Fleet Behavior Extrapolation

Mechanism

The forecasting engine, when invoked for a target agent, performs three structural operations that together constitute fleet behavior extrapolation. First, it resolves the set of credentialed fleet peers — agents whose lineage shares a common fleet credential with the target, where the credential carries a declared scope (route class, mission profile, hardware revision, operator identity) that licenses cross-member inference. Second, it ingests the observed behavioral traces of those peers as first-class evidence: completed tasks, latency distributions, energy curves, exception patterns, intervention rates. Third, it composes a forecast for the target by projecting peer-observed patterns onto the target's pending interval, weighted by the credentialed similarity between target and peer and bounded by the explicit extrapolation envelope declared in policy.

Per-member admissibility is the load-bearing element. Each peer's contribution to the composite forecast is admitted or rejected on its own merits: a peer with stale credential, with lineage gaps over the inference interval, with hardware revision outside the declared scope, or with a recent disqualifying exception is excluded from the composition for that forecast even if it remains a fleet member for other purposes. The forecast carries a manifest enumerating which peers contributed, the weight assigned to each, and the credential clauses that licensed admission. Downstream consumers — coordination authorities, regulators, the agent's own arbitration engine — read the manifest as a structural artifact rather than treating the forecast as an opaque scalar.

The extrapolation is bounded. Policy declares the maximum projection horizon, the maximum permissible deviation from the peer-observed mean, and the conditions under which the extrapolation must fall back to a per-unit-only forecast — for example, when the credentialed peer set falls below a quorum, when peer traces diverge beyond a configured dispersion threshold, or when the target's current state crosses a regime boundary that peers have not themselves traversed under the same credential. Beyond the bound, the engine refuses to extrapolate and emits a structured insufficiency event rather than producing a forecast of unattested confidence.

Operating Parameters

The primitive exposes a defined parameter surface. The fleet credential predicate determines which peers are eligible: it may be exact-match (same operator, same hardware build, same mission profile) or relaxed under explicit policy clauses (cross-operator under a recognized cross-recognition agreement, cross-build under a declared compatibility manifest). The peer-weighting function maps credentialed similarity onto contribution weight; common forms are uniform within scope, recency-decayed, or similarity-kernel weighted, but the function itself is policy-declared and recorded in the forecast manifest. The horizon parameter sets the forward projection window and is typically tied to the credential's freshness clause — a credential that expires in fifteen minutes cannot license a six-hour extrapolation.

Quorum and dispersion parameters govern fallback. A configured minimum count of admissible peers must be present for the extrapolation to proceed; a configured maximum dispersion among peer traces must not be exceeded. When either bound is breached, the engine downgrades to a per-unit forecast and records the downgrade as a lineage event so that downstream consumers can distinguish a fleet-extrapolated projection from a single-unit projection. The deviation envelope parameter caps how far the composite forecast may move from the target's own per-unit baseline; this prevents a peer set with anomalous traces from yanking the target's projection into a regime it has no independent evidence for.

Each parameter is policy-declared rather than learned, and each is auditable through the credentialed policy reference. Operators tune the parameters per fleet and per mission class; the structural mechanism is unchanged across tunings.

Alternative Embodiments

The primitive admits multiple embodiments that preserve the structural contract while varying implementation detail. In a centralized embodiment, a fleet-coordination authority subscribes to per-member traces, computes the composite forecast on behalf of each member, and returns it as a credentialed observation; members admit the returned forecast through their normal validation pipeline. In a peer-to-peer embodiment, each agent subscribes directly to its credentialed peers' published traces and computes its own composite locally; the manifest is constructed at the consumer rather than at the authority. In a hybrid embodiment, a coordination authority publishes a fleet-mean trace as a credentialed summary, and individual agents combine the summary with selectively subscribed peer traces.

The extrapolation function admits multiple forms: linear projection of peer-observed deltas, kernel-weighted regression over peer traces, or learned models whose training corpus is itself credentialed. The structural requirement is not the function family but the manifest: whichever function is used, the forecast carries an explicit record of the inputs, the weights, and the policy clauses that licensed admission. Cross-fleet embodiments are supported through credentialed cross-recognition, in which an agent in fleet A admits peer traces from fleet B under a declared cross-recognition clause; the manifest records the clause so that downstream consumers may apply differential trust.

The primitive is not limited to mobility fleets. Logistics agents, distributed inference workers, monitoring sensors, and cooperative manipulators all qualify as fleets in the sense the patent specifies: pluralities of agents whose shared credential licenses cross-member behavioral extrapolation under bounded policy.

Composition with Adjacent Primitives

Fleet behavior extrapolation does not stand alone. Its outputs are credentialed observations that enter the agent's normal validation pipeline. The mutation engine merges the composite forecast into candidate state; the validation engine checks the composite against constraint clauses (resource budgets, safety envelopes, regulatory windows); the arbitration engine resolves conflicts when the fleet-extrapolated projection disagrees with a per-unit projection or with a directly observed measurement. The forecast's manifest is read by arbitration as evidence — a forecast supported by a large credentialed peer set with low dispersion outweighs one supported by a thin peer set, and the arbitration record cites the manifest explicitly.

