Metric quality & invariants

Two pieces of the IPC-5 / SGPlan-class work: a satisfaction-guided metric optimizer for the preference (soft-goal) track, and the Helmert-style mutex-group synthesis that feeds the partition-and-resolve mode.

IPC-5 / metric quality

ferroplan handles all six IPC-5 (2006) simple-preferences soft-goal domains — openstacks, tpp, storage, trucks, rovers, pathways — where the :metric charges for violated preferences (and, in rovers, a numeric traverse cost) and lower is better. Preferences are compiled away (Keyder & Geffner) and the metric is driven by an exact-closure optimizer with budget-escalating branch-and-bound (see PDDL3 preferences).

rovers was the last to fall in: its metric also charges a monotone numeric quantity (sum-traverse-cost), which the optimizer used to ignore — scoring a bogus 0. Folding monotone numeric terms into total-cost lets it optimize the full metric (a real 935.3); see Performance.

The hard part is that delete-relaxation hides the cost of forgoing a soft goal: the free Keyder–Geffner forgo makes every preference look reachable, so on openstacks-soft the metric search had no gradient toward actually delivering — it sat on the all-forgo floor (metric 70 on p01). Two engine steps closed most of that gap:

1. Satisfaction-guided ordering (search::SatGuidance) — a heap penalty counting the preferences forgone in the concrete state, giving the search a reason to deliver. It broke the floor (70 → 63 on openstacks p01) without ever regressing, since it only changes node ordering (branch-and-bound keeps the best plan found).
2. The exact-closure metric optimizer (0.4.0, now the default) — real-state search with metric-bounded acceptance plus an exact collect/forgo phase tail, static preference simplification at compile, barrier-free full-DNF guidance, and a budget-escalating branch-and-bound. This pushed openstacks p01 further (63 → 42) and lifted whole domains (storage to full 8/8 coverage, trucks p08 133 → 10).

The last piece for openstacks was the scheduling of the shared stacks-avail resource, invisible to the satisfaction term because it appears in no preference. That is exactly what the opt-in ESPC penalty loop (FF_ESPC) now does: its λ schedule drives a partitioned composition that prices stacks-avail as a global constraint — taking openstacks ahead of SGPlan5 on p04–p08.

Standing (vs SGPlan5, the IPC-5 winner): full 48/48 coverage and a domain-level lead on two of six (openstacks with FF_ESPC; storage on defaults), parity on the small instances nearly everywhere else — a strong 2nd under the coverage-first rule. The full per-instance tables, the ESPC method, and reproduction commands: benchmarks/ipc5-scoreboard.md.

Mutex groups & SAS+

For the SGPlan-style partition mode, ferroplan synthesizes mutex-group "guidance variables" — Helmert-style monotonicity invariants that recover the SAS+ multi-valued variables of a task (the "where is X / what is held" facts that are mutually exclusive).

A cheap single-predicate pass only finds variables whose values are all the same predicate — the clean position variables (at-robby, lift-at, pointing) — and yields nothing on blocks or logistics. The real work is the multi-predicate refinement: when an action unbalances a candidate (it adds into the variable via one predicate but deletes via another), the candidate is extended with the deleted-and-required fact and re-verified to a fixpoint (crates/ferroplan/src/invariants.rs). That recovers exactly the variables the partitioner needs:

These groups feed SGPlan-style partitioning: the initial partition is seeded from a goal-interaction graph over the mutex variables, and on a conflict the resolver merges the actual conflicting pair. The result shortens blocks plans ~25% where goals share structure but aren't resource-coupled; on resource-coupled domains naive decomposition still re-traverses the shared resource — which the opt-in ESPC penalty loop (FF_ESPC) now prices as a global constraint. Method, coverage numbers, and findings: docs/invariants-measurement.md.