Compare Designs Under Constraints
Keep only acceptable inspection models
Tighten the permitted miss count
Botie has measured candidate latency and misses on comparable fixtures. A hard failure cannot be compensated by excellence on the other measurement.
BOTIE
The inputs we are comparing
Latency statistic by candidate (ms)
Same in both cases
- 060
- 190
- 2120
- 380
Misses on the same faulty-part set
Same in both cases
- 05
- 13
- 21
- 34
Maximum latency (ms)
Same in both cases
100
Maximum miss count
Two feasible candidates
4
Tighter miss limit
2
Result for two feasible candidates
- 00
- 11
- 20
- 31
The fastest candidate misses too many faults
This check uses the pieces from “Require each candidate to pass both limits”.
BOTIE
- Latency statistic by candidate (ms)
- 040
- 1100
- 2130
- 390
- Misses on the same faulty-part set
- 06
- 12
- 20
- 33
- Maximum latency (ms)
100
- Maximum miss count
2
Use a number, text, or an array matching the requested result. For a matrix, put each row in brackets. Saving your prediction runs your current construction on these inputs and checks the earlier examples too.
Build a construction in the workspace above first. This case will test those pieces.
What additional evidence could justify changing the candidate set or revisiting a requirement?
Filter by hard requirements before trading off acceptable designs, and preserve uncertainty behind the measurements.
These sample measurements do not establish future guarantees. Compare uncertainty, operating conditions, and any omitted requirements.
Work through the Python challenges →