Proceedings Abstracts of the Twenty-Third International Joint Conference on Artificial Intelligence

Just-in-Time Compilation of Knowledge Bases / 447
Gilles Audemard, Jean-Marie Lagniez, Laurent Simon

Since the first principles of Knowledge Compilation (KC), most of the work has been focused in finding a good compilation target language in terms of compromises between compactness and expressiveness. The central idea remained unchanged in the last fifteen years: an off-line, very hard, stage, allows to "compile" the initial theory in order to guarantee (theoretically) an efficient on-line stage, on a set of predefined queries and operations.  We propose a new "Just-in-Time" approach for KC. Here, any Knowledge Base (KB) will be immediately available for queries, and the effort spent on past queries will be partly amortized for future ones.  To guarantee efficient answers, we rely on the tremendous progresses made in the practical solving of SAT and incremental SAT applicative problems. Even if each query may be theoretically hard, we  show that our approach outperforms previous KC approaches on the set of classical problems used in the field, and allows to handle problems that are out of the scope of current approaches.