一、 引言：从黑箱到透明AI的范式演进
传统深度学习模型虽然在某些任务上表现出色，但其黑箱特性限制了在关键领域的应用。神经符号AI通过融合神经网络和符号AI的优势，实现了：

可解释性：提供基于符号逻辑的决策解释

知识注入：将先验知识和约束融入学习过程

样本效率：通过符号推理减少对大量数据的依赖

持续学习：支持增量知识更新和逻辑修正

Java在企业级系统、知识管理和复杂逻辑处理方面的优势，使其成为构建生产级神经符号AI系统的理想平台。本文将基于Drools、Apache Jena和Deep Java Library，演示如何构建可解释的混合智能系统。

二、 神经符号架构设计

1. 系统架构概览

text
感知层 → 符号化模块 → 知识库 → 推理引擎 → 决策层
↓ ↓ ↓ ↓ ↓
原始数据 → 神经符号 → 规则库 → 逻辑推理 → 可解释
↓ 转换器 ↓ 引擎 ↓ 输出
多模态输入 → 概念提取 → 本体库 → 约束求解 → 行动推荐

1. 核心组件选型

符号推理：Drools、Apache Jena

神经网络：Deep Java Library、ND4J

知识表示：OWL API、RDF4J

约束求解：Choco Solver、JaCoP

可视化：JUNG、GraphStream

1. 项目依赖配置

xml

org.springframework.boot
spring-boot-starter-web
${spring-boot.version}

<!-- 规则引擎 -->
<dependency>
    <groupId>org.drools</groupId>
    <artifactId>drools-engine</artifactId>
    <version>${drools.version}</version>
</dependency>

<!-- 知识图谱 -->
<dependency>
    <groupId>org.apache.jena</groupId>
    <artifactId>apache-jena-libs</artifactId>
    <version>${jena.version}</version>
    <type>pom</type>
</dependency>

<!-- 深度学习 -->
<dependency>
    <groupId>ai.djl</groupId>
    <artifactId>api</artifactId>
    <version>${djl.version}</version>
</dependency>

<dependency>
    <groupId>ai.djl.pytorch</groupId>
    <artifactId>pytorch-engine</artifactId>
    <version>${djl.version}</version>
</dependency>

<!-- 本体论 -->
<dependency>
    <groupId>net.sourceforge.owlapi</groupId>
    <artifactId>owlapi-api</artifactId>
    <version>${owlapi.version}</version>
</dependency>

<!-- 约束求解 -->
<dependency>
    <groupId>org.choco-solver</groupId>
    <artifactId>choco-solver</artifactId>
    <version>4.10.14</version>
</dependency>

<!-- 图计算 -->
<dependency>
    <groupId>net.sf.jung</groupId>
    <artifactId>jung-graph-impl</artifactId>
    <version>2.1.1</version>
</dependency>

三、 神经符号转换与表示学习

1. 概念提取与符号化

java
// NeuralSymbolicConverter.java
@Component
@Slf4j
public class NeuralSymbolicConverter {

private final ConceptExtractionModel conceptModel;
private final SymbolGroundingService groundingService;
private final OntologyManager ontologyManager;

public NeuralSymbolicConverter(ConceptExtractionModel conceptModel,
                             SymbolGroundingService groundingService,
                             OntologyManager ontologyManager) {
    this.conceptModel = conceptModel;
    this.groundingService = groundingService;
    this.ontologyManager = ontologyManager;
}

/**
 * 从原始数据提取符号概念
 */
public SymbolicRepresentation extractSymbols(RawData data, ExtractionConfig config) {
    log.info("从原始数据提取符号概念，数据类型: {}", data.getDataType());

    SymbolicRepresentation representation = new SymbolicRepresentation();

    switch (data.getDataType()) {
        case TEXT:
            representation = extractFromText((TextData) data, config);
            break;
        case IMAGE:
            representation = extractFromImage((ImageData) data, config);
            break;
        case TIME_SERIES:
            representation = extractFromTimeSeries((TimeSeriesData) data, config);
            break;
        default:
            throw new IllegalArgumentException("不支持的数据类型: " + data.getDataType());
    }

