What is BDI architecture in AI?

BDI (Belief-Desire-Intention) is a software model for developing intelligent agents that mimics human reasoning by separating what an agent knows, what it wants to achieve, and what it is committed to doing.

How does the T2B2T paradigm work?

T2B2T stands for Triples-to-Beliefs-to-Triples. it involves translating external RDF triples into internal agent beliefs, performing cognitive reasoning, and then projecting the resulting intentions back into RDF format.

Can this skill help with AI explainability?

Yes, it links every mental entity to explicit Justification instances and reasoning chains, allowing you to trace an agent's actions back to its specific perceptions and motivations.

What is Logic Augmented Generation (LAG)?

LAG is a technique used in this skill to ground LLM outputs in ontological constraints, ensuring that the generated content remains logically consistent with the agent's internal mental state.

BDI Mental State Modeling

Name: BDI Mental State Modeling
Author: aiskillstore

byaiskillstore

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114

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Data Science & ML

Transforms RDF context into Belief-Desire-Intention (BDI) architectures to enable formal cognitive reasoning and rational agency in AI agents.

This skill provides a robust framework for modeling agent mental states within Claude Code, bridging the gap between raw RDF data and sophisticated cognitive reasoning. By implementing the BDI (Belief-Desire-Intention) ontology, it enables developers to build agents that perceive world states, deliberate over desires, and commit to intentions through traceable reasoning chains. This is particularly useful for neuro-symbolic AI, multi-agent systems, and Logic Augmented Generation (LAG), where explainability and formal logical grounding are critical for performance and safety.

Key Features

01114 GitHub stars

02Temporal validity tracking for mental states and belief evolution

03Traceable justification chains for explainable AI decision-making

04Formal BDI ontology implementation for autonomous rational agency

05T2B2T (Triples-to-Beliefs-to-Triples) bidirectional state transformation

06Logic Augmented Generation (LAG) to constrain LLM outputs with symbols

Use Cases

01Creating explainable AI agents that can justify actions via belief-chains

02Building autonomous multi-agent systems with semantic interoperability

03Implementing neuro-symbolic architectures for complex deliberative reasoning

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add aiskillstore/marketplace muratcankoylan

For use in Claude.ai and ChatGPT

Download Skill