Bdi Mental States

: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: :Intention_I1 bdi:specifies :Plan_P1 .

:Plan_P1 a bdi:Plan ; bdi:addresses :Goal_G1 ; bdi:beginsWith :Task_T1 ; bdi:endsWith :Task_T3 .

:Task_T1 bdi:precedes :Task_T2 . :Task_T2 bdi:precedes :Task_T3 . T2B2T Paradigm Triples-to-Beliefs-to-Triples implements bidirectional flow between RDF knowledge graphs and internal mental states: Phase 1: Triples-to-Beliefs

External RDF context triggers belief formation

:WorldState_notification a bdi:WorldState ; rdfs:comment "Push notification: Payment request $250" ; bdi:triggers :BeliefProcess_BP1 .

:BeliefProcess_BP1 a bdi:BeliefProcess ; bdi:generates :Belief_payment_request . Phase 2: Beliefs-to-Triples

Mental deliberation produces new RDF output

:Intention_pay a bdi:Intention ; bdi:specifies :Plan_payment .

:PlanExecution_PE1 a bdi:PlanExecution ; bdi:satisfies :Plan_payment ; bdi:bringsAbout :WorldState_payment_complete . Notation Selection by Level C4 Level Notation Mental State Representation L1 Context ArchiMate Agent boundaries, external perception sources L2 Container ArchiMate BDI reasoning engine, belief store, plan executor L3 Component UML Mental state managers, process handlers L4 Code UML/RDF Belief/Desire/Intention classes, ontology instances Justification and Explainability Mental entities link to supporting evidence for traceable reasoning: :Belief_B1 a bdi:Belief ; bdi:isJustifiedBy :Justification_J1 .

:Justification_J1 a bdi:Justification ; rdfs:comment "Official announcement received via email" .

:Intention_I1 a bdi:Intention ; bdi:isJustifiedBy :Justification_J2 .

:Justification_J2 a bdi:Justification ; rdfs:comment "Location precondition satisfied" . Temporal Dimensions Mental states persist over bounded time periods: :Belief_B1 a bdi:Belief ; bdi:hasValidity :TimeInterval_TI1 .

:TimeInterval_TI1 a bdi:TimeInterval ; bdi:hasStartTime :TimeInstant_9am ; bdi:hasEndTime :TimeInstant_11am . Query mental states active at specific moments: SELECT ?mentalState WHERE { ?mentalState bdi:hasValidity ?interval . ?interval bdi:hasStartTime ?start ; bdi:hasEndTime ?end . FILTER(?start <= "2025-01-04T10:00:00"^^xsd:dateTime && ?end >= "2025-01-04T10:00:00"^^xsd:dateTime) } Compositional Mental Entities Complex mental entities decompose into constituent parts for selective updates: :Belief_meeting a bdi:Belief ; rdfs:comment "Meeting at 10am in Room 5" ; bdi:hasPart :Belief_meeting_time , :Belief_meeting_location .

Update only location component

:BeliefProcess_update a bdi:BeliefProcess ; bdi:modifies :Belief_meeting_location . Integration Patterns Logic Augmented Generation (LAG) Augment LLM outputs with ontological constraints: def augment_llm_with_bdi_ontology ( prompt, ontology_graph ): ontology_context = serialize_ontology(ontology_graph, format

'turtle' ) augmented_prompt = f" {ontology_context} \n\n {prompt} " response = llm.generate(augmented_prompt) triples = extract_rdf_triples(response)

is_consistent = validate_triples(triples, ontology_graph)

return triples if is_consistent else retry_with_feedback() SEMAS Rule Translation Map BDI ontology to executable production rules: % Belief triggers desire formation [HEAD: belief(agent_a, store_open)] / [CONDITIONALS: time(weekday_afternoon)] » [TAIL: generate_desire(agent_a, buy_groceries)].

% Desire triggers intention commitment [HEAD: desire(agent_a, buy_groceries)] / [CONDITIONALS: belief(agent_a, has_shopping_list)] » [TAIL: commit_intention(agent_a, buy_groceries)]. Guidelines 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)