    // 符号落地：将提取的概念关联到知识库中的实体
    groundSymbols(representation);

    return representation;
}

/**
 * 从文本数据提取符号概念
 */
private SymbolicRepresentation extractFromText(TextData textData, ExtractionConfig config) {
    SymbolicRepresentation representation = new SymbolicRepresentation();

    try {
        // 使用神经网络提取实体和关系
        NeuralExtractionResult neuralResult = conceptModel.extractFromText(
            textData.getContent(), config);

        // 转换为符号表示
        for (Entity entity : neuralResult.getEntities()) {
            SymbolicConcept concept = new SymbolicConcept(
                entity.getText(),
                entity.getType(),
                entity.getConfidence(),
                ConceptType.ENTITY
            );
            representation.addConcept(concept);
        }

        for (Relation relation : neuralResult.getRelations()) {
            SymbolicRelation symbolicRelation = new SymbolicRelation(
                relation.getSubject(),
                relation.getPredicate(),
                relation.getObject(),
                relation.getConfidence()
            );
            representation.addRelation(symbolicRelation);
        }

        // 提取高层次模式
        List<Pattern> patterns = extractPatterns(neuralResult);
        representation.setPatterns(patterns);

    } catch (Exception e) {
        log.error("文本符号提取失败", e);
    }

    return representation;
}

/**
 * 从图像数据提取符号概念
 */
private SymbolicRepresentation extractFromImage(ImageData imageData, ExtractionConfig config) {
    SymbolicRepresentation representation = new SymbolicRepresentation();

    try {
        // 使用视觉概念提取模型
        VisualConceptResult visualResult = conceptModel.extractFromImage(
            imageData.getImageBytes(), config);

        // 对象检测结果转换为符号
        for (DetectedObject obj : visualResult.getObjects()) {
            SymbolicConcept concept = new SymbolicConcept(
                obj.getLabel(),
                "VisualObject",
                obj.getConfidence(),
                ConceptType.ENTITY
            );
            concept.addAttribute("bounding_box", obj.getBoundingBox());
            concept.addAttribute("color", obj.getDominantColor());
            representation.addConcept(concept);
        }

        // 场景理解转换为符号关系
        for (SceneRelation sceneRel : visualResult.getSceneRelations()) {
            SymbolicRelation relation = new SymbolicRelation(
                sceneRel.getSubject(),
                sceneRel.getSpatialRelation(),
                sceneRel.getObject(),
                sceneRel.getConfidence()
            );
            representation.addRelation(relation);
        }

        // 提取视觉模式
        List<VisualPattern> visualPatterns = extractVisualPatterns(visualResult);
        representation.setPatterns(visualPatterns.stream()
            .map(p -> (Pattern) p)
            .collect(Collectors.toList()));

    } catch (Exception e) {
        log.error("图像符号提取失败", e);
    }

    return representation;
}

/**
 * 从时间序列提取符号模式
 */
private SymbolicRepresentation extractFromTimeSeries(TimeSeriesData tsData, ExtractionConfig config) {
    SymbolicRepresentation representation = new SymbolicRepresentation();

    try {
        // 时间序列模式提取
        TimeSeriesPatternResult tsResult = conceptModel.extractFromTimeSeries(
            tsData.getValues(), tsData.getTimestamps(), config);

        // 转换模式为符号概念
        for (TemporalPattern pattern : tsResult.getPatterns()) {
            SymbolicConcept concept = new SymbolicConcept(
                pattern.getPatternType().name(),
                "TemporalPattern",
                pattern.getConfidence(),
                ConceptType.PATTERN
            );
            concept.addAttribute("start_time", pattern.getStartTime());
            concept.addAttribute("end_time", pattern.getEndTime());
            concept.addAttribute("amplitude", pattern.getAmplitude());
            representation.addConcept(concept);
        }

