Conformant Planning as a Case Study of Incremental QBF Solving
This is an incremental improvement for planning and QBF solving domains, potentially applicable to other areas.
The paper tackled planning with uncertainty by using incremental quantified Boolean formula (QBF) solving, showing that this approach outperforms non-incremental solving in experiments.
We consider planning with uncertainty in the initial state as a case study of incremental quantified Boolean formula (QBF) solving. We report on experiments with a workflow to incrementally encode a planning instance into a sequence of QBFs. To solve this sequence of incrementally constructed QBFs, we use our general-purpose incremental QBF solver DepQBF. Since the generated QBFs have many clauses and variables in common, our approach avoids redundancy both in the encoding phase and in the solving phase. Experimental results show that incremental QBF solving outperforms non-incremental QBF solving. Our results are the first empirical study of incremental QBF solving in the context of planning and motivate its use in other application domains.