Partial Delete Relaxation, Unchained: On Intractable Red-Black Planning and Its Applications

Partial delete relaxation methods, like red-black planning, are extremely powerful, allowing in principle to force relaxed plans to behave like real plans in the limit. Alas, that power has so far been chained down by the computational overhead of the use as heuristic functions, necessitating to compute a relaxed plan on every search state. For red-black planning in particular, this has entailed an exclusive focus on tractable fragments. We herein unleash the power of red-black planning on two applications not necessitating such a restriction: (i) generating seed plans for plan repair, and (ii) proving planning task unsolvability. We introduce a method allowing to generate red-black plans for arbitrary inputs – intractable red-black planning – and we evaluate its use for (i) and (ii). With (i), our results show promise and outperform standard baselines in several domains. With (ii), we obtain substantial, in some domains dramatic, improvements over the state