        // 提取因果关系
        for (TemporalRelation relation : tsResult.getRelations()) {
            SymbolicRelation symbolicRelation = new SymbolicRelation(
                relation.getCause(),
                "precedes",
                relation.getEffect(),
                relation.getConfidence()
            );
            symbolicRelation.addAttribute("time_lag", relation.getTimeLag());
            representation.addRelation(symbolicRelation);
        }

    } catch (Exception e) {
        log.error("时间序列符号提取失败", e);
    }

    return representation;
}

/**
 * 符号落地：将神经网络输出关联到知识库实体
 */
private void groundSymbols(SymbolicRepresentation representation) {
    for (SymbolicConcept concept : representation.getConcepts()) {
        GroundingResult grounding = groundingService.groundConcept(
            concept.getName(), concept.getType());

        if (grounding.isSuccess()) {
            concept.setUri(grounding.getUri());
            concept.setConfidence(concept.getConfidence() * grounding.getSimilarity());

            // 添加本体类型信息
            concept.setOntologyTypes(grounding.getTypes());
        }
    }

    for (SymbolicRelation relation : representation.getRelations()) {
        GroundingResult subjGrounding = groundingService.groundConcept(
            relation.getSubject(), "ENTITY");
        GroundingResult objGrounding = groundingService.groundConcept(
            relation.getObject(), "ENTITY");
        GroundingResult predGrounding = groundingService.groundProperty(
            relation.getPredicate());

        if (subjGrounding.isSuccess() && objGrounding.isSuccess() && predGrounding.isSuccess()) {
            relation.setSubjectUri(subjGrounding.getUri());
            relation.setObjectUri(objGrounding.getUri());
            relation.setPredicateUri(predGrounding.getUri());
        }
    }
}

/**
 * 从神经网络结果提取模式
 */
private List<Pattern> extractPatterns(NeuralExtractionResult neuralResult) {
    List<Pattern> patterns = new ArrayList<>();

    // 使用模式挖掘算法发现频繁模式
    PatternMiner patternMiner = new PatternMiner();
    List<FrequentPattern> frequentPatterns = patternMiner.minePatterns(
        neuralResult.getEntities(), neuralResult.getRelations());

    for (FrequentPattern freqPattern : frequentPatterns) {
        if (freqPattern.getSupport() > 0.1) { // 支持度阈值
            Pattern pattern = new SymbolicPattern(
                freqPattern.getPatternType(),
                freqPattern.getComponents(),
                freqPattern.getSupport(),
                freqPattern.getConfidence()
            );
            patterns.add(pattern);
        }
    }

    return patterns;
}

/**
 * 提取视觉模式
 */
private List<VisualPattern> extractVisualPatterns(VisualConceptResult visualResult) {
    List<VisualPattern> patterns = new ArrayList<>();

    // 空间关系模式挖掘
    SpatialPatternMiner spatialMiner = new SpatialPatternMiner();
    List<SpatialPattern> spatialPatterns = spatialMiner.minePatterns(
        visualResult.getObjects(), visualResult.getSceneRelations());

    for (SpatialPattern spatialPattern : spatialPatterns) {
        VisualPattern pattern = new VisualPattern(
            spatialPattern.getPatternType(),
            spatialPattern.getSpatialConfig(),
            spatialPattern.getConfidence()
        );
        patterns.add(pattern);
    }

    return patterns;
}

/**
 * 反向转换：从符号表示生成数据
 */
public RawData generateFromSymbols(SymbolicRepresentation symbols, GenerationConfig config) {
    // 实现符号到数据的生成（如文本生成、图像合成等）
    switch (config.getTargetDataType()) {
        case TEXT:
            return generateText(symbols, config);
        case IMAGE:
            return generateImage(symbols, config);
        default:
            throw new IllegalArgumentException("不支持的生成类型");
    }
}

