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bdi-mental-states

Translate RDF context into BDI cognitive states using a formal ontology.

1|Updated Jan 27, 2026
Search Tags:#ontology#multi-agent#deliberation#explainability#rdf#cognitive-architecture#bdi
One-click install
npx skills add https://github.com/phonowell/mimikit --skill bdi-mental-states-phonowell
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bdi-mental-states
Source: https://github.com/phonowell/mimikit/tree/main/.agents/skills/context-engineering-collection/skills/bdi-mental-states
Command: npx skills add https://github.com/phonowell/mimikit --skill bdi-mental-states-phonowell

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

The BDI Mental State Modeling skill provides a structured approach to represent and reason about an agent's beliefs, desires, and intentions using a formal BDI ontology, grounding cognitive states in world states and enabling explainable deliberation and traceable reasoning across multi-agent systems.

Core Features & Use Cases

  • Ground mental states in world-state references with temporal validity and justifications to support explainable AI.
  • Support a two-phase T2B2T workflow (Triples-to-Beliefs and Beliefs-to-Triples) for RDF interoperability and reproducible reasoning.
  • Integrate with Logic Augmented Generation (LAG) and SEMAS-style frameworks to constrain generated cognition and translate ontologies into executable rules.
  • Temporal reasoning and compositional mental entities (Belief, Desire, Intention) with justification patterns and provenance.

Quick Start

Supply a concrete world-state example to generate corresponding beliefs, desires, and intentions using the BDI ontology.

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 agent beliefs, desires, and intentions using an RDF ontology?▼

To model agent beliefs, desires, and intentions, you can translate external RDF context into cognitive states using a formal BDI ontology, grounding mental entities in concrete world-state references. This enables explainable deliberation and traceable reasoning.

What is the BDI model in multi-agent systems and how does it support explainable AI?▼

The BDI model structures multi-agent reasoning through beliefs, desires, and intentions. It supports explainable AI by grounding cognitive states in world states with temporal validity and justification chains, making the deliberation process fully traceable.

Can I use BDI mental states with Logic Augmented Generation and SEMAS frameworks?▼

Yes, BDI mental states integrate with Logic Augmented Generation and SEMAS-style frameworks. This integration constrains generated cognition and translates the formal BDI ontology into executable rules for multi-agent environments.

How do I convert RDF triples into agent cognitive states for reasoning?▼

You convert RDF triples into agent cognitive states using a two-phase T2B2T workflow: Triples-to-Beliefs translates RDF context into beliefs, desires, and intentions, while Beliefs-to-Triples ensures RDF interoperability and reproducible reasoning.

Does this BDI ontology support temporal reasoning and justification chains?▼

Yes, the BDI ontology supports temporal reasoning by assigning temporal validity to mental states. It also tracks justification patterns and provenance, ensuring that each belief, desire, and intention is grounded with an explainable chain.

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