Downstream of forecasting, the composite is consumed by planning, by capacity advertisement, by cross-fleet coordination authorities, and by regulatory observers operating on credentialed read-access. Each consumer applies its own admissibility policy to the forecast, and each can reject it on grounds the forecast manifest exposes. The primitive composes upward into multi-fleet emergent forecasts when fleet-coordination authorities are themselves credentialed members of an inter-fleet credential domain — emergent behaviors at the inter-fleet scale are projected through the same structural mechanism applied at a higher tier.

Prior-Art Distinction

Conventional fleet forecasting falls into two families. The first treats fleet members as independent units and forecasts each one in isolation, then aggregates the per-unit forecasts statistically; this misses cross-member structure and cannot bound an individual unit's projection by peer evidence. The second pools fleet data into a centralized model that produces fleet-level outputs but offers no per-member admissibility, no manifest, and no structural account of which member's data licensed which inference. Neither family treats fleet membership as a credentialed relation, and neither carries the per-member admissibility manifest that the present primitive requires.

The distinction is not statistical refinement. Conventional pipelines may match the present primitive's accuracy under benign conditions. They diverge under adversarial or anomalous conditions, where the absence of per-member admissibility and the absence of a credential-bounded extrapolation envelope permit a single compromised or anomalous peer to pull the fleet forecast in directions no policy authorized. The present primitive's structural rejection of such contamination — through credential check, dispersion bound, deviation envelope, and quorum requirement — is the operative novelty.

Evidence Weighting and Credential Decay

Evidence weighting is the bridge between credentialed admissibility and quantitative composition. Each admitted peer enters the extrapolation with a scalar weight produced by a declared evidence-weighting function. The function's inputs are credential strength, credential freshness, lineage continuity over the inference interval, and demonstrated lineage-anchored performance within the relevant scope. Each input is itself a credentialed observation — credential strength is read from the credentialing authority's declared schema, freshness from the credential's issue and expiry timestamps, continuity from the absence of gap events in the peer's lineage over the inference window, and performance from the peer's anchored outcome record on tasks the policy declares relevant.

Credential decay applies to peers whose credentials are valid but stale relative to the inference horizon. A peer credentialed for a route class three months ago, with no subsequent re-attestation, contributes less weight than a peer re-attested within the policy-declared freshness window. Decay is policy-declared rather than implicit: the decay function appears in the forecast manifest, and operators tune it per fleet and per mission. Where decay drops a peer's weight below an admission threshold, the peer is excluded from the composition and the exclusion is recorded with grounds, preserving the per-member admissibility guarantee.

The evidence-weighting function is itself versioned and credentialed. A fleet whose evidence-weighting policy changes — for example, in response to an observed correlated failure mode — produces forecasts under the new policy with the policy version recorded in each manifest. Replay of historical forecasts under the historical policy version is supported through the lineage substrate, which preserves both the policy and the inputs at the time of each composition. The structural commitment is that the forecast can always be reconstructed from credentialed inputs and a credentialed policy.

Failure Modes and Insufficiency Events

The primitive declares its own failure modes structurally. Quorum insufficiency, dispersion exceedance, horizon overrun, and credential-staleness exhaustion each produce a named insufficiency event rather than a degraded forecast. A consumer reading the lineage sees either a forecast with a complete admission manifest or an insufficiency event with named grounds; the consumer never sees a forecast of unattested confidence masquerading as one of attested confidence. Insufficiency events are themselves anchored, so that an operator reviewing fleet behavior over time can distinguish intervals where extrapolation was structurally possible from intervals where it was not, and can tune fleet composition or credential policy in response.

Correlated peer failure is treated explicitly. When the engine detects that peer traces share a common anomaly — a manufacturing-revision defect, a software-update regression, a shared environmental disturbance — the dispersion bound may be satisfied while the peer set is collectively misleading. The primitive defends against this through declared correlation-detection clauses that, when triggered, suppress the contribution of the correlated subset and record the suppression in the manifest. The detection is policy-declared and credentialed, not implicit in the engine's implementation.

Disclosure Scope

The disclosure covers the structural mechanism by which a credentialed peer set licenses bounded behavioral extrapolation, the per-member admissibility manifest that accompanies the resulting forecast, the policy-declared parameter surface that governs admission and bounding, and the composition of the primitive with adjacent forecasting, validation, and arbitration primitives. The scope extends across centralized, peer-to-peer, and hybrid embodiments, across linear, kernel, and learned extrapolation functions, and across fleet types ranging from mobility through logistics through distributed inference. The scope does not depend on a particular extrapolation function, a particular credential issuance protocol, or a particular communication topology; it depends on the structural treatment of fleet membership as a credentialed relation that licenses bounded, manifested, per-member-admissible cross-agent inference.