private TextData generateText(SymbolicRepresentation symbols, GenerationConfig config) {
    // 使用符号概念生成连贯文本
    SymbolicTextGenerator generator = new SymbolicTextGenerator();
    String generatedText = generator.generate(symbols, config);
    return new TextData(generatedText);
}

private ImageData generateImage(SymbolicRepresentation symbols, GenerationConfig config) {
    // 使用符号描述生成图像
    SymbolicImageGenerator generator = new SymbolicImageGenerator();
    byte[] imageBytes = generator.generate(symbols, config);
    return new ImageData(imageBytes);
}

// 数据类
@Data
public static class SymbolicRepresentation {
    private List<SymbolicConcept> concepts = new ArrayList<>();
    private List<SymbolicRelation> relations = new ArrayList<>();
    private List<Pattern> patterns = new ArrayList<>();
    private double overallConfidence;

    public void addConcept(SymbolicConcept concept) {
        concepts.add(concept);
        updateOverallConfidence();
    }

    public void addRelation(SymbolicRelation relation) {
        relations.add(relation);
        updateOverallConfidence();
    }

    private void updateOverallConfidence() {
        double conceptAvg = concepts.stream()
                .mapToDouble(SymbolicConcept::getConfidence)
                .average().orElse(1.0);
        double relationAvg = relations.stream()
                .mapToDouble(SymbolicRelation::getConfidence)
                .average().orElse(1.0);
        this.overallConfidence = (conceptAvg + relationAvg) / 2;
    }
}

@Data
@AllArgsConstructor
public static class SymbolicConcept {
    private String name;
    private String type;
    private double confidence;
    private ConceptType conceptType;
    private String uri;
    private List<String> ontologyTypes = new ArrayList<>();
    private Map<String, Object> attributes = new HashMap<>();

    public void addAttribute(String key, Object value) {
        attributes.put(key, value);
    }
}

@Data
@AllArgsConstructor
public static class SymbolicRelation {
    private String subject;
    private String predicate;
    private String object;
    private double confidence;
    private String subjectUri;
    private String objectUri;
    private String predicateUri;
    private Map<String, Object> attributes = new HashMap<>();

    public void addAttribute(String key, Object value) {
        attributes.put(key, value);
    }
}

public enum ConceptType {
    ENTITY, ATTRIBUTE, EVENT, PATTERN, RELATION
}

}

1. 符号落地服务

java
// SymbolGroundingService.java
@Service
@Slf4j
public class SymbolGroundingService {

private final KnowledgeBase knowledgeBase;
private final EmbeddingModel embeddingModel;
private final SimilarityCalculator similarityCalculator;

public SymbolGroundingService(KnowledgeBase knowledgeBase,
                            EmbeddingModel embeddingModel) {
    this.knowledgeBase = knowledgeBase;
    this.embeddingModel = embeddingModel;
    this.similarityCalculator = new CosineSimilarityCalculator();
}

/**
 * 将概念落地到知识库实体
 */
public GroundingResult groundConcept(String conceptText, String conceptType) {
    try {
        // 获取概念嵌入
        float[] conceptEmbedding = embeddingModel.embed(conceptText);

        // 在知识库中搜索相似实体
        List<EntityCandidate> candidates = knowledgeBase.findSimilarEntities(
            conceptEmbedding, conceptType, 10);

        if (candidates.isEmpty()) {
            return GroundingResult.failure("未找到匹配实体");
        }

        // 选择最佳匹配
        EntityCandidate bestCandidate = selectBestCandidate(conceptText, candidates);

        return GroundingResult.success(
            bestCandidate.getUri(),
            bestCandidate.getSimilarity(),
            bestCandidate.getTypes()
        );

    } catch (Exception e) {
        log.error("概念落地失败: {}", conceptText, e);
        return GroundingResult.failure(e.getMessage());
    }
}

