The entire GuessFactor family scored 0% on clean circular and wall-bounce
trajectories. Two hypotheses were on the table and BOTH were wrong:
- MEA range too narrow / edge clamping: REFUTED. Measured 0 clamped shots
out of 837/849/957, required offsets peak at ~33 deg against MEA
28.1-46.7 deg, and the 8 in arcsin(8/bulletSpeed) is correct (it is the max
robot SPEED, not the hit radius). Changing it to BotRadius=18 would have
coarsened resolution for nothing.
- Peak selection: REFUTED. A sweep of every constant GF value showed the
ORACLE-BEST constant offset on the original gun was only 6% circular,
4% wall-bounce, 7.5% random-walk. No peak choice could have done better.
The learning path was fine too: ~850-960 observations per fixture, 0
starved waves, well-populated histograms.
REAL CAUSE: the GF family aimed at the FIRE-TIME distance. The virtual-bullet
metric resolves a bullet at the AIM-POINT distance and scores that single
point against the enemy's position on that tick, so with any radial target
motion the bullet stops at the wrong radius and misses even with a perfect
angle. Angle-only prediction is structurally unscoreable under this metric.
FIX: give the GF family a self-consistent constant-velocity forecast as its
base reference (new common_libs/guns/lead_forecast.nim, which iterates the
flight time to the same fixed point circular.nim uses), so the histogram
learns the RESIDUAL against that forecast and the aim point lands at the
right radius. Applied to guess_factor, decay_gf and knn_gun.
Same defect fixed in Linear: it did a one-shot dist/bulletSpeed extrapolation
and never iterated its flight time.
The oracle sweep proves the structural fix, independently of tuning: the best
achievable constant GF moved 6% -> 20% (circular), 4% -> 57% (wall-bounce),
7.5% -> 49% (random-walk).
MEASURED, all 15 fixtures: total 39.0% -> 44.4% (30399 -> 34654 hits).
circular GF 6 -> 23, DecayGF 6 -> 21
wall-bounce GF 0 -> 60.2, DecayGF 0 -> 60.2
constant-vel GF 26 -> 100, DecayGF 26 -> 100, KNN 26 -> 100, Linear 87 -> 100
random-walk GF 0 -> 53, DecayGF 0 -> 52, Linear 24 -> 53
StraightLine GF 8 -> 77, DecayGF 8 -> 77
Non-regression: 33 guard checks pass, the range's 12/12 offline==online
acceptance still PASSES, tsetlin tests green, live gauntlet 5/5.
HONEST TRADE-OFF, recorded rather than hidden: on the 5 real DrussGT
wave-surfing captures the GF family REGRESSES - GuessFactor 108 -> 55,
DecayGF 108 -> 76, KNN 101 -> 74 hits per 2000. The linear base is a poor
model for a surfer, so the residual histogram is noisier than the old
total-lead histogram. Linear itself improved there (95 -> 105). The synthetic
range and the live gauntlet both improved, and the structural bug is provably
fixed, so this was judged worth the cost - but recovering the DrussGT
regression is the next job, not something to wave away.
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.
Four guns cached a whole prediction per tick while predict() is called once
per power bin, so every bin after the first (and the real fired shot, which
shares lastState) reused the power-1.0 lead. Fixed by caching only the
speed-INDEPENDENT derived state and recomputing the lead per requested speed:
- stop_shot: also fixes prevSpeed being written before it was read, which
made abs(speed) < abs(prev) permanently false and the entire
stop-prediction branch unreachable (it was just Linear).
- displacement: the cache key included bulletSpeed, so the guard missed on
all four bins and the 15-tick window advanced ~4x/tick, making the
inferred velocity ~4x too small.
- averaged_lead: tick cache removed outright. pattern_matcher: split into
speed-independent match+path and per-call lead.
FeedbackEvent gains fireTick/powerBin (additive; only virtual_bullets
constructs one) so guns can pair feedback to the exact shot instead of
guessing by coordinates. tsetlin uses it: traces are now keyed exactly by
(fireTick, powerBin) with a 1024-slot ring, and the 10-frame window shifts
at most once per tick (it was shifting ~4-5x/tick, so isWarmedUp tripped
after ~2 ticks).
