This skill should be used when the user asks to \\"model agent mental states\\", \\"implement BDI architecture\\", \\"create belief-desire-intention models\\", \\"transform RDF to beliefs\\", \\"build cognitive agent\\", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.
This skill should be used when the user asks to \\"model agent mental states\\", \\"implement BDI architecture\\", \\"create belief-desire-intention models\\", \\"transform RDF to beliefs\\", \\"build cognitive agent\\", or mentions BDI ontology, mental state modeling, rational agency, or neuro-symbolic AI integration.
Transform external RDF context into agent mental states (beliefs, desires, intentions) using formal BDI ontology patterns. This skill enables agents to reason about context through cognitive architecture, supporting deliberative reasoning, explainability, and semantic interoperability within multi-agent systems.
When to Use
Activate this skill when:
Processing external RDF context into agent beliefs about world states
Modeling rational agency with perception, deliberation, and action cycles
Enabling explainability through traceable reasoning chains
Implementing BDI frameworks (SEMAS, JADE, JADEX)
Augmenting LLMs with formal cognitive structures (Logic Augmented Generation)
Coordinating mental states across multi-agent platforms
Tracking temporal evolution of beliefs, desires, and intentions
Linking motivational states to action plans
Core Concepts
Mental Reality Architecture
Mental States (Endurants): Persistent cognitive attributes
Belief: What the agent believes to be true about the world
Desire: What the agent wishes to bring about
Intention: What the agent commits to achieving
Mental Processes (Perdurants): Events that modify mental states
BeliefProcess: Forming/updating beliefs from perception
DesireProcess: Generating desires from beliefs
IntentionProcess: Committing to desires as actionable intentions
Cognitive Chain Pattern
:Belief_store_open a bdi:Belief ;
rdfs:comment "Store is open" ;
bdi:motivates :Desire_buy_groceries .
:Desire_buy_groceries a bdi:Desire ;
rdfs:comment "I desire to buy groceries" ;
bdi:isMotivatedBy :Belief_store_open .
:Intention_go_shopping a bdi:Intention ;
rdfs:comment "I will buy groceries" ;
bdi:fulfils :Desire_buy_groceries ;
bdi:isSupportedBy :Belief_store_open ;
bdi:specifies :Plan_shopping .
World State Grounding
Mental states reference structured configurations of the environment:
:Agent_A a bdi:Agent ;
bdi:perceives :WorldState_WS1 ;
bdi:hasMentalState :Belief_B1 .
:WorldState_WS1 a bdi:WorldState ;
rdfs:comment "Meeting scheduled at 10am in Room 5" ;
bdi:atTime :TimeInstant_10am .
:Belief_B1 a bdi:Belief ;
bdi:refersTo :WorldState_WS1 .
Goal-Directed Planning
Intentions specify plans that address goals through task sequences:
Model world states as configurations independent of agent perspectives, providing referential substrate for mental states.
Distinguish endurants (persistent mental states) from perdurants (temporal mental processes), aligning with DOLCE ontology.
Treat goals as descriptions rather than mental states, maintaining separation between cognitive and planning layers.
Use hasPart relations for meronymic structures enabling selective belief updates.
Associate every mental entity with temporal constructs via atTime or hasValidity.
Use bidirectional property pairs (motivates/isMotivatedBy, generates/isGeneratedBy) for flexible querying.
Link mental entities to Justification instances for explainability and trust.
Implement T2B2T through: (1) translate RDF to beliefs, (2) execute BDI reasoning, (3) project mental states back to RDF.
Define existential restrictions on mental processes (e.g., BeliefProcess ⊑ ∃generates.Belief).
Reuse established ODPs (EventCore, Situation, TimeIndexedSituation, BasicPlan, Provenance) for interoperability.
Competency Questions
Validate implementation against these SPARQL queries:
# CQ1: What beliefs motivated formation of a given desire?
SELECT ?belief WHERE {
:Desire_D1 bdi:isMotivatedBy ?belief .
}
# CQ2: Which desire does a particular intention fulfill?
SELECT ?desire WHERE {
:Intention_I1 bdi:fulfils ?desire .
}
# CQ3: Which mental process generated a belief?
SELECT ?process WHERE {
?process bdi:generates :Belief_B1 .
}
# CQ4: What is the ordered sequence of tasks in a plan?
SELECT ?task ?nextTask WHERE {
:Plan_P1 bdi:hasComponent ?task .
OPTIONAL { ?task bdi:precedes ?nextTask }
} ORDER BY ?task
Anti-Patterns
Conflating mental states with world states: Mental states reference world states, they are not world states themselves.
Missing temporal bounds: Every mental state should have validity intervals for diachronic reasoning.
Flat belief structures: Use compositional modeling with hasPart for complex beliefs.
Implicit justifications: Always link mental entities to explicit justification instances.
Direct intention-to-action mapping: Intentions specify plans which contain tasks; actions execute tasks.
Integration
RDF Processing: Apply after parsing external RDF context to construct cognitive representations
Semantic Reasoning: Combine with ontology reasoning to infer implicit mental state relationships
Multi-Agent Communication: Integrate with FIPA ACL for cross-platform belief sharing
Temporal Context: Coordinate with temporal reasoning for mental state evolution
Explainable AI: Feed into explanation systems tracing perception through deliberation to action
Neuro-Symbolic AI: Apply in LAG pipelines to constrain LLM outputs with cognitive structures
References
See references/ folder for detailed documentation:
bdi-ontology-core.md - Core ontology patterns and class definitions
rdf-examples.md - Complete RDF/Turtle examples
sparql-competency.md - Full competency question SPARQL queries
framework-integration.md - SEMAS, JADE, LAG integration patterns
Primary sources:
Zuppiroli et al. "The Belief-Desire-Intention Ontology" (2025)
Rao & Georgeff "BDI agents: From theory to practice" (1995)
Bratman "Intention, plans, and practical reason" (1987)