/**
 * 将关系落地到知识库属性
 */
public GroundingResult groundProperty(String relationText) {
    try {
        float[] relationEmbedding = embeddingModel.embed(relationText);

        List<PropertyCandidate> candidates = knowledgeBase.findSimilarProperties(
            relationEmbedding, 10);

        if (candidates.isEmpty()) {
            return GroundingResult.failure("未找到匹配属性");
        }

        PropertyCandidate bestCandidate = selectBestPropertyCandidate(relationText, candidates);

        return GroundingResult.success(
            bestCandidate.getUri(),
            bestCandidate.getSimilarity(),
            List.of(bestCandidate.getRangeType())
        );

    } catch (Exception e) {
        log.error("关系落地失败: {}", relationText, e);
        return GroundingResult.failure(e.getMessage());
    }
}

/**
 * 选择最佳实体候选
 */
private EntityCandidate selectBestCandidate(String conceptText, List<EntityCandidate> candidates) {
    // 基于语义相似度和类型匹配的综合评分
    return candidates.stream()
            .max(Comparator.comparingDouble(candidate -> 
                calculateCandidateScore(conceptText, candidate)))
            .orElse(candidates.get(0));
}

/**
 * 计算候选实体得分
 */
private double calculateCandidateScore(String conceptText, EntityCandidate candidate) {
    double semanticScore = candidate.getSimilarity();

    // 字符串相似度（编辑距离）
    double stringSimilarity = calculateStringSimilarity(conceptText, candidate.getLabel());

    // 类型匹配得分
    double typeScore = calculateTypeScore(candidate.getTypes());

    // 综合得分
    return 0.6 * semanticScore + 0.3 * stringSimilarity + 0.1 * typeScore;
}

/**
 * 选择最佳属性候选
 */
private PropertyCandidate selectBestPropertyCandidate(String relationText, List<PropertyCandidate> candidates) {
    return candidates.stream()
            .max(Comparator.comparingDouble(candidate -> 
                calculatePropertyScore(relationText, candidate)))
            .orElse(candidates.get(0));
}

private double calculatePropertyScore(String relationText, PropertyCandidate candidate) {
    double semanticScore = candidate.getSimilarity();
    double stringSimilarity = calculateStringSimilarity(relationText, candidate.getLabel());

    return 0.7 * semanticScore + 0.3 * stringSimilarity;
}

/**
 * 计算字符串相似度
 */
private double calculateStringSimilarity(String str1, String str2) {
    // 使用编辑距离计算相似度
    int maxLength = Math.max(str1.length(), str2.length());
    if (maxLength == 0) return 1.0;

    int editDistance = calculateLevenshteinDistance(str1, str2);
    return 1.0 - (double) editDistance / maxLength;
}

/**
 * 计算莱文斯坦距离
 */
private int calculateLevenshteinDistance(String str1, String str2) {
    int[][] dp = new int[str1.length() + 1][str2.length() + 1];

    for (int i = 0; i <= str1.length(); i++) {
        for (int j = 0; j <= str2.length(); j++) {
            if (i == 0) {
                dp[i][j] = j;
            } else if (j == 0) {
                dp[i][j] = i;
            } else {
                dp[i][j] = min(
                    dp[i - 1][j - 1] + (str1.charAt(i - 1) == str2.charAt(j - 1) ? 0 : 1),
                    dp[i - 1][j] + 1,
                    dp[i][j - 1] + 1
                );
            }
        }
    }

    return dp[str1.length()][str2.length()];
}

private int min(int a, int b, int c) {
    return Math.min(a, Math.min(b, c));
}

/**
 * 计算类型匹配得分
 */
private double calculateTypeScore(List<String> types) {
    // 简化实现：如果有匹配的类型则得高分
    return types.stream().anyMatch(type -> 
        type.equals("Person") || type.equals("Organization") || type.equals("Location")) ? 1.0 : 0.5;
}

