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As also shown in this paper, the simple combination of reachability heuristics is often unable to provide detailed guidance. Therefore, an attempt is carried out to combine information obtained from different planning graphs. The presented results are still somewhat preliminary. Conformant FF (CFF). The most recent and impressive approach to conformant planning is implemented in CFF [8]. CFF is the first conformant planner able to fully exploit the ability of state-of-the-art classical planners to deal with large deterministic domains.

Thus, unless the current target knowledge has reached a maximal bound, it is increased by I NCREASE TARGET K NOWLEDGE , and control is switched over to the ACQUIRE K NOWLEDGE M ODE in order to acquire the new target knowledge. If, instead, the target knowledge has reached the maximal knowledge bound, the search is continued by recurring on R EACH G OAL M ODE. Therefore, once ground search has failed for every selected target knowledge, it is no longer exited, until a solution is found or and all open nodes are recursively expanded.

However, in this case the use Vψ function does not improve the search. This ∗ is able to discover the need to acquire the key to achieve the goal. is due to the fact that Vdp Navigation hard. We tried to identify necessary knowledge formulae for the SQUAREHOLE and for the CUBE-OBSTACLE domains considered in the previous section. For the . SQUARE-HOLE a necessary knowledge formula is ψ = K(x = 0) ∨ K(x = N/2), while for the CUBE-OBSTACLE we used the generalization of the cube case of the one used for the empty room with an obstacle described above.

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