Three-Tier Intent Fidelity

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.

Crucially, the tier at which a given entity is resolved is not fixed. It is selected per-entity per-cycle by a tier selector that weighs three things: the capability of the source agent (can it broadcast Tier 1 at all), the evidence available at the moment (is a Tier 2 indicator currently visible), and the governance posture in force (does the consumer have credentials to admit Tier 1 from this source under the current policy). When a higher tier becomes available — a cooperative broadcast arrives, a credential is issued, an indicator activates — the selector promotes. When a higher tier becomes unavailable — broadcast lost, credential revoked, indicator obscured — the selector demotes gracefully, never silently.

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 declared fidelity weight in the range [0, 1]. Tier 1 weights cluster near unity; Tier 3 weights cluster near a small positive floor. The weights are not free parameters at runtime — they are policy-bound, declared in the governance reference, and audited. A consumer cannot silently up-weight a low-fidelity tier to influence admissibility; any weight change is a governance event.

Tier promotion is gated on three independent conditions, all of which must be satisfied: capability presence (the source can produce the higher tier), evidence quality (the higher-tier signal exceeds a declared signal-to-noise threshold), and credential validity (the consumer is authorized to admit the higher tier under current policy). Failure of any single condition pins the entity at the next-lower tier rather than producing partial or ambiguous admission.

The composite evaluator runs continuously per neighboring entity within observation range. Each cycle produces a per-entity intent estimate with a fidelity tag, a confidence scalar, and a provenance record listing which tier-tagged observations contributed. Downstream planning consumes the estimate as a credentialed cognitive observation; the fidelity tag flows through to action selection so that low-fidelity attribution cannot drive high-stakes maneuvers without governance escalation.

Refresh cadence is tier-dependent. Tier 1 broadcasts refresh at the broadcast rate of the source. Tier 2 indicators refresh at the perception loop rate. Tier 3 inferences refresh on every kinematic update. The evaluator timestamps each contribution and decays older observations on a tier-specific time constant, so that stale Tier 1 broadcasts do not over-weight against fresh Tier 3 evidence when an agent has lost cooperative contact.

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 JADC2 embodiment, Tier 1 is friendly cognitive-state sharing under coalition credentials; Tier 2 is IFF and link-data extraction; Tier 3 is sensor-fusion inference for unidentified or adversarial tracks. The tier selector enforces that adversarial-classified entities are pinned to Tier 3 regardless of any apparent cooperative emission, preventing spoofed broadcasts from acquiring 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, promotion thresholds, and credential requirements are all expressed in the policy reference. A regulator or operator can adjust the tier ladder for a particular deployment without modifying the planning code. 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 separate fidelity (the structural quality of the source) from confidence (the instantaneous noise estimate). Conflating the two prevents the system from down-weighting an inferred attribution structurally even when its instantaneous noise is low. Tier-tagging keeps fidelity and confidence orthogonal so that policy can constrain action selection on fidelity independently of confidence.

Disclosure Scope

The disclosure covers the mechanism of resolving operator intent at one of N declared fidelity tiers under coarse-to-fine progression, with promotion gated on capability, evidence, and governance, and with cross-tier fusion through a composite admissibility evaluator that consumes tier-tagged credentialed observations. Embodiments described include three-tier reference deployments and parameterized N-tier variants in connected-vehicle, drone-swarm, defense, human-robot, and industrial settings.

The disclosure scope includes: the tier ladder itself; the per-entity tier selector; the gated-promotion policy combining capability, evidence, and credential checks; the cross-tier fusion in the composite admissibility evaluator; the orthogonal treatment of fidelity and confidence; the tier-tagged credentialed observation as the unifying interface; and the policy-bound nature of tier weights and promotion thresholds. The mechanism is part of the broader Cognition Patent and is intended to be claimed in coordination with related operator-intent intake mechanisms disclosed in this and companion filings.