/**
 * 批量概念落地
 */
public Map<String, GroundingResult> groundConceptsBatch(List<String> concepts, String conceptType) {
    return concepts.parallelStream()
            .collect(Collectors.toMap(
                concept -> concept,
                concept -> groundConcept(concept, conceptType)
            ));
}

// 数据类
@Data
@AllArgsConstructor
public static class GroundingResult {
    private boolean success;
    private String uri;
    private double similarity;
    private List<String> types;
    private String errorMessage;

    public static GroundingResult success(String uri, double similarity, List<String> types) {
        return new GroundingResult(true, uri, similarity, types, null);
    }

    public static GroundingResult failure(String errorMessage) {
        return new GroundingResult(false, null, 0.0, List.of(), errorMessage);
    }
}

}
四、 神经符号推理引擎

1. 混合推理引擎

java
// NeuroSymbolicReasoner.java
@Service
@Slf4j
public class NeuroSymbolicReasoner {

private final RuleEngine ruleEngine;
private final NeuralInferenceService neuralService;
private final ConstraintSolver constraintSolver;
private final ExplanationGenerator explanationGenerator;

public NeuroSymbolicReasoner(RuleEngine ruleEngine,
                           NeuralInferenceService neuralService,
                           ConstraintSolver constraintSolver,
                           ExplanationGenerator explanationGenerator) {
    this.ruleEngine = ruleEngine;
    this.neuralService = neuralService;
    this.constraintSolver = constraintSolver;
    this.explanationGenerator = explanationGenerator;
}

/**
 * 执行神经符号推理
 */
public ReasoningResult reason(ReasoningRequest request) {
    log.info("执行神经符号推理，查询: {}", request.getQuery());

    ReasoningContext context = new ReasoningContext(request);
    ReasoningResult result = new ReasoningResult();

    try {
        // 1. 符号推理阶段
        SymbolicReasoningResult symbolicResult = performSymbolicReasoning(context);
        result.setSymbolicResult(symbolicResult);

        // 2. 神经推理阶段
        NeuralReasoningResult neuralResult = performNeuralReasoning(context, symbolicResult);
        result.setNeuralResult(neuralResult);

        // 3. 融合推理结果
        FusedResult fusedResult = fuseResults(symbolicResult, neuralResult, context);
        result.setFusedResult(fusedResult);

        // 4. 约束求解和验证
        ConstraintSolution constraintSolution = solveConstraints(fusedResult, context);
        result.setConstraintSolution(constraintSolution);

        // 5. 生成解释
        Explanation explanation = generateExplanation(result, context);
        result.setExplanation(explanation);

        result.setSuccess(true);

    } catch (Exception e) {
        log.error("神经符号推理失败", e);
        result.setSuccess(false);
        result.setErrorMessage(e.getMessage());
    }

    return result;
}

/**
 * 符号推理
 */
private SymbolicReasoningResult performSymbolicReasoning(ReasoningContext context) {
    SymbolicReasoningResult result = new SymbolicReasoningResult();

    // 执行规则推理
    RuleExecutionResult ruleResult = ruleEngine.executeRules(
        context.getFacts(), context.getRules());
    result.setRuleResult(ruleResult);

    // 执行逻辑查询
    LogicalQueryResult queryResult = ruleEngine.executeLogicalQuery(
        context.getQuery(), context.getKnowledgeBase());
    result.setQueryResult(queryResult);

    // 执行本体推理
    OntologicalReasoningResult ontologyResult = ruleEngine.performOntologicalReasoning(
        context.getOntology());
    result.setOntologyResult(ontologyResult);

    return result;
}

/**
 * 神经推理
 */
private NeuralReasoningResult performNeuralReasoning(ReasoningContext context, 
                                                   SymbolicReasoningResult symbolicResult) {
    NeuralReasoningResult result = new NeuralReasoningResult();

    // 准备神经推理输入
    NeuralInput neuralInput = prepareNeuralInput(context, symbolicResult);

