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bdi-mental-states

Generate Belief-Desire-Intention mental state RDF from external RDF context.

1|Updated May 16, 2026
Search Tags:#ontology#multi-agent#reasoning#sparql#rdf#bdi#mental state modeling
One-click install
npx skills add https://github.com/enigmaicon-eng/AI-Enterprise-OS --skill bdi-mental-states-enigmaicon-eng
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/enigmaicon-eng/AI-Enterprise-OS/tree/main/examples/agent-skills/skills/bdi-mental-states
Command: npx skills add https://github.com/enigmaicon-eng/AI-Enterprise-OS --skill bdi-mental-states-enigmaicon-eng

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

It solves the problem of converting external RDF context into explicit, explainable agent mental states (beliefs, desires, and intentions) that can be queried and used for deliberative reasoning.

Core Features & Use Cases

  • RDF-to-BDI transformation (T2B2T): Converts incoming triples into belief/desire/intention structures with provenance links, then projects outcomes back into RDF.
  • BDI cognitive chain modeling: Encodes motivators (belief->desire) and commitments (desire->intention) using bidirectional relations for traceability.
  • World-state grounding & temporal validity: Forces beliefs to reference specific world states and assigns validity intervals so you can resolve conflicts over time.
  • Explainability via justifications: Attaches evidential Justification entities to mental entities to make reasoning auditable.
  • Plan-structured intentions: Connects intentions to plans and plans to ordered tasks, enabling execution topology and trace-back of why something was chosen.

Quick Start

Use the bdi-mental-states skill to transform the RDF context of an agent scenario into beliefs, desires, and intentions with grounded world-state references, temporal validity, and justifications.

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 multi-agent reasoning?▼

To convert RDF context into BDI mental states, apply the T2B2T modeling approach to transform incoming triples into belief, desire, and intention structures with provenance links. This process grounds agent reasoning in world states and produces queryable outputs for cognitive architectures.

What is the T2B2T modeling process for cognitive architecture design?▼

The T2B2T modeling process converts external RDF context into BDI mental states and projects outcomes back into RDF. It encodes motivators and commitments using bidirectional relations, ensuring traceability and explainable reasoning for semantic multi-agent modeling.

How do I model temporal validity and resolve belief conflicts in RDF?▼

To resolve belief conflicts over time in RDF, assign temporal validity intervals to beliefs that reference specific world states. This ensures each mental entity has a defined validity window, allowing your multi-agent system to manage conflicting context chronologically.

How do I make multi-agent reasoning auditable with justification entities?▼

To make multi-agent reasoning auditable, attach evidential Justification entities to your mental entities. This links beliefs, desires, and intentions directly to their supporting evidence, creating an explainable BDI cognitive chain that can be queried for provenance and verification.

Can I connect BDI intentions to execution plans and ordered tasks in RDF?▼

Yes, you can connect BDI intentions to execution plans and ordered tasks in RDF. This plan-structured intention modeling links intentions to plans and plans to ordered tasks, enabling execution topology and trace-back of why a particular action was chosen.

Does this BDI mental state modeling require SPARQL for querying outputs?▼

Querying BDI mental state outputs with SPARQL is supported because the transformation produces consistent, queryable RDF. The generated belief, desire, and intention structures are grounded in standard RDF formats, allowing semantic queries across the cognitive chain relations.

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