Systematic Industrial Assurance
A proof about a timer matters to a machine builder when the installed timer behaves as the proof assumes. Controller, firmware, settings and operating conditions give that theorem an address in the world.
I’m building Symbolia to keep that address intact as machines change. Its world graph will connect what we know about components to the installations that depend on them. Each job will leave knowledge the next can use.
Preserve what makes a claim applicable
| Record | What travels with it |
|---|---|
| Requirement | The human intent and reviewed contract |
| Implementation | The exact build and the model used to reason about it |
| Component contract | The behaviour promised and assumptions required |
| Proof or analysis | The property, checker, model and bounds |
| Measurement | The equipment, place, time and conditions observed |
| Configuration | The versions and settings in use |
| Approval | Who accepted which evidence, for what purpose |
A source records what someone says. A declared premise states what a calculation assumes. A proof establishes its specified claim within a model. A measurement supplies evidence from the world. Approval accepts evidence for a named use. The graph preserves those distinctions through every transfer.
Changed premises trigger reassessment. Earlier versions remain available to explain earlier decisions.
Where Symbolia compounds
One sound controller model can serve many routines. A component contract can spare the next engineer a fresh specification. A commissioning fault can reveal an assumption shared by several installations.
I expect this inheritance to become Symbolia’s central asset. Dierickx and Cool describe how accumulated assets can reinforce one another, resist imitation, erode or be displaced. Which of those forces governs this business is something I must measure. MM09 MM10
The measure is work saved after checking applicability and paying for maintenance. A million graph entries mean little if every new machine still needs a million explanations.
Invest in reusable capability
Two experiments will test the graph. The first asks whether a compatible new deployment receives equally justified assurance with less total work. The second asks whether further investment in mapping and evidence improves later work enough to repay construction, review and maintenance. A useful library can still be expensive to expand.
Each mapping batch will retain its sources, accepted assets, rejected work and actual costs. I’ll evaluate its library release on cases held out from construction, with the evaluator, model, requirements and resources fixed within each comparison. A case used to improve the next batch becomes development material; fresh cases must test that batch.
Some investments will disappoint. A contract may never apply; an exception may cost more to maintain than it saves. The programme will direct further spending towards assets that earn their place, leaving unknown costs visible.
Establish and organise the library
Customer evidence belongs within its authorised project. Access must hold through every dependency a query follows, including counts and ranking information that might reveal another customer.
A reusable contract needs an owner, exact dependencies, permitted uses, applicable versions and independent review. Sharing requires both parties to approve the exact material crossing the boundary. Imported evidence retains its meaning; the receiving engineer must still establish that it fits the new installation. Agents can propose assets. They cannot approve their own interpretation.
Retrieval finds candidates. The relevant checker decides whether a result applies. An unsuccessful search establishes only that the search missed.
The private prototype has exercised bounded transfer across project scopes. Publication into a shared world library still needs a governed way to clear eligible knowledge for reuse. Production sharing and reuse on new deployments remain to be qualified.
What a competitor must reproduce
Competitors can buy the same models, use public proofs and hire engineers. Equipment vendors may know their products better than I do. Customers can export their own evidence.
The advantage I expect lies in knowledge that is difficult to acquire and useful repeatedly. A firmware deviation may be absent from the manual. A stopping-time measurement belongs to particular conditions. A failure investigation may expose an interaction nobody had modelled.
A rival can obtain access, repeat the experiment or choose a design that avoids the missing fact. The question is what equivalent, justified coverage costs them. Private knowledge used only once can sustain a service relationship without building a reusable asset.
Computational irreducibility
Discovery, proof checking and establishing physical applicability have different costs. Lean separates proof construction from checking through a small kernel; supported artifacts can be checked by independent implementations. Reuse should spare the next customer the original search. MM11
Time spent building a graph establishes no lower bound on reproducing its value. A better abstraction may bypass much of that history. P versus NP does not settle Symbolia’s competitive position. MM12
Where evidence requires equipment access, operating history and repeated validation, acquisition can remain slow while code generation accelerates. I’ll measure the cost of equivalent accepted coverage, rather than the cost of copying every edge.
Test the graph against better AI
After the second-arm comparison, I’ll test unseen customer changes under three conditions: the maintained graph plus the strongest available model; the same model with public tools and documentation; and that baseline with the graph’s additional evidence supplied as ordinary documents. The third condition separates access to facts from the value of their organisation.
All three receive the same customer-permitted project information. Freeze the useful property set, match compute and total specialist budgets, and have an independent reviewer assess coverage and evidence quality. Repeat after model upgrades with the same model available to every condition.
Count missed and confirmed defects, unresolved obligations, false rejections, preparation, repair, review and maintenance. The test must transfer a contract to a different implementation, configuration or operating condition. Repeating an identical cached check is a control.
I expect the graph to make better AI more useful. If its contribution disappears, I’ll retire the assets whose upkeep no longer earns its cost.
Maintenance is the asset
Customers will control their confidential evidence. Reuse of generic adapters, templates and de-identified failure knowledge needs explicit agreement. Technical access grants no commercial reuse right.
Exportable evidence and openly checkable results will let customers examine our work and move between tools. Symbolia must earn their return through usefulness. Its accounts must include proof repair, obsolete contracts, missing dependencies and recovery work.
The graph I want to build will carry hard-won knowledge forward while staying answerable to the machines it describes.
What my simulation has established so far
The internal energy-network study registered fifteen obligations, checked three arithmetic results and kept twelve gaps open. Repeating the transfer reused all three results without another worker execution. The workflow also exposed errors in numerical meaning, source bindings and applicability. SY01
Study-side adapters exercised screening, premise-domain and pricing examples without changing the retained core. Some remained proposals outside the governed claim tree. These are simulation and integration results; robot qualification, physical validation and total labour savings remain ahead. SY02
Illustrative dependency model
What survives a change?
Select a change to see which claims need fresh evidence. This small example illustrates how I want assurance to stay useful as a machine changes.