bdi-mental-states

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Chinese

BDI Mental State Modeling

BDI心理状态建模

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.

使用正式的BDI本体模式将外部RDF上下文转换为Agent的心理状态（信念、愿望、意图）。此技能使Agent能够通过认知架构对上下文进行推理，支持多Agent系统中的审慎推理、可解释性和语义互操作性。

When to Activate

激活时机

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

在以下场景中激活此技能：

将外部RDF上下文处理为Agent关于世界状态的信念
建模包含感知、审慎思考和行动周期的理性智能体
通过可追溯的推理链实现可解释性
实现BDI框架（SEMAS、JADE、JADEX）
用正式认知结构增强大语言模型（Logic Augmented Generation，LAG）
在多Agent平台间协调心理状态
跟踪信念、愿望和意图的时间演变
将动机状态与行动计划关联

Core Concepts

核心概念

Mental Reality Architecture

心理现实架构

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.

将心理状态分为两个本体类别，因为BDI推理需要区分持久存在的事物和发生的事件：

Mental States (Endurants)——持久心理状态：将其建模为在时间区间内持续存在的认知属性：

```
Belief
```
：代表Agent认为真实的世界情况。每个信念都必须关联到一个世界状态引用。
```
Desire
```
：代表Agent希望实现的目标。每个愿望都要关联到驱动它的信念。
```
Intention
```
：代表Agent承诺要达成的目标。意图必须满足一个愿望并指定一个计划。

Mental Processes (Perdurants)——心理过程：将其建模为创建或修改心理状态的事件，因为跟踪因果转换可实现可解释性：

```
BeliefProcess
```
：由感知触发信念的形成/更新。始终关联到生成它的世界状态。
```
DesireProcess
```
：从现有信念生成愿望。保留动机链。
```
IntentionProcess
```
：将选定的愿望承诺为可执行的意图。

Cognitive Chain Pattern

认知链模式

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?):

turtle

: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 .

使用双向属性（

motivates

isMotivatedBy

、

fulfils

isFulfilledBy

）将信念、愿望和意图连接成定向链，因为这既支持正向推理（Agent应该做什么？）也支持反向追溯（Agent为什么采取行动？）：

turtle

: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

世界状态锚定

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

turtle

: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 .

始终将心理状态锚定到世界状态引用而非自由文本描述，因为未锚定的信念会破坏语义查询和跨Agent互操作性：

turtle

: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

目标导向规划

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:

turtle

: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 .

通过

bdi:specifies

将意图与计划关联，并使用

bdi:precedes

将计划分解为有序任务序列，因为这种分离允许计划在不同意图间复用，同时保持执行顺序明确：

turtle

: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

T2B2T范式

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:

turtle

: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:

turtle

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

:PlanExecution_PE1 a bdi:PlanExecution ;
    bdi:satisfies :Plan_payment ;
    bdi:bringsAbout :WorldState_payment_complete .

实现Triples-to-Beliefs-to-Triples作为双向管道，因为Agent既要消费外部RDF上下文，也要生成新的RDF断言。将每个T2B2T实现分为两个明确阶段：

阶段1：三元组转信念——将传入的RDF三元组转换为信念实例。使用

bdi:triggers

将外部世界状态与

BeliefProcess

关联，使用

bdi:generates

生成最终信念。这样可以保留从源数据到内部认知的溯源信息：

turtle

: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 .

阶段2：信念转三元组——BDI审慎推理选定意图并执行计划后，使用

bdi:bringsAbout

将结果投影回RDF。这形成闭环，使下游系统可以将Agent输出作为标准关联数据消费：

turtle

: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

符号选择层级

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

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

根据建模的C4抽象层级选择符号，因为在错误层级混合符号会模糊而非清晰呈现认知架构：

C4层级	符号	心理状态表示
L1 上下文	ArchiMate	Agent边界、外部感知源
L2 容器	ArchiMate	BDI推理引擎、信念存储、计划执行器
L3 组件	UML	心理状态管理器、过程处理器
L4 代码	UML/RDF	信念/愿望/意图类、本体实例

Justification and Explainability

合理性与可解释性

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:

turtle

: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" .

