/semantica:reason

Apply reasoning over the knowledge graph. Usage: /semantica:reason <mode> [args]

$ARGUMENTS = reasoning mode + rules/observations/query.

---

deductive [--facts '<json-list>'] [--rules '<rule1>|<rule2>']

Apply deductive rules to known facts to derive new conclusions.

from semantica.reasoning.deductive_reasoner import DeductiveReasoner, Premise

reasoner = DeductiveReasoner()

# Add base facts to working memory
# Facts can be strings like "Person(John)" or structured dicts
import json
facts = json.loads(facts_json) if facts_json else []
reasoner.add_facts(facts)

# Apply logic with explicit premises
# Premise objects have: statement, confidence, source
premises = [
    Premise(statement=fact, confidence=1.0)
    for fact in facts
]

conclusions = reasoner.apply_logic(premises=premises)

Return: | Conclusion | Triggering Premises | Confidence | Rule Applied |

If zero rules given, run reasoner.prove_theorem() on any provided theorem:

proof = reasoner.prove_theorem(theorem=theorem_text)

Output: Proof: <proof.steps> | Valid: YES / NO

---

prove <theorem> [--facts '<json-list>']

Prove or disprove a theorem against known facts.

from semantica.reasoning.deductive_reasoner import DeductiveReasoner

reasoner = DeductiveReasoner()
import json
reasoner.add_facts(json.loads(facts_json) if facts_json else [])

proof = reasoner.prove_theorem(theorem=theorem)

Output:

Theorem: "<theorem>"
Result:  PROVED ✓  |  DISPROVED ✗  |  UNDECIDABLE ⚠

Proof steps:
  1. <premise>, <justification>
  2. ...
  → QED: <theorem>

Confidence: <proof.confidence>

---

abductive <observation> [--knowledge '<json-list>'] [--top N]

Generate and rank hypotheses that explain an observation.

from semantica.reasoning.abductive_reasoner import (
    AbductiveReasoner, Observation
)

reasoner = AbductiveReasoner()