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bdi-mental-states

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

Updated Apr 17, 2026
Search Tags:#ontology#multi-agent systems#neuro-symbolic#sparql#rdf#bdi#mental states
One-click install
npx skills add https://github.com/Syedyasir001/RVULibPass --skill bdi-mental-states-syedyasir001
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/Syedyasir001/RVULibPass/tree/main/.agent/skills/library/bdi-mental-states
Command: npx skills add https://github.com/Syedyasir001/RVULibPass --skill bdi-mental-states-syedyasir001

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires rdflib, and includes references (resource) components.

What problem does it solve? Modeling agent cognition with beliefs, desires, and intentions requires a formal structure that most ad-hoc implementations lack, making reasoning opaque, untraceable, and incompatible across multi-agent systems. This Skill provides ontology-grounded patterns for converting external RDF context into structured BDI mental states with explainability and temporal tracking. ## Core Features & Use Cases - T2B2T Pipeline: Implements the Triples-to-Beliefs-to-Triples paradigm, translating incoming RDF into beliefs and projecting deliberation results back into RDF for downstream consumption. - Cognitive Chain Modeling: Wires beliefs, desires, and intentions with bidirectional properties (motivates/isMotivatedBy, fulfils/isFulfilledBy) enabling both forward reasoning and backward explainability tracing. - Framework Integration: Provides translation patterns for SEMAS production rules, JADE/JADEX agent platforms, FIPA ACL communication, and Logic Augmented Generation pipelines that constrain LLM outputs with ontological validation. - Use Case: A developer building a neuro-symbolic agent receives RDF sensor data, uses this Skill to model it as temporally-bounded beliefs with justifications, generates desires and intentions through the cognitive chain, and validates the result against SPARQL competency questions before deploying to a JADE platform. ## Quick Start Model the attached RDF context as BDI mental states with beliefs, desires, intentions, justifications, and temporal validity intervals serialized in Turtle.

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 endurants linked through motivational properties like motivates and fulfils, and ground each belief in a WorldState via refersTo. Use the T2B2T pipeline to translate incoming triples into beliefs and project deliberation results back into RDF.

What is the Triples-to-Beliefs-to-Triples paradigm?▼

T2B2T is a bidirectional pipeline where external RDF triples are translated into agent beliefs via BeliefProcess instances, BDI deliberation selects intentions and executes plans, and results are projected back into RDF using bringsAbout so downstream systems consume outputs as linked data.

How do I integrate BDI ontology with JADE or JADEX agents?▼

Map ontology individuals to Java mental state objects, storing beliefs, desires, and intentions in an RDF graph alongside the agent's runtime collections. JADEX goals can query the ontology with SPARQL ASK queries for target and drop conditions.

Can LLM outputs be constrained with a BDI ontology?▼

Yes, Logic Augmented Generation serializes the BDI ontology into the prompt context, then validates generated triples against ontological restrictions such as requiring every intention to fulfill a desire. Invalid output triggers retry with error feedback.

Why do BDI beliefs need temporal validity intervals?▼

Beliefs without temporal bounds cannot be garbage-collected or conflict-checked during diachronic reasoning. Assigning hasValidity intervals enables SPARQL queries filtering mental states active at a specific moment and detecting overlapping conflicting beliefs.

What are common mistakes when implementing BDI ontologies?▼

Common pitfalls include conflating mental states with world states, omitting justifications, skipping the plan layer between intentions and actions, and building belief chains deeper than three levels, which inflates LLM inference cost without improving decisions.

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