使用

bdi:isJustifiedBy

将

bdi:Justification

实例附加到每个心理实体，因为无合理性依据的心理状态会使Agent推理不透明且无法追溯。每个合理性依据应记录生成心理状态的证据或规则：

turtle

: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

时间维度

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:

turtle

: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:

sparql

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)
}

使用

bdi:hasValidity

和

TimeInterval

实例为每个心理状态分配有效性区间，因为没有时间边界的信念无法在历时推理中被垃圾回收或冲突检查：

turtle

:Belief_B1 a bdi:Belief ;
    bdi:hasValidity :TimeInterval_TI1 .

:TimeInterval_TI1 a bdi:TimeInterval ;
    bdi:hasStartTime :TimeInstant_9am ;
    bdi:hasEndTime :TimeInstant_11am .

使用SPARQL时间过滤器查询特定时刻活跃的心理状态。当同一世界状态的多个信念在时间上重叠时，使用此模式解决冲突：

sparql

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

组合式心理实体

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:

turtle

:Belief_meeting a bdi:Belief ;
    rdfs:comment "Meeting at 10am in Room 5" ;
    bdi:hasPart :Belief_meeting_time , :Belief_meeting_location .

使用

bdi:hasPart

关系将复杂信念分解为组成部分，因为整体式信念在部分更新时需要完全替换。构建组合信念时，每个子信念应能独立更新、查询或失效：

turtle

: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

仅更新位置组件，不修改时间

:BeliefProcess_update a bdi:BeliefProcess ; bdi:modifies :Belief_meeting_location .

undefined

:BeliefProcess_update a bdi:BeliefProcess ; bdi:modifies :Belief_meeting_location .

undefined

Integration Patterns

集成模式

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:

python

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()

使用LAG用本体结构约束大语言模型输出，因为无约束生成会产生违反BDI类限制的三元组。将本体序列化到提示上下文中，在接受生成的三元组前先验证其与本体的一致性：

python

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

SEMAS规则转换

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:

prolog

% 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)].

部署到基于规则的Agent平台时，将BDI本体模式转换为可执行的产生式规则。将每个认知链链接（信念到愿望、愿望到意图）映射为HEAD/CONDITIONALS/TAIL规则，因为这样可以保留审慎语义同时支持运行时执行：

prolog

% 信念触发愿望形成
[HEAD: belief(agent_a, store_open)] /
[CONDITIONALS: time(weekday_afternoon)] »
[TAIL: generate_desire(agent_a, buy_groceries)].

% 愿望触发意图承诺
[HEAD: desire(agent_a, buy_groceries)] /
[CONDITIONALS: belief(agent_a, has_shopping_list)] »
[TAIL: commit_intention(agent_a, buy_groceries)].

Guidelines

指南

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.

将世界状态建模为独立于Agent视角的配置，为心理状态提供参考基础。
区分持久实体（持久心理状态）和瞬时过程（临时心理过程），与DOLCE本体保持一致。
将目标视为描述而非心理状态，保持认知层与规划层分离。
使用
```
hasPart
```
关系构建分体结构，支持选择性信念更新。
通过
```
atTime
```
或
```
hasValidity
```
将每个心理实体与时间构造关联。
使用双向属性对（
```
motivates
```
/
```
isMotivatedBy
```
、
```
generates
```
/
```
isGeneratedBy
```
）实现灵活查询。
将心理实体与
```
Justification
```
实例关联，提升可解释性和信任度。
通过以下步骤实现T2B2T：(1) 将RDF转换为信念；(2) 执行BDI推理；(3) 将心理状态投影回RDF。
为心理过程定义存在限制（例如：
```
BeliefProcess ⊑ ∃generates.Belief
```
）。
复用已确立的ODP（EventCore、Situation、TimeIndexedSituation、BasicPlan、Provenance）以提升互操作性。

Competency Questions

能力问题

Validate implementation against these SPARQL queries:

sparql

undefined

通过以下SPARQL查询验证实现：

sparql

undefined

CQ1: What beliefs motivated formation of a given desire?

