Mechanism

The mechanism stratifies operator intent into N fidelity tiers and resolves each observed entity at the highest tier its current evidence and credentials support. The reference embodiment uses three tiers, but the architecture is parameterized: a deployment may declare two, three, four, or more, with the tier ladder defined by the governance policy in force.

Tier 1 is full cognitive-state broadcast. A cooperative agent publishes its planning graph, intent field, capability envelope, constraint set, and uncertainty bands as credentialed observations. The broadcast is signed, time-stamped, and bounded by a declared scope of validity. Consumers within scope receive the broadcast and admit it through governance-credentialed channels, treating its contents as native cognitive context rather than as raw sensor data.

Tier 2 is structured partial-fidelity extraction. The observed agent does not publish its full plan but does emit a constrained, machine-readable subset of intent indicators: turn signals, brake activation, formation tags, transponder squawks, route hints, mode bits. The partial broadcast is structured enough to be parsed deterministically and constrained enough to be issuable from legacy or low-capability platforms. The consumer reconstructs an intent estimate from the structured signal alone, accompanied by a declared confidence reflecting the residual ambiguity.

Tier 3 is behavior-inferred attribution. No cooperative emission is available. The consumer infers intent from observed kinematics, trajectory, velocity profile, gaze vector, gesture, formation geometry, historical pattern continuity. The inference produces a credentialed observation with substantially lower fidelity than Tier 1 or Tier 2, but with the same admissibility format, so that downstream evaluators handle it through the same pipeline.

The tier at which a given entity is resolved is not fixed. A fidelity-tier classifier assigns each operating unit to a tier through one or more disclosed classification mechanisms: self-declared classification, where the unit advertises its tier as part of its mesh-identity observation; credential-based classification, where the unit's governance credentials indicate its intent-disclosure capability; observation-based classification, where the mesh infers the unit's tier from observed intent-disclosure behavior; capability-based classification, drawn from the unit's capability envelope; manufacturer-attestation classification at deployment enrollment; and hybrid combinations of the foregoing. The classifier also supports dynamic-tier classification, in which a unit's tier may transition during operation. A fully integrated unit temporarily operating without network connectivity may transition to a lower tier.

All three tiers feed contributions into the same composite admissibility evaluator. The evaluator receives, per entity, a vector of tier-tagged observations with declared fidelity, confidence, and provenance. It produces a fused intent estimate weighted by tier, with cross-tier corroboration explicitly evaluated: agreement between Tier 2 indicator and Tier 3 inference raises confidence; disagreement triggers a flagged observation that propagates to planning rather than being silently averaged away.

Operating Parameters

The number of tiers N is a deployment parameter. Two-tier deployments collapse Tier 2 and Tier 3 into a single inferred-or-indicated tier; four-tier deployments split Tier 1 into a self-broadcast tier and a third-party-attested tier. The reference description uses N=3 because it covers the population partition observed in current mixed-fleet conditions: full cooperators, signal-emitting non-cooperators, and silent participants.

Each tier carries a governance-policy-configurable tier-weighted evidential factor. These factors admit multiple representations, including scalar weights, probability distributions over intent confidence, bounded uncertainty intervals, and governance-policy-defined composite representations. In an exemplary scalar representation, the full-fidelity tier weights near unity while the behavior-inferred tier weights toward a lower factor. The weights are governance-policy-bound rather than free runtime parameters: a consumer cannot silently up-weight a low-fidelity tier to influence admissibility.

Tier boundaries are themselves governance-policy-configurable, and the structure admits additional tiers or finer-grained subdivision through governance-policy-defined tier specification, supporting deployments with deployment-specific intent-disclosure capabilities without architectural modification.

The composite evaluator treats intent observations from all tiers as input with tier-appropriate evidential weighting, producing a unified admissibility determination without privileging any tier a priori. The fidelity-tier factor composes with the broader admissibility weighting, integrating authority weight, staleness weight, modality-reliability weight, dispositional weight, reputation weight, corroboration weight, physical-plausibility weight, and consent-governance weight. Each contribution carries lineage, and the consuming agent's confidence governor modulates response admissibility based on the composite intent confidence, with higher composite confidence admitting earlier and more decisive coordination and lower composite confidence admitting only conservative coordination.

Alternative Embodiments

In a connected-vehicle embodiment, Tier 1 is V2X cooperative messaging carrying the planning graph; Tier 2 is the structured visual indicator set (turn signal, brake light, hazard, headlight modulation) plus transponder data; Tier 3 is motion-based attribution from on-board perception. The same composite evaluator drives lane-change negotiation, intersection arbitration, and merge coordination across cooperators and non-cooperators in the same traffic stream.

In a drone-swarm UTM embodiment, Tier 1 is full plan broadcast among credentialed swarm members; Tier 2 is ADS-B or Remote ID emission from non-swarm aircraft; Tier 3 is radar-track or optical-track inference for non-emitting platforms. Cross-tier corroboration is essential because a swarm operating in mixed airspace must reconcile its own high-fidelity plans with low-fidelity attributions for nearby uncooperative traffic.

