debugging biological networks to reach coherence, completeness and consistency
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Updated
Feb 18, 2024 - Common Lisp
debugging biological networks to reach coherence, completeness and consistency
Haskell and Scala translations of Truth Maintenance Systems and other tools from Forbus and de Kleer's Building Problem Solvers.
Extracts every assumption your coding agent made, links each one to the code it justifies, and tells you which ones broke. Stdlib only.
A logic-based Truth Maintenance System (LTMS) and pattern-directed reasoning engine in pure Python, after Forbus & de Kleer's Building Problem Solvers. Docs: https://pisanuw.github.io/ltms/
Commitment tracking for LLM agents — a normative overlay that gives long-running agents a scoreboard of their own commitments, not just a memory of what happened.
Verified knowledge for AI agents. Compress context, extract and store facts, define rules, and ask questions — get deterministic answers with proof, not LLM guesses. Connect agents via MCP, Python SDK, TypeSc
Towards a Modern Truth-Maintenance System and Inference Engine
A decision ledger for long-running agent work; like git but for LLM decision making
An negation assumption based truth maintenance system (NATMS)
An R&D substrate & toolkit for LLMs to build external reasoning layer - Providing versioned long-term memory, deterministic, incremental & critical reasoning, truth maintenance & various cool stuffs
Stops AI agents from acting on stale plans, with dependency-aware leases, fenced commits, and stale-safe LangGraph resume.
Governed beliefs for LLM agents: an append-only ledger, SMT-checked consistency, defeasible revision, and calibration instruments.
Truth maintenance for AI knowledge bases — conventions, tooling and a multi-agent method to keep a Markdown KB verified, fresh and honest. Zero dependencies.
A truth maintenance system for AI coding agents — one graph over code, intent, plan, and the why: every belief justified, anchored to the code it concerns, and flagged when it stops holding. pip install yigraf
Epistemic truth maintenance and premise-validation skills for AI coding agents
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