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bdi-mental-states

Transform RDF context into BDI agent beliefs, desires, and intentions using formal ontology patterns.

Updated Jan 31, 2026
Search Tags:#ontology#multi-agent systems#neuro-symbolic#sparql#rdf#bdi#mental states
One-click install
npx skills add https://github.com/adrianasiam19/ATLAS --skill bdi-mental-states-adrianasiam19
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/adrianasiam19/ATLAS/tree/main/.agent/skills/bdi-mental-states
Command: npx skills add https://github.com/adrianasiam19/ATLAS --skill bdi-mental-states-adrianasiam19

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 consistent ontological structure, temporal grounding, and traceable reasoning chains, which ad-hoc implementations typically lack. ## Core Features & Use Cases - BDI Ontology Modeling: Structure mental states as endurants (Belief, Desire, Intention) and mental processes as perdurants, aligned with DOLCE and reused ontology design patterns. - T2B2T Pipeline: Implement Triples-to-Beliefs-to-Triples workflows that consume external RDF context and project deliberation results back as linked data. - Framework Integration: Translate BDI models into SEMAS production rules, JADE/JADEX agent structures, and Logic Augmented Generation pipelines that constrain LLM outputs with ontology validation. - Use Case: Given an RDF notification like a payment request, generate grounded beliefs, motivated desires, committed intentions with plans, and SPARQL competency queries to validate the cognitive chain. ## Quick Start Model the agent mental states for this RDF context using the BDI ontology, including beliefs, desires, intentions, justifications, and temporal validity intervals.

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 motivates and fulfils properties, with each belief grounded in a WorldState via refersTo. Use Turtle serialization and connect mental processes through generates relations to preserve provenance.

What is the Triples-to-Beliefs-to-Triples (T2B2T) pattern?▼

T2B2T is a bidirectional pipeline where incoming RDF triples are translated into belief instances via BeliefProcess entities, BDI deliberation selects intentions and executes plans, then results are projected back into RDF using bringsAbout for downstream consumption.

Can BDI ontology constrain LLM outputs in Logic Augmented Generation?▼

Yes, serialize the BDI ontology into the LLM prompt context, generate triples, then validate them against ontological restrictions such as intentions fulfilling desires and beliefs referencing world states. Retry with error feedback when validation fails.

How do I translate BDI models to SEMAS or JADE frameworks?▼

Map each cognitive chain link to a HEAD/CONDITIONALS/TAIL production rule in SEMAS, or store mental states as RDF-backed Java objects in JADE agents. The references include rule templates and a Python translation layer using rdflib.

Why do BDI beliefs need temporal validity intervals?▼

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

What are common mistakes when implementing BDI ontologies?▼

Common pitfalls include conflating mental states with world states, omitting justifications, using monolithic beliefs instead of hasPart composition, skipping the plan layer between intentions and actions, and building belief chains deeper than three levels.

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