solvers. We evaluate our approach experimentally on the ACCEL benchmark for plan recognition in Natural Language Understanding. Our encodings are publicly available, modular, and our approach is more efficient than state-of-the-art solvers on the ACCEL benchmark.