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bdi-mental-states

Transform RDF context into BDI mental states for cognitive agents.

947|163|Updated Jan 13, 2026
Search Tags:#ontology#reasoning#cognitive architecture#rdf#bdi#agent modeling#explainable ai
One-click install
npx skills add https://github.com/guanyang/antigravity-skills --skill bdi-mental-states-guanyang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/guanyang/antigravity-skills/tree/main/skills/bdi-mental-states
Command: npx skills add https://github.com/guanyang/antigravity-skills --skill bdi-mental-states-guanyang

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill enables the modeling of agent mental states (beliefs, desires, intentions) by transforming external data into formal cognitive representations, facilitating rational agency and explainability.

Core Features & Use Cases

  • BDI Architecture Implementation: Create belief-desire-intention models for cognitive agents.
  • RDF to Belief Transformation: Convert external RDF context into internal agent beliefs.
  • Use Case: Build a cognitive agent that perceives its environment, forms beliefs about it, develops desires based on those beliefs, and commits to intentions to act, all represented using formal BDI ontology patterns.

Quick Start

Use the bdi-mental-states skill to model agent mental states from provided RDF context.

Frequently Asked Questions about bdi-mental-states

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I model agent mental states using the BDI architecture from RDF data?▼

You can model agent mental states by transforming external RDF context into internal beliefs, desires, and intentions. The BDI architecture enables rational agents to deliberate and act based on formal cognitive representations.

What is the T2B2T paradigm in BDI agent modeling?▼

The T2B2T paradigm handles BDI ontology patterns by transforming external context into internal beliefs and back to external actions. It ensures semantic interoperability and supports explainable AI for rational agents.

How does BDI reasoning support explainable AI for cognitive agents?▼

BDI reasoning supports explainable AI by structuring agent decisions through formal beliefs, desires, and intentions. This cognitive architecture transparently maps deliberative processes to semantic ontology patterns.

Can I integrate BDI mental states with agent frameworks like SEMAS or JADE?▼

Yes, BDI mental states integrate with agent frameworks like SEMAS and JADE. This integration allows rational agents to process RDF context and execute deliberative reasoning within established cognitive architectures.

How do I convert external RDF context into internal agent beliefs?▼

You convert external RDF context into internal agent beliefs using BDI ontology patterns. This transformation enables cognitive agents to form desires and commit to intentions based on perceived environmental data.

What are the limitations of using BDI ontology patterns for rational agents?▼

BDI ontology patterns require structured RDF context for accurate belief transformation. Without proper semantic interoperability or deliberate reasoning frameworks, rational agents may struggle to form coherent intentions.

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