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

Ciencia de Datos y ML

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

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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.

Características Principales

114 GitHub stars
Temporal validity tracking for mental states and belief evolution
Traceable justification chains for explainable AI decision-making
Formal BDI ontology implementation for autonomous rational agency
T2B2T (Triples-to-Beliefs-to-Triples) bidirectional state transformation
Logic Augmented Generation (LAG) to constrain LLM outputs with symbols

Casos de Uso

Creating explainable AI agents that can justify actions via belief-chains
Building autonomous multi-agent systems with semantic interoperability
Implementing neuro-symbolic architectures for complex deliberative reasoning

Acerca de

Características Principales

114 GitHub stars
Temporal validity tracking for mental states and belief evolution
Traceable justification chains for explainable AI decision-making
Formal BDI ontology implementation for autonomous rational agency
T2B2T (Triples-to-Beliefs-to-Triples) bidirectional state transformation
Logic Augmented Generation (LAG) to constrain LLM outputs with symbols

Casos de Uso

Creating explainable AI agents that can justify actions via belief-chains
Building autonomous multi-agent systems with semantic interoperability
Implementing neuro-symbolic architectures for complex deliberative reasoning