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An incremental belief-state search initially plans for a small subset $B_0$ of the possible initial states, then tests the candidate plan on the full initial belief state $B$. If a state in $B$ makes an action inapplicable or fails the final goal test, add that counterexample to $B_0$ and solve the enlarged problem. The new search may need to revise the entire plan, rather than append actions to the previous candidate.

With finitely many initial states and a complete finite-model solver for each restricted problem, at most $|B|$ state additions are required. Failure to find a plan even for a restricted subset proves that none can work for the whole belief state. A candidate that passes full validation is a genuine plan for sensorless planning. The advantage is selective tracking: a small number of representative initial states may force a universally successful plan without constructing the full power set search space.

Sensorless planning chooses one action sequence that succeeds from every possible initial physical state without receiving distinguishing observations during execution. A belief state is a set of possibilities. For a deterministic search transition model $T$, an applicable action maps $B$ to $\{T(s,a):s\in B\}$; for nondeterministic outcomes, take the union of all possible successors. Applicability must hold for every member of $B$, and success requires the resulting belief state to be a subset of the physical goal set.

Every fixed action sequence preserves inclusion of the initial belief states. Consequently, a successful plan for $B$ also succeeds for every subset of $B$. Success on a superset is an additional requirement, so cannot be inferred from success on $B$ alone. This property supports incremental belief-state search.