The example computes eight, three, one, then zero surviving worlds as clauses are added. R,S,T represent three pit variables. R OR S plus NOT T leaves W010,W100,W110: T is pit-clear while R/S remain unknown. Add NOT R and only W010 remains. Add R as well and the whole model is inconsistent, with no per-cell clearance.
Define the universe explicitly
Use all eight Boolean assignments in R,S,T order, with IDs W000 through W111. True means pit present in this bounded model; false means absent. These are variable names rather than grid coordinates, and no hidden world is supplied to the inference calculation. With no clauses, every variable has both values among the candidates, so all are unknown. Candidate counts are possibilities, not equal-probability outcomes; no prior or probability model has been provided.
Clause C1 is inclusive R OR S: at least one, potentially both, has a pit. C2 is NOT T. Together they leave W010,W100,W110. T is false in every surviving world, while each of R and S appears both false and true. Do not select the first row as the real world or convert the frequency of a value into a risk estimate. The conclusion follows all candidates, not a guess about which one secretly describes an environment.
Add and remove evidence
C3 NOT R leaves only W010. Now R and T are pit-clear under the supplied pit model, and S is a proven pit. The S conclusion comes from combining C1 with C3, not from reading a secret map. Add C4 R while retaining NOT R: no assignment survives. Display INCONSISTENT for the entire knowledge base and block every clearance gate. Keep the full contradictory clause list visible rather than deleting the inconvenient observation to make the display green.
Check for zero candidates before any all-values test. Python all on an empty iterable returns true; that language behavior must not become an operational approval that every cell is clear. For a nonempty set, classify all false as pit-clear, all true as proven pit, and mixed values as unknown. The application deliberately reports whole-model inconsistency separately from ordinary unknown cells. A malformed clause is another distinct problem: reject it for correction instead of treating it as either evidence or a normal contradiction.
Recompute from the complete universe
Remove C4 and rebuild using C1,C2,C3: W010 returns. Filtering the previous empty set cannot recover it. Remove C3 too and the original three candidates return. Reset with no clauses restores all eight. After each clause change, recalculate from all eight assignments in the fixed universe. Retain previous native traces under their original clause versions so the contradictory case is not rewritten as though it never occurred. Changing a variable's meaning requires a new model version as well.
The browser aid performs this bounded calculation in JavaScript, not Python or an autonomous game agent. To port it, enumerate Boolean triples, validate the allowed clause IDs, filter from the universe, test emptiness, then classify by candidate values. Native tests should compare world IDs and classifications, not just counts. Pit-clear says nothing about Wumpus presence, routes, orientation, gold or other hazards. It is a qualified inference result under supplied clauses, never a generic safe badge or an instruction to move automatically.
Make your own working example
Build an original possible-world pit kernel demonstration in JavaScript, explicitly not Python execution or a full Wumpus game. Enumerate R/S/T Boolean worlds W000 through W111. Provide buttons for K0 no clauses; K1 C1 R OR S plus C2 NOT T; K2 adds C3 NOT R; K3 adds C4 R; Remove C4 from the current validated list; Reset. Also provide a bounded comma-separated clause-ID field and Evaluate.
K0 leaves 8 unknown worlds. K1 leaves W010,W100,W110, T pit-clear and R/S unknown. K2 leaves W010, R/T pit-clear and S proven pit. K3 leaves 0 and displays whole-model INCONSISTENT with no clearance. Removing C4 preserves every other current clause and recomputes from all 8; from K3 this restores W010. Invalid current lists are held without replacement. Reset returns 8.
Validate C1-C4 only, rejecting unknown or duplicate IDs. Do not evaluate arbitrary user code. Classify only a nonempty set: all false clear, all true pit, mixed unknown. Show active clauses, exact candidate IDs and qualified labels together. Explain Python all([]) hazard without executing Python. Counts are not probabilities, and pit-clear does not certify any other hazard or authorize movement.
Use one self-contained HTML file with no remote dependency. Keep the initial sample, output, and labeled controls near the top. Use at least 16px text, strong contrast, visible focus, and a stacked layout at 360px without horizontal overflow. Use explicit type=button click handlers and explicit DOM selectors; do not depend on native form submission. Clear stale feedback when task inputs change. Render entered text safely. Keep practice data local to the current page; do not imply saving, sending, or production integration. Return only the finished HTML.
Review the contradictory trace
Keep native Python and its clause-version tests authoritative. In NOS, eligible static trace explanations can be revised visually without asking AI to regenerate the HTML. Clarify why zero candidates block clearance before using the reading copy to explain the native kernel.