What problem does it solve? Modeling why an agent believes something, what goal that belief creates, and which plan it commits to is hard to do in a traceable, queryable way. This Skill converts external RDF context into formal Belief-Desire-Intention mental states so agent reasoning becomes explainable, temporally bounded, and interoperable across multi-agent systems. ## Core Features & Use Cases - T2B2T Pipeline: Translate incoming RDF triples into beliefs, run BDI deliberation, then project results back into RDF for downstream systems. - Cognitive Chain Modeling: Wire beliefs, desires, and intentions with bidirectional properties (motivates/isMotivatedBy, fulfils/isFulfilledBy) for forward reasoning and backward explainability tracing. - Framework Integration: Deploy BDI models to SEMAS production rules, JADE/JADEX platforms, or constrain LLM outputs via Logic Augmented Generation with ontology validation. - Use Case: A payment-notification agent receives an RDF world state, forms a justified belief, derives a desire to pay, commits an intention with an ordered task plan, and emits the completed payment as new triples. ## Quick Start Model the agent's beliefs, desires, and intentions from this RDF notification using the BDI ontology patterns, including justifications and temporal validity intervals.