In a defense embodiment, Tier 1 is friendly cognitive-state sharing under coalition credentials; Tier 2 is structured intent extraction from credentialed cooperative units; Tier 3 is sensor-fusion inference for unidentified or adversarial tracks. The adversarial-intent inference mechanism evaluates entities exhibiting adversarial signatures, and the disclosure expressly treats deceptive masquerading signals purporting to be cooperative signals as an adversarial-signature category, so that a spoofed broadcast does not acquire trust through credential confusion.

In a human-robot teaming embodiment, Tier 1 is explicit operator command (typed, spoken, or commanded through a structured interface); Tier 2 is gesture or gaze command parsed through a constrained vocabulary; Tier 3 is behavioral inference from posture and approach trajectory. The robot resolves operator intent at the highest tier currently supported by the operator's actions, falling back gracefully when the operator stops speaking or stops gesturing.

In an industrial-coordination embodiment, Tier 1 is full schedule broadcast among cooperating cells; Tier 2 is signal-light or status-tag emission from legacy machinery; Tier 3 is throughput-pattern inference for unmonitored equipment. The tiering allows mixed legacy and modern equipment to coordinate without retrofitting the legacy assets to full broadcast capability.

Composition with Other Mechanisms

The graduated-fidelity tier ladder composes with the broader operator-intent intake stack. The fidelity tag produced per entity flows downstream into admissibility evaluation, where it weights the contribution of intent-derived observations relative to other cognitive inputs. A high-confidence Tier 3 attribution is admitted as a credentialed observation but cannot, by policy, override a directly-observed physical constraint.

The mechanism composes with credentialed observation channels: each tier emits its contribution through the same admission interface, so that downstream consumers do not branch on tier. Tier shows up as a field on the observation, not as a routing decision in the consumer. This is what makes the architecture composable across deployments, adding a new tier or a new sensor modality is a producer-side change, not a consumer-side one.

The mechanism composes with governance and policy enforcement: tier weights, tier boundaries, and credential requirements are all governance-policy-configurable. A regulator or operator can adjust the tier specification for a particular deployment without architectural modification. This makes the same primitive deployable across regulated and unregulated domains.

The mechanism composes with cross-entity coordination: because all observed entities are surfaced at some tier, planning code never has to special-case "agent we know about" versus "agent we infer about." Both are credentialed observations, and the planner reasons over the population as a single set with heterogeneous fidelity rather than as two separate populations requiring two separate code paths.

Prior-Art Distinction

Prior approaches to mixed-population coordination treat each population as a separate problem with separate machinery. V2X stacks address cooperator-to-cooperator messaging. Sensor-fusion stacks address inference of non-cooperators. Classification stacks address adversarial detection. Each stack has its own inputs, its own outputs, its own fidelity assumptions, and its own failure modes. Boundary cases, a partially cooperative agent, a cooperator with degraded broadcast, an inferred agent that briefly emits a structured signal, are handled by ad-hoc glue code that grows brittle as deployments scale.

The mechanism described here is structurally different. There is one admissibility evaluator. All populations enter it through the same observation interface. The fidelity tier is a declared field on the observation, not a separate code path. Cross-tier corroboration is an explicit operation on tier-tagged contributions rather than an emergent behavior of independently-tuned subsystems. Boundary cases are first-class: an agent that gains or loses cooperative emission moves up or down the tier ladder under explicit policy, rather than being silently re-routed between subsystems.

Prior weighted-fusion approaches mix sensor inputs with confidence weights but do not carry a distinct fidelity-tier factor reflecting the source's intent-disclosure capability. In this mechanism the fidelity-tier weight is a separate factor in the composite admissibility weighting, composing alongside authority, staleness, modality-reliability, dispositional, reputation, corroboration, physical-plausibility, and consent-governance factors, so that policy can weight a low-fidelity attribution by tier independently of its instantaneous confidence.

Disclosure Scope

The disclosure covers the mechanism of resolving operator intent across a plurality of fidelity tiers spanning fully-shared cognitive state, structured partial-fidelity integration, and behavior-inferred intent, with a governance-policy-configurable fidelity-tier classifier and with cross-tier fusion through a composite admissibility evaluator that consumes tier-tagged governance-credentialed observations. Embodiments described include the exemplary three-tier deployment and governance-policy-defined finer-grained variants in connected-vehicle, drone-swarm, defense, human-robot, and industrial settings.

The disclosure scope includes: the fidelity-tier structure with governance-policy-configurable tier boundaries; the fidelity-tier classifier and its classification mechanisms, including dynamic-tier transition; the tier-weighted evidential factors; the cross-tier composite admissibility evaluator and multi-source intent fusion; the tier-specific governance-credentialed intent observation schemas as the unifying interface; and the governance-policy-bound nature of tier weights and tier specification. This subject matter is disclosed in U.S. Provisional Application No. 64/049,409 and is intended to be claimed in coordination with related operator-intent intake mechanisms disclosed in that application.