pith:WSFJUP5Z
Paraconsistent Semantics for Extended Fuzzy Logic Programs via Approximation Fixpoint Theory [Extended Version]
Approximating fixpoint theory defines paraconsistent semantics for fuzzy logic programs containing both negation as failure and strong negation.
arxiv:2605.05286 v2 · 2026-05-06 · cs.LO
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Record completeness
Claims
we use the framework of approximating fixpoint theory to formulate well-behaved semantics for fuzzy logic programs containing both 'by-failure' and strong negation. We show that this framework generalizes several existing semantics as well as giving rise to a host of new semantics.
That approximation fixpoint theory can be directly applied to fuzzy logic programs with both kinds of negation while ensuring the resulting semantics are well-behaved and paraconsistent without additional unstated restrictions.
Paraconsistent semantics for fuzzy logic programs containing both negation as failure and strong negation are formulated using approximation fixpoint theory, generalizing prior semantics and yielding new ones.
Receipt and verification
| First computed | 2026-05-25T02:02:16.303878Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
b48a9a3fb9b4026826ce3939c50cbe843fea20278ca7700313c92b0c4f120f5f
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/WSFJUP5ZWQBGQJWOHE44KDF6QQ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
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Canonical record JSON
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