Bdi Mental States

Separate mental states into two ontological categories because BDI reasoning requires distinguishing what persists from what happens:

Mental States (Endurants) -- model these as persistent cognitive attributes that hold over time intervals:

• Belief: Represent what the agent holds true about the world. Ground every belief in a world state reference.
• Desire: Represent what the agent wishes to bring about. Link each desire back to the beliefs that motivate it.
• Intention: Represent what the agent commits to achieving. An intention must fulfil a desire and specify a plan.

Mental Processes (Perdurants) -- model these as events that create or modify mental states, because tracking causal transitions enables explainability:

• BeliefProcess: Triggers belief formation/update from perception. Always connect to a generating world state.
• DesireProcess: Generates desires from existing beliefs. Preserves the motivational chain.
• IntentionProcess: Commits to selected desires as actionable intentions.

Cognitive Chain Pattern

Wire beliefs, desires, and intentions into directed chains using bidirectional properties (motivates/isMotivatedBy, fulfils/isFulfilledBy) because this enables both forward reasoning (what should the agent do?) and backward tracing (why did the agent act?):

: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

Always ground mental states in world state references rather than free-text descriptions, because ungrounded beliefs break semantic querying and cross-agent interoperability:

: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

Connect intentions to plans via bdi:specifies, and decompose plans into ordered task sequences using bdi:precedes, because this separation allows plan reuse across different intentions while keeping execution order explicit:

: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

Implement Triples-to-Beliefs-to-Triples as a bidirectional pipeline because agents must both consume external RDF context and produce new RDF assertions. Structure every T2B2T implementation in two explicit phases:

Phase 1: Triples-to-Beliefs -- Translate incoming RDF triples into belief instances. Use bdi:triggers to connect the external world state to a BeliefProcess, and bdi:generates to produce the resulting belief. This preserves provenance from source data through to internal cognition:

: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 -- After BDI deliberation selects an intention and executes a plan, project the results back into RDF using bdi:bringsAbout. This closes the loop so downstream systems can consume agent outputs as standard linked data:

: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

Choose notation based on the C4 abstraction level being modeled, because mixing notations at the wrong level obscures rather than clarifies the cognitive architecture:

Justification and Explainability

Attach bdi:Justification instances to every mental entity using bdi:isJustifiedBy, because unjustified mental states make agent reasoning opaque and untraceable. Each justification should capture the evidence or rule that produced the mental state:

: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

Assign validity intervals to every mental state using bdi:hasValidity with TimeInterval instances, because beliefs without temporal bounds cannot be garbage-collected or conflict-checked during diachronic reasoning:

: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 a specific moment using SPARQL temporal filters. Use this pattern to resolve conflicts when multiple beliefs about the same world state overlap in time:

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

Decompose complex beliefs into constituent parts using bdi:hasPart relations, because monolithic beliefs force full replacement on partial updates. Structure composite beliefs so that each sub-belief can be independently updated, queried, or invalidated:

: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 without touching time
:BeliefProcess_update a bdi:BeliefProcess ;
    bdi:modifies :Belief_meeting_location .

Integration Patterns

Logic Augmented Generation (LAG)

Use LAG to constrain LLM outputs with ontological structure, because unconstrained generation produces triples that violate BDI class restrictions. Serialize the ontology into the prompt context, then validate generated triples against it before accepting them:

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

Translate BDI ontology patterns into executable production rules when deploying to rule-based agent platforms. Map each cognitive chain link (belief-to-desire, desire-to-intention) to a HEAD/CONDITIONALS/TAIL rule, because this preserves the deliberative semantics while enabling runtime execution:

% 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

1. Model world states as configurations independent of agent perspectives, providing referential substrate for mental states.

2. Distinguish endurants (persistent mental states) from perdurants (temporal mental processes), aligning with DOLCE ontology.

3. Treat goals as descriptions rather than mental states, maintaining separation between cognitive and planning layers.

4. Use hasPart relations for meronymic structures enabling selective belief updates.

5. Associate every mental entity with temporal constructs via atTime or hasValidity.

6. Use bidirectional property pairs (motivates/isMotivatedBy, generates/isGeneratedBy) for flexible querying.

7. Link mental entities to Justification instances for explainability and trust.

8. Implement T2B2T through: (1) translate RDF to beliefs, (2) execute BDI reasoning, (3) project mental states back to RDF.

9. Define existential restrictions on mental processes (e.g., BeliefProcess ⊑ ∃generates.Belief).

10. 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

Gotchas

1. Conflating mental states with world states: Mental states reference world states via bdi:refersTo, they are not world states themselves. Mixing them collapses the perception-cognition boundary and breaks SPARQL queries that filter by type.

2. Missing temporal bounds: Every mental state needs validity intervals for diachronic reasoning. Without them, stale beliefs persist indefinitely and conflict detection becomes impossible.

3. Flat belief structures: Use compositional modeling with hasPart for complex beliefs. Monolithic beliefs force full replacement when only one attribute changes.

4. Implicit justifications: Always link mental entities to explicit Justification instances. Unjustified mental states cannot be audited or traced.

5. Direct intention-to-action mapping: Intentions specify plans which contain tasks; actions execute tasks. Skipping the plan layer removes the ability to reuse, reorder, or share execution strategies.

6. Ontology over-complexity: Start with 5-10 core classes and properties (Belief, Desire, Intention, WorldState, Plan, plus key relations). Expanding the ontology prematurely inflates prompt context and slows SPARQL queries without improving reasoning quality.

7. Reasoning cost explosion: Keep belief chains to 3 levels or fewer (belief -> desire -> intention). Deeper chains become prohibitively expensive for LLM inference and rarely improve decision quality over shallower alternatives.

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

Internal references:

• BDI Ontology Core - Read when: implementing BDI class hierarchies or defining ontology properties from scratch
• RDF Examples - Read when: writing Turtle serializations of mental states or debugging triple structure
• SPARQL Competency Queries - Read when: validating an implementation against competency questions or building custom queries
• Framework Integration - Read when: deploying BDI models to SEMAS, JADE, or LAG pipelines

Primary sources:

• Zuppiroli et al. "The Belief-Desire-Intention Ontology" (2025) — Read when: implementing formal BDI class hierarchies or validating ontology alignment
• Rao & Georgeff "BDI agents: From theory to practice" (1995) — Read when: understanding the theoretical foundations of practical reasoning agents
• Bratman "Intention, plans, and practical reason" (1987) — Read when: grounding implementation decisions in the philosophical basis of intentionality

Skill Metadata

Created: 2026-01-07 Last Updated: 2026-03-17 Author: Agent Skills for Context Engineering Contributors Version: 2.0.0