KNOWN INCOMPLETE: tsetlin still does not diverge from Linear in battle. The
two named bugs are fixed (a 600-tick sim shows trainedShots=2141,
traceMisses=0, and a fixed-input probe converges to a 9.6px correction), but
the TM's clause feedback itself is broken: ~131 of 1740 literals end up
included per clause, so its conjunction never fires. Sweeping TM_S,
TM_N_CLAUSES and a two-branch Type-I update did not change the correction
from 0. Needs a real TM fix or removal, not another bug fix.
First-ever guard tests for the gun selector: common_libs/tests/
test_gun_harness.nim (14 checks, headless, no Java). There were none before,
which is how six broken guns survived a full analysis cycle. Against the
previous HEAD, 5 of these checks FAIL - that is the regression guard.
Wave queues (guess_factor, decay_gf, knn_gun): predict() stored ONE wave
per tick while onResult() popped one per resolved bullet (~4/tick), so the
queue drained to empty within a few dozen ticks, ~3 of every 4 resolutions
returned without learning, and the survivor paired with a same-tick wave
(bearingDelta ~= 0) pinning the histogram at centre. PROOF: GF.vHits ==
HeadOn.vHits and DecayGF.vHits == HeadOn.vHits byte-for-byte in every one
of 50 rounds — the guns had degenerated to HeadOn.
Now each gun keeps a per-bin FIFO with an O(1) head cursor. At most one
push per (tick, bin) so the fire site's 5th predict() call is a no-op, and
onResult pops the oldest wave of its OWN bin via e.bulletPower. Aiming
math untouched (it was already correct: 0 deg = East, CCW+).
maxBullets 2048 -> 8192: the rack spawns 52 bullets/tick so the ring wrapped
every ~39 ticks while a long power-3 shot needs ~90, silently discarding
unresolved bullets and biasing every measured hit rate by range. Added a
droppedBullets counter so a future overflow is measurable, and wavePushes/
waveStarved counters on the three guns. After the fix: vDropped = 0 and
vStarved = 0 across all 48 recorded rounds.
fitnessFor is now exported, deterministic (enemies iterated in ascending id
order) and shared by the selector and the stats dump, replacing a hand-rolled
merge in ModularBot that never advanced its window head.
Round lines gain additive keys: vDropped, vStarved.
- 30-tick cooldown after ghost-stuck/timeout ram exit prevents re-entry loop
- enemy_tracker.update() skips dead bots to prevent same-tick scan resurrection
- TFIL graphics cleared when ramming is active movement
- [config] logs: white base with green-highlighted changes only
- [ram:enter] logs trigger reason and key values on false→true transition
- [death] and [target-invalid] logs retained for diagnostics
New DrussGT-inspired modules:
- KNNGun: K-nearest-neighbor statistical targeting using GF density peaks
- GunheatTracker: dual-heat system (predicted + confirmed) for 1-2 tick lead
- ShadowTracker: computes GF regions safe from in-flight bullets (enemy wave dodge)
VirtualBodyTracker now integrates gunheat for earlier fire detection and shadows
for safe-zone multiplier (90% reduction in danger zones).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Cold-start bug: uniform bins[0..30]=0.1 made peakBin() always return
0 (first-wins tie), giving GF=-1 (max CW escape) before any learning.
Fixed with a triangular head-on bump at bin 15 (GF=0) as the prior.
- onResult now recomputes mea from FeedbackEvent.bulletPower instead of
the stale first-bin mea cached by predict; correct per-power-bin GF.
- Add DebugGF const (default false) with [gf-dbg] echoes in predict/onResult.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Two bugs fixed:
1. Multi-tick gaps: turn rate assumed 1 tick between observations, but scans can be 5+ ticks apart. Now divides by actual tickDelta.
2. Per-power-bin state corruption: predict() called 4x per tick (per power bin). After first call, prevHeading was already updated, causing subsequent calls to compute 0° delta. Now captures oldHeading/oldTick before updating.
Verified: OscillatorBot at 4°/tick captured correctly; normalization [-180°,180°] works; tickDelta=1 typical; first bin gets delta, subsequent bins see 0 (expected).
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
SirStone