UK Patent Application
Applicant: Neil Grassby
AIEP --- Deterministic Autonomous Problem Generation and Exploration Engine
Abstract
A computer-implemented method and system for autonomous generation of candidate problem statements from evidence patterns within a governed reasoning substrate operating under an Architected Instruction & Evidence Protocol (AIEP). The engine detects ProblemGenerationConditions comprising evidence density anomalies, unexplained variance clusters, and schema-predicted but unobserved evidence patterns. Upon condition detection, a candidate ProblemArtefact is automatically generated encoding the identified problem statement without external instruction. ProblemArtefacts are subject to full constitutional arbitration and may be elevated to GoalVectors upon sufficient evidence justification. A ProblemGenerationHash encodes the detection conditions. The invention provides deterministic autonomous problem identification as a precursor to GoalVector generation.
Description
Field of the Invention
[0001] The present invention relates to governed distributed reasoning substrates and more particularly to autonomous generation of candidate problem statements from evidence pattern analysis within an Architected Instruction & Evidence Protocol (AIEP) system.
Background to the Invention
[0002] Governed substrates accumulate evidence artefacts across domains. Anomalous evidence patterns, unexplained variance clusters, and systematic gaps between schema-predicted and observed evidence represent candidate problems that, when identified and formalised, may become the source of GoalVector generation.
[0003] Existing systems do not provide a mechanism for autonomously detecting these conditions, generating structured ProblemArtefacts encoding them, and routing them through constitutional arbitration toward GoalVector elevation.
[0004A] Boundary Note (Cross-check E): The monitoring in the present invention evaluates evidential PATTERN CONDITIONS in the Evidence Ledger — density anomalies, unexplained variance, and systematic absence of schema-predicted evidence. This is distinct from cross-ledger referential integrity monitoring which verifies that Reasoning Ledger entries reference valid EvidenceHash identifiers. The present invention identifies conditions of evidential insufficiency and absence — not referential inconsistency between ledgers.
[0004] There exists a need for a deterministic problem generation engine that produces structured, governed candidate problem statements from evidential pattern analysis without external instruction.
Summary of the Invention
[0005] According to a first aspect, there is provided a method for autonomous problem generation comprising:
(a) monitoring substrate evidence ledger for ProblemGenerationConditions comprising: evidence density anomalies exceeding schema-defined thresholds; unexplained variance clusters within evidential domains; and schema-predicted evidence patterns that are absent or systematically under-evidenced;
(b) upon ProblemGenerationCondition detection, automatically generating a ProblemArtefact encoding the problem statement without external instruction;
(c) computing a ProblemGenerationHash over the canonical ProblemArtefact representation and detection conditions;
(d) subjecting the ProblemArtefact to constitutional arbitration including plausibility evaluation;
(e) appending the ProblemArtefact to append-only lineage; and
(f) enabling elevation of a sufficiently evidenced ProblemArtefact to a GoalVector upon satisfaction of evidence-weight elevation thresholds.
[0006] According to a second aspect, there is provided a system comprising: a ProblemGenerationCondition monitor; a ProblemArtefact generator; a ProblemGenerationHash module; a constitutional arbitration interface; and a GoalVector elevation controller.
Detailed Description of Preferred Embodiments
1. ProblemGenerationCondition Detection
[0007] The condition monitor evaluates the evidence ledger at each execution cycle against schema-defined anomaly thresholds.
[0008] Evidence density anomaly detection identifies substrate regions where evidence accumulation rate departs significantly from schema-predicted norms.
[0009] Variance cluster detection identifies unexplained systematic variance in evidence-weight distributions across related artefact groups.
[0010] Gap detection identifies schema-predicted evidence types that are systematically absent or under-evidenced.
2. ProblemArtefact Generation
[0011] A ProblemArtefact comprises: problem statement encoding in canonical form; reference to detecting condition instances; evidence support artefact references; and an initial plausibility assessment.
[0012] Generation is automatic and deterministic: identical conditions produce identical ProblemArtefacts.
3. GoalVector Elevation
[0013] A ProblemArtefact may be elevated to a GoalVector when: its evidence-weight exceeds an elevation threshold; and a constitutional arbitration cycle confirms the problem represents a genuine unresolved incompatibility.
[0014] Elevation appends a GoalVector generation record with lineage reference to the originating ProblemArtefact.
Technical Effect
[0015] The invention provides autonomous problem identification as a deterministic precursor to GoalVector generation.
[0016] It extends self-directed intelligence capabilities by enabling the substrate to identify problems before they become GoalVector-generating incompatibilities.
[0017] It provides constitutional governance over autonomous problem generation.
CLAIMS
1. A computer-implemented method for autonomous problem generation in a governed reasoning substrate, comprising: monitoring the substrate evidence ledger for ProblemGenerationConditions — evaluating evidential pattern conditions in evidence content, distinct from cross-ledger referential integrity verification — comprising evidence density anomalies, unexplained variance clusters, and absent schema-predicted patterns; automatically generating a ProblemArtefact encoding the identified problem upon condition detection; computing a ProblemGenerationHash over the ProblemArtefact and detection conditions; subjecting the ProblemArtefact to constitutional arbitration; and enabling elevation to a GoalVector upon evidence-weight threshold satisfaction.
2. The method of claim 1 wherein ProblemArtefact generation is automatic and requires no external instruction.
3. The method of claim 1 wherein identical detection conditions produce identical ProblemArtefacts across distributed nodes.
4. A system for autonomous problem generation comprising: a ProblemGenerationCondition monitor; a ProblemArtefact generator; a ProblemGenerationHash module; and a GoalVector elevation controller.
5. A non-transitory computer-readable medium storing instructions which, when executed, perform the method of claim 1.
Abstract
An autonomous problem generation engine for AIEP governed substrates. Evidence density anomalies, variance clusters, and schema-predicted gaps are monitored as ProblemGenerationConditions. ProblemArtefacts encoding identified problems are automatically generated upon detection and hashed via ProblemGenerationHash. Constitutional arbitration governs ProblemArtefact validity. Sufficiently evidenced ProblemArtefacts may be elevated to GoalVectors. The invention provides deterministic autonomous problem identification as a precursor to self-directed inquiry.
Brief Description of the Drawing
FIG. 1 — Branch Structure and Pruning
┌──────────────┐
│ Root State │
│ (canonical) │
└──────┬───────┘
┌───────────┼───────────┐
┌──────▼──────┐ │ ┌───────▼─────┐
│ Branch B1 │ │ │ Branch B2 │
│ viable │ │ │ stale → │
└──────┬──────┘ │ │ PRUNE │
│ │ └─────────────┘
┌──────▼──────┐ │
│ Branch B1a │ │ ┌─────────────┐
│ (deepened) │ └───▶│ Branch B3 │
└─────────────┘ │ candidate │
└──────┬──────┘
│
┌────────▼────────┐
│ Selected Path │
│ hash-committed │
└─────────────────┘