    // 执行神经网络推理
    NeuralOutput neuralOutput = neuralService.infer(neuralInput);
    result.setNeuralOutput(neuralOutput);

    // 提取神经符号模式
    List<NeuralPattern> patterns = extractNeuralPatterns(neuralOutput);
    result.setPatterns(patterns);

    // 计算不确定性
    UncertaintyEstimation uncertainty = estimateUncertainty(neuralOutput, symbolicResult);
    result.setUncertainty(uncertainty);

    return result;
}

/**
 * 融合符号和神经推理结果
 */
private FusedResult fuseResults(SymbolicReasoningResult symbolicResult,
                              NeuralReasoningResult neuralResult,
                              ReasoningContext context) {
    FusedResult fused = new FusedResult();

    // 基于置信度的结果融合
    List<FusedFact> fusedFacts = fuseFacts(
        symbolicResult.getRuleResult().getInferredFacts(),
        neuralResult.getNeuralOutput().getPredictedFacts()
    );
    fused.setFusedFacts(fusedFacts);

    // 冲突检测和解决
    List<Conflict> conflicts = detectConflicts(symbolicResult, neuralResult);
    fused.setConflicts(conflicts);

    List<ConflictResolution> resolutions = resolveConflicts(conflicts, context);
    fused.setConflictResolutions(resolutions);

    // 计算融合置信度
    double fusedConfidence = calculateFusedConfidence(symbolicResult, neuralResult);
    fused.setOverallConfidence(fusedConfidence);

    return fused;
}

/**
 * 约束求解
 */
private ConstraintSolution solveConstraints(FusedResult fusedResult, ReasoningContext context) {
    ConstraintModel constraintModel = buildConstraintModel(fusedResult, context);

    return constraintSolver.solve(constraintModel);
}

/**
 * 生成解释
 */
private Explanation generateExplanation(ReasoningResult result, ReasoningContext context) {
    return explanationGenerator.generateExplanation(result, context);
}

/**
 * 准备神经推理输入
 */
private NeuralInput prepareNeuralInput(ReasoningContext context, SymbolicReasoningResult symbolicResult) {
    NeuralInput input = new NeuralInput();

    // 将符号事实转换为神经网络输入
    List<SymbolicFact> symbolicFacts = symbolicResult.getRuleResult().getInferredFacts();
    float[] symbolicFeatures = convertFactsToFeatures(symbolicFacts);
    input.setSymbolicFeatures(symbolicFeatures);

    // 添加原始数据特征
    if (context.getRawData() != null) {
        float[] rawFeatures = extractRawFeatures(context.getRawData());
        input.setRawFeatures(rawFeatures);
    }

    // 添加上下文信息
    input.setContextEmbedding(context.getContextEmbedding());

    return input;
}

/**
 * 将符号事实转换为特征向量
 */
private float[] convertFactsToFeatures(List<SymbolicFact> facts) {
    // 使用嵌入模型将符号事实转换为向量
    List<float[]> factEmbeddings = facts.stream()
            .map(fact -> embeddingModel.embed(fact.toString()))
            .collect(Collectors.toList());

    // 平均池化得到整体表示
    return averagePooling(factEmbeddings);
}

/**
 * 提取原始数据特征
 */
private float[] extractRawFeatures(RawData rawData) {
    switch (rawData.getDataType()) {
        case TEXT:
            return embeddingModel.embed(((TextData) rawData).getContent());
        case IMAGE:
            return neuralService.extractImageFeatures(((ImageData) rawData).getImageBytes());
        case TIME_SERIES:
            return neuralService.extractTimeSeriesFeatures(
                ((TimeSeriesData) rawData).getValues());
        default:
            return new float[0];
    }
}

/**
 * 提取神经模式
 */
private List<NeuralPattern> extractNeuralPatterns(NeuralOutput neuralOutput) {
    List<NeