Task 1 - the acceptance proof was unrunnable because RecordWorldState was a
compile-time const set to false. It is now a RUNTIME switch
(let RecordWorldState* = existsEnv("TR_RECORD_WORLDSTATE")), default OFF, so
ordinary runs write no fixture, and acceptance_offline_vs_online.nim enables
it for the battle it spawns and clears it afterwards. Restored and run twice:
12/12 deterministic guns match exactly (128-tick and 546-tick battles), with
Tsetlin reported separately as stochastic. Both nimble build variants clean.
Task 2 - does a compact encoding turn the TM's 99.35% into a READABLE rule?
Measured across window sizes (fixed seed, no tuning):
frames TEST acc eff.lits/clause firing clauses counterfactual low/high/mean
10 99.35% 152.8 37 100/24/62.4%
3 95.94% 54.9 35 96/20/58.6%
2 99.48% 39.6 38 95/25/60.7%
1 98.30% 19.2 45 100/24/62.3%
So 2 frames is strictly better than 10 on BOTH axes: +0.13 accuracy for 4x
smaller clauses. The 3-frame dip is non-monotonic and left unexplained rather
than smoothed over.
A readable rule WAS partially recovered. Five clauses carry the exact Gray
form !g10 ^ !g9 ^ !g8; g10 is inert in this data, so the effective rule is the
2-literal proposition !g9 ^ !g8, i.e. energy < 25.6. That is a genuine
threshold in readable propositional form - but at 25.6, NOT the labelled 30,
because 256 is a power-of-two Gray boundary expressible in two literals while
300 needs a longer conjunction. The TM found the nearest SIMPLE threshold.
The honest caveat: that threshold is not the ensemble's decision mechanism.
The counterfactual follow rate (high 24%, mean 62.3%) is statistically
identical at 1, 2 and 10 frames, so compactness did not make the model read
energy - its vote is carried by co-occurring bearing/velocity/heading/wall
literals. Also identified: clauses containing all 11 Gray energy bits are
satisfied at exactly one raw value (50, the dataset floor), so they are
'energy has hit the floor' detectors, not thresholds.
Methodological fix worth keeping: the earlier single-frame counterfactual
wrote energy into all 10 frame slots including the zeroed ones, reviving dead
clauses and producing a spurious 2% high-follow rate. setEnergyFrames now
rewrites only the exposed frames; the corrected figure is 24%.
The gun has never contributed anything: Tsetlin.vHits was byte-for-byte
equal to Linear.vHits in every measured round of every run, because its
learned correction was always exactly 0.
Six diagnosed defects fixed, plus one that was required to make the first
one work:
1. Type I now conditions on the clause output. It previously rewarded
included true literals unconditionally, omitting Granmo's (c=0, lk=1)
-> toward Exclude counter-force, so true literals ratcheted toward
Include forever. This was the root cause of the saturation.
2. Type II was unreachable dead code: its guard required cOut==1 AND
lits[lit]==0 AND st>0 (included), but cOut==1 guarantees every included
literal is 1. Its direction was wrong too - it should increment EXCLUDED
false literals when the clause fires.
3. Resource allocation restored: Granmo's (T - clip(v,-T,T))/(2T) target
replaces |error|/(2*RESID_MAX); TM_T was only an output normaliser.
4. Label baseline fixed - the factor-2 shrink. predX = linearX + cx, so the
label was delta - cx while the learner's output IS cx, giving
error = delta - 2cx and a fixed point of cx = delta/2: HALF the needed
correction even with perfect feedback. TmTrace now stores linearX/linearY
and training uses delta.
5. Hits no longer zero their label (a hit means |miss| < 18px, not 0).
6. The enemy-energy feature was duplicated - tmEncodeFrame passed
state.selfEnergy with a stale comment claiming enemyEnergy was absent,
while WorldState.enemyEnergy exists. Enemy-energy rules were literally
unrepresentable.
7. REQUIRED EXTRA: tmEvalClause now implements Granmo Eq. 6 - an all-Exclude
clause outputs 1 during learning and 0 during classification. Without it,
fix#1 deadlocks every clause at empty.
MEASURED EFFECT (energy-threshold-turner fixture, seed 1):
mean included literals per active clause 714.0 -> 13.8
active clauses 100/100 -> 53/100
nonzero corrections 8/764 -> 708/764
Tsetlin virtual hits (Linear = 27/400) 27/400 -> 69/400
Divergence achieved: offline on 7/8 fixtures, and in a live gauntlet
(RandomMover: Tsetlin 199/1200 vs Linear 288/1200, vDropped=vStarved=0).
Tsetlin now LEARNS but is not yet competitive with Linear - the regression
head is untuned, flagged as follow-up rather than claimed as a win.
Also ignores compiled test harnesses that have no file extension, which the
existing '**/tests/test_*' rule misses.
SirStone