tms-core-is-deterministic-and-conservative
IN derived (depth 2)
The TMS engine produces deterministic, terminating truth maintenance through uniform pure evaluation, guaranteed convergence, and conservative asymmetric failure semantics for missing nodes.
Summary
The truth maintenance engine will always produce the same results given the same inputs, will always finish running, and errs on the side of caution when information is missing. This matters because it means the system is predictable and safe — it won't loop forever, won't give different answers on repeated runs, and when in doubt about a dependency, it assumes the worst rather than guessing optimistically.
Justifications
SL — Three orthogonal engine properties combine into a single correctness guarantee: purity ensures no hidden state, termination ensures completion, and asymmetry ensures safe-by-default behavior
Antecedents (all must be IN):
- IN justification-evaluation-is-uniform-and-pure — All justification types (SL and CP) use the same validity rule (antecedents IN, outlist OUT), evaluated as a pure function with no side effects
- IN propagation-terminates-deterministically — Truth propagation is guaranteed to terminate: BFS prevents stack overflow, stop-on-unchanged prevents oscillation, and fixpoint iteration bounds the outer loop
- IN missing-nodes-have-asymmetric-fail-semantics — Missing nodes are treated asymmetrically: absent antecedents fail validation (conservative), absent outlist nodes pass (permissive), creating a "believe unless proven otherwise" default
Dependents
These beliefs depend on this one:
- IN reasoning-engine-is-deterministic-and-reversible — The TMS engine achieves deterministic reversible non-monotonic reasoning: truth maintenance produces predictable terminating results through uniform evaluation and conservative asymmetry, while every non-monotonic operation (challenge, kill-switch, supersession, dialectics) is inherently undoable through the single outlist primitive.
- OUT system-achieves-full-correctness — The system achieves correctness at every level: deterministic conservative truth maintenance, a single reversible primitive for all non-monotonic features, and data integrity spanning all architectural layers — the system is sound end-to-end.