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Substituting object names for all parameters in an action schema, retaining the type-correct tuples and any required static restrictions. The result is a collection of ground actions.

An action with no unbound object variables. Its precondition and effect sets consist of fixed ground facts. Action grounding an action schema over a finite object universe creates one candidate action for each type-correct parameter tuple, after any static filtering; a time-indexed encoding then creates copies or variables for individual action occurrences.

The difficulty is representing dictionary membership and the one-letter relationship in positive conjunctive STRIPS preconditions; a finite dictionary nevertheless admits an exact encoding. Pure STRIPS does not provide string indexing, a “differs in one position” operator, arbitrary disjunctive tests or a procedure that calls a dictionary lookup. Those relationships must be represented by ground facts or compiled into ground actions.

One exact whole-word encoding uses a planning fluent $\mathrm{At}(w)$ and a static predicate $\mathrm{Next}(w,w^{\prime })$ containing exactly the permitted dictionary mutations. For the action schema $\mathrm{Change}(w,w^{\prime })$, useInitialize $\mathrm{At}(s)$ and the static transition facts, and use goal $\mathrm{At}(t)$. Only one $\mathrm{At}$ fact is true initially. Each applicable ground action deletes it and adds exactly its permitted successor, so mathematical induction proves that exactly one current-word fact remains true and that every plan is a legal word ladder. Conversely, every legal word ladder directly supplies a plan. An isolated non-dictionary start is handled as in (c). A shortest plan has the fewest mutations.

This encoding moves the hard lexical relationship into preprocessing. For $N$ dictionary words, checking all ordered pairs takes $O(N^{2}L)$ character comparisons, although indexing can do better; the number of stored transitions is at most $NL(|\Sigma |-1)$ when all states are dictionary words. The STRIPS representation is finite, but its size and preprocessing cost matter.

A position-based representation instead uses planning fluents $\mathrm{Letter}(i,\ell )$. Changing position $i$ must delete its old letter and add its new one; all other positions persist through the STRIPS update rule. Merely requiring each position to contain a letter admits non-dictionary strings. A disjunction over all dictionary words is not a positive conjunctive STRIPS precondition. It can be compiled into separate ground actions, each requiring every letter of a valid source word and changing one position to reach a valid destination. This again enumerates lexical cases and can be large. Arbitrary letter changes followed by an unconstrained “validate” action are unsound: several illegal intermediate strings could otherwise lead to a legal goal. Legality must hold after every mutation, not only at the endpoint.

There are $G=2mn+n(n-1)$ distinct ground actions, or $tG$ time-indexed action variables over $t$ transition steps. Each of STRIPS's pick and drop action schemas has $mn$ type-correct instantiations. A move chooses an ordered pair of distinct buckets, giving $n(n-1)$. Every physical action is a ground action before it receives a time index in SATPlan.These counts exclude static type checks after action grounding and exclude optional persistence/no-op action symbols. Using successor-state axioms supplies persistence without separate no-op action variables. If one action occurrence is allowed per layer, it is selected from the same ground-action set; a parallel encoding also begins with these physical ground actions, but has a different exclusion policy.

Use balls $a,b$, buckets $1,2$, transition index $k$ and the grounded action schemas from (a). Under a sequential encoding, one example successor-state axiom isBall $a$ is in bucket $1$ afterwards exactly when it is dropped there now, or it was there and is not picked up. Actions involving other balls or arm movement leave this fact unchanged. This is both an effect rule and a persistence rule, rather than an implication that permits spontaneous appearances.

An example action precondition axiom isA selected pick action must have the ball, arm and empty hand in the appropriate current state. The static type conditions have already been satisfied by action grounding.

An example action exclusion axiom isThe single arm cannot pick up both balls in one step. For a serial encoding include such an exclusion for every pair of distinct action occurrences at one layer; with at-most-one rather than exactly-one actions, a layer may idle. Initial-state clauses, goal clauses and corresponding successor-state axioms for all other planning fluents complete the plan-as-SAT construction. The plan-as-SAT approach was introduced by Henry Kautz and Bart Selman in 1992.^[ref]