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bdi-mental-states

Converts RDF context into Belief-Desire-Intention mental states with justifications and temporal validity for multi-agent systems.

Updated Mar 13, 2026

Search Tags:#ontology #explainability #rdf #bdi #cognitive-agent #worldstate #temporal-logic

Ready to run

npx skills add https://github.com/marinvch/ai-os --skill bdi-mental-states-marinvch

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/marinvch/ai-os/tree/main/.agents/skills/context-engineering-collection/skills/bdi-mental-states
Command: npx skills add https://github.com/marinvch/ai-os --skill bdi-mental-states-marinvch

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill enables agents to transform RDF context into Belief-Desire-Intention mental states (BDI) using standard ontologies, supporting deliberation, explainability, and cross-agent interoperability in multi-agent systems.

Core Features & Use Cases

RDF-to-BDI Transformation: convert context RDF into grounded Belief, Desires, and Intentions.
Explainability & Provenance: attach Justifications and temporal validity to mental states.
Integrations & Patterns: supports LAG, SEMAS, RDF/Semantic interoperability, with world-state grounding and plan/task composition.

Quick Start

Provide an RDF description of a scenario; the skill outputs a BDI mental-state graph in Turtle with each Belief referencing a WorldState and each Intention mapping to a Plan.

Frequently Asked Questions about bdi-mental-states

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

FAQPage Schema

How do I convert RDF context into BDI mental states for cognitive agents?▼

To convert RDF context into BDI mental states, provide an RDF scenario description to generate a Turtle graph mapping context into grounded Beliefs, Desires, and Intentions for multi-agent deliberative reasoning.

What is the BDI architecture used for in multi-agent systems?▼

The BDI architecture models Beliefs, Desires, and Intentions to enable deliberative reasoning and explainability in multi-agent systems, providing traceable decision-making through justification tracking and temporal validity metadata.

How do I add temporal logic and explainability to agent mental states?▼

Attach Justifications and temporal validity metadata to BDI mental states to add temporal logic and explainability. Outputs maintain referential relations like hasValidity and refersTo for traceable world-state reasoning.

Can I use this BDI ontology approach for cross-agent semantic interoperability?▼

Yes, you can use this BDI ontology approach for cross-agent semantic interoperability. It supports LAG, SEMAS, and RDF semantic patterns to standardize mental states and enable interoperability across multiple agents.

Does this BDI transformation require a specific world-state grounding ontology?▼

Yes, BDI transformation requires a BDI ontology and world-state grounding. Each Belief must reference a WorldState and each Intention maps to a Plan, ensuring referential relations are maintained throughout the output graph.