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.
a contextualized first-order logic knowledge base with a justified-inference reasoning engine
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/
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
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.
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.
Truth maintenance for AI knowledge bases — conventions, tooling and a multi-agent method to keep a Markdown KB verified, fresh and honest. Zero dependencies.
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