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bdi-mental-states

Transform RDF context into BDI agent mental states using formal ontology patterns.

Updated Mar 22, 2026
Search Tags:#ontology#multi-agent systems#neuro-symbolic#sparql#rdf#bdi#cognitive agent
One-click install
npx skills add https://github.com/TECH-HY/SKILLS --skill bdi-mental-states-tech-hy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/TECH-HY/SKILLS/tree/main/skills/bdi-mental-states
Command: npx skills add https://github.com/TECH-HY/SKILLS --skill bdi-mental-states-tech-hy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Modeling agent cognition with beliefs, desires, and intentions requires a formal, interoperable structure that ad-hoc data models cannot provide. This Skill converts external RDF context into traceable BDI mental states, enabling deliberative reasoning, explainability, and semantic interoperability in multi-agent systems. ## Core Features & Use Cases - RDF-to-Belief Transformation: Implements the T2B2T (Triples-to-Beliefs-to-Triples) paradigm to convert external RDF context into beliefs and project deliberation results back to RDF. - Formal BDI Ontology Patterns: Provides Turtle/OWL patterns for beliefs, desires, intentions, plans, tasks, justifications, and temporal validity intervals aligned with DOLCE and established ODPs. - Explainability & Validation: Links mental entities to Justification instances and validates implementations with SPARQL competency questions. - Use Case: When building a cognitive agent that receives a payment-request notification as RDF, use this Skill to model the triggered belief, the generated desire, the committed intention, and the executed payment plan with full justification traces. ## Quick Start Use the bdi-mental-states skill to model an agent's beliefs, desires, and intentions from the attached RDF context and generate the corresponding Turtle ontology instances.

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 BDI agent mental states in RDF?▼

Model beliefs, desires, and intentions as endurant classes linked by properties like motivates, fulfils, and isSupportedBy. Ground each mental state in a WorldState instance and connect intentions to plans containing ordered tasks.

What is the T2B2T paradigm in BDI agents?▼

T2B2T (Triples-to-Beliefs-to-Triples) is a bidirectional flow between RDF knowledge graphs and internal mental states. External RDF triggers belief formation processes, and after BDI deliberation, intentions and plan executions are projected back as new RDF triples.

Can BDI ontology integrate with LLMs and neuro-symbolic AI?▼

Yes, through Logic Augmented Generation the ontology is serialized into the LLM prompt, and extracted RDF triples from the response are validated against the ontology for consistency. Inconsistent outputs trigger retry with feedback.

Does the BDI ontology work with JADE or JADEX frameworks?▼

The ontology patterns support BDI frameworks including SEMAS, JADE, and JADEX. SEMAS integration maps ontology concepts to executable production rules where beliefs trigger desire generation and desires trigger intention commitment.

How do I track temporal evolution of agent beliefs?▼

Associate every mental state with a TimeInterval via hasValidity, with start and end TimeInstants. Query active mental states at any moment using SPARQL filters comparing the interval bounds against a target timestamp.

What are common mistakes when modeling BDI mental states?▼

Common anti-patterns include conflating mental states with world states, omitting temporal validity bounds, using flat belief structures instead of compositional hasPart modeling, and mapping intentions directly to actions instead of through plans and tasks.

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