Can this skill be used with Large Language Models (LLMs)?

Yes, it supports Logic Augmented Generation (LAG), which uses BDI ontological constraints to validate and guide LLM outputs, ensuring they remain consistent with the agent's mental state and world knowledge.

What is BDI architecture for AI agents?

BDI (Belief-Desire-Intention) is a software model for developing intelligent agents that mimics human reasoning by managing what the agent knows (beliefs), what its goals are (desires), and what it has committed to acting upon (intentions).

How does this skill improve agent explainability?

The skill uses formal ontology patterns to link every intention and action to specific beliefs and justifications, creating a traceable reasoning chain that can be queried and audited.

What is the T2B2T paradigm?

T2B2T stands for Triples-to-Beliefs-to-Triples. it is a process where external RDF knowledge graph triples are converted into internal agent beliefs for reasoning, and the results of that reasoning are projected back as RDF output.

BDI Mental State Modeling

Name: BDI Mental State Modeling
Author: Kalyanikhandare29

byKalyanikhandare29

0•

Data Science & ML

Transforms external RDF context into formal Belief-Desire-Intention (BDI) models to enable rational agency and explainable reasoning in AI agents.

This skill implements a robust cognitive architecture for AI agents based on the Belief-Desire-Intention (BDI) framework. It provides standardized patterns to transform structured RDF data into internal agent mental states, facilitating complex deliberative reasoning, temporal state management, and semantic interoperability. By bridging the gap between symbolic logic and agent behavior, it allows developers to build autonomous systems that can justify their actions through traceable reasoning chains, making it essential for neuro-symbolic AI, multi-agent coordination, and high-trust autonomous applications.

Key Features

010 GitHub stars

02Formal BDI ontology mapping for beliefs, desires, and intentions

03Temporal validity tracking for persistent and evolving mental states

04Traceable justification instances for agent decision explainability

05T2B2T (Triples-to-Beliefs-to-Triples) bidirectional data flow

06Logic Augmented Generation (LAG) to constrain LLM outputs with cognitive structures

Use Cases

01Integrating LLMs with symbolic logic to ensure goal-directed behavior and consistency

02Creating explainable AI systems where decisions are backed by formal reasoning chains

03Developing autonomous agents for multi-agent systems like JADE or SEMAS

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kalyanikhandare29/agent-skills-for-context-engineering bdi-mental-states

For use in Claude.ai and ChatGPT

Download Skill

Key Features

010 GitHub stars

02Formal BDI ontology mapping for beliefs, desires, and intentions

03Temporal validity tracking for persistent and evolving mental states

04Traceable justification instances for agent decision explainability

05T2B2T (Triples-to-Beliefs-to-Triples) bidirectional data flow

06Logic Augmented Generation (LAG) to constrain LLM outputs with cognitive structures

Use Cases

01Integrating LLMs with symbolic logic to ensure goal-directed behavior and consistency

02Creating explainable AI systems where decisions are backed by formal reasoning chains

03Developing autonomous agents for multi-agent systems like JADE or SEMAS

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add kalyanikhandare29/agent-skills-for-context-engineering bdi-mental-states

For use in Claude.ai and ChatGPT

Download Skill