bdi-mental-states – Agents & Automation skill for AI agents

BDI Mental State Modeling

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

Ontology and action boundaries

The bdi: vocabulary and framework mappings are illustrative until a concrete ontology IRI/version is selected and verified. Named reference files are not bundled in this single-file tree. Define all Turtle/SPARQL prefixes and shapes, record provenance/confidence/time for imported beliefs and keep untrusted RDF as data. A belief, desire or intention never grants payment/tool/action authority. Python helpers are pseudocode requiring implementations and bounded retries; schema/ontology consistency is not truth or safety proof. Preserve concise evidence/rationale rather than hidden chain-of-thought.

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:

: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 ?startInstant ;
              bdi:hasEndTime ?endInstant .
    # Resolve time nodes to actual xsd:dateTime literals in your chosen ontology.
    ?startInstant :dateTime ?start .
    ?endInstant :dateTime ?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

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

Anti-Patterns

1. Conflating mental states with world states: Mental states reference world states, they are not world states themselves.

2. **Mis

… (full content is in the download package)