Compiling Possibilistic Networks: Alternative Approaches to Possibilistic Inference
This work addresses a specific bottleneck in possibility theory for researchers in uncertain reasoning, though it is incremental relative to existing probabilistic methods.
The paper tackled the lack of alternative exact reasoning algorithms for qualitative possibilistic networks by exploring compilation techniques, resulting in a new purely possibilistic method that outperforms adapted probabilistic methods, as confirmed by experimental results.
Qualitative possibilistic networks, also known as min-based possibilistic networks, are important tools for handling uncertain information in the possibility theory frame- work. Despite their importance, only the junction tree adaptation has been proposed for exact reasoning with such networks. This paper explores alternative algorithms using compilation techniques. We first propose possibilistic adaptations of standard compilation-based probabilistic methods. Then, we develop a new, purely possibilistic, method based on the transformation of the initial network into a possibilistic base. A comparative study shows that this latter performs better than the possibilistic adap- tations of probabilistic methods. This result is also confirmed by experimental results.