CQ1: 哪些信念驱动了特定愿望的形成？

SELECT ?belief WHERE { :Desire_D1 bdi:isMotivatedBy ?belief . }

CQ2: Which desire does a particular intention fulfill?

CQ2: 特定意图满足哪个愿望？

SELECT ?desire WHERE { :Intention_I1 bdi:fulfils ?desire . }

CQ3: Which mental process generated a belief?

CQ3: 哪个心理过程生成了某个信念？

SELECT ?process WHERE { ?process bdi:generates :Belief_B1 . }

CQ4: What is the ordered sequence of tasks in a plan?

CQ4: 计划中的任务顺序是什么？

SELECT ?task ?nextTask WHERE { :Plan_P1 bdi:hasComponent ?task . OPTIONAL { ?task bdi:precedes ?nextTask } } ORDER BY ?task

undefined

SELECT ?task ?nextTask WHERE { :Plan_P1 bdi:hasComponent ?task . OPTIONAL { ?task bdi:precedes ?nextTask } } ORDER BY ?task

undefined

Gotchas

注意事项

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.
Missing temporal bounds: Every mental state needs validity intervals for diachronic reasoning. Without them, stale beliefs persist indefinitely and conflict detection becomes impossible.
Flat belief structures: Use compositional modeling with
```
hasPart
```
for complex beliefs. Monolithic beliefs force full replacement when only one attribute changes.
Implicit justifications: Always link mental entities to explicit
```
Justification
```
instances. Unjustified mental states cannot be audited or traced.
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.
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.
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.

混淆心理状态与世界状态：心理状态通过
```
bdi:refersTo
```
引用世界状态，它们本身不是世界状态。混淆两者会打破感知-认知边界，使按类型过滤的SPARQL查询失效。
缺少时间边界：每个心理状态都需要有效性区间以支持历时推理。没有时间边界，过时的信念会无限存在，冲突检测也变得不可能。
扁平信念结构：对复杂信念使用组合建模和
```
hasPart
```
。整体式信念在仅一个属性变化时需要完全替换。
隐含合理性依据：始终将心理实体与明确的
```
Justification
```
实例关联。无合理性依据的心理状态无法被审计或追溯。
意图到行动直接映射：意图指定包含任务的计划；行动执行任务。跳过计划层会失去复用、重排或共享执行策略的能力。
本体过度复杂：从5-10个核心类和属性开始（Belief、Desire、Intention、WorldState、Plan及关键关系）。过早扩展本体会增加提示上下文并减慢SPARQL查询，却无法提升推理质量。
推理成本爆炸：将信念链控制在3层以内（信念 -> 愿望 -> 意图）。更深的链会使大语言模型推理成本过高，且相比浅层替代方案很少能提升决策质量。

Integration

集成

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

RDF处理：解析外部RDF上下文后应用，以构建认知表示
语义推理：与本体推理结合，推断隐含的心理状态关系
多Agent通信：与FIPA ACL集成，实现跨平台信念共享
时间上下文：与时间推理协调，处理心理状态演变
可解释AI：输入到解释系统，跟踪从感知、审慎思考到行动的全过程
神经符号AI：在LAG管道中应用，用认知结构约束大语言模型输出

References

参考资料

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

内部参考：

BDI Ontology Core — 适用场景：实现BDI类层次结构或从头定义本体属性
RDF Examples — 适用场景：编写心理状态的Turtle序列化或调试三元组结构
SPARQL Competency Queries — 适用场景：根据能力问题验证实现或构建自定义查询
Framework Integration — 适用场景：将BDI模型部署到SEMAS、JADE或LAG管道

主要来源：

Zuppiroli等人《The Belief-Desire-Intention Ontology》(2025) — 适用场景：实现正式BDI类层次结构或验证本体对齐
Rao & Georgeff《BDI agents: From theory to practice》(1995) — 适用场景：理解实用推理Agent的理论基础
Bratman《Intention, plans, and practical reason》(1987) — 适用场景：将实现决策基于意向性的哲学基础

Skill Metadata

技能元数据

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

创建时间：2026-01-07 最后更新：2026-03-17 作者：Agent Skills for Context Engineering Contributors 版本：2.0.0