Fast Inference for Probabilistic Answer Set Programs via the Residual Program
This work addresses efficiency issues for users of probabilistic answer set programming, though it is incremental as it builds on existing SLG resolution techniques.
The paper tackles the problem of slow inference in Probabilistic Answer Set Programs by identifying and removing program parts that do not affect query probabilities, using the residual program from SLG resolution. Empirical results on graph datasets show significantly faster inference times.
When we want to compute the probability of a query from a Probabilistic Answer Set Program, some parts of a program may not influence the probability of a query, but they impact on the size of the grounding. Identifying and removing them is crucial to speed up the computation. Algorithms for SLG resolution offer the possibility of returning the residual program which can be used for computing answer sets for normal programs that do have a total well-founded model. The residual program does not contain the parts of the program that do not influence the probability. In this paper, we propose to exploit the residual program for performing inference. Empirical results on graph datasets show that the approach leads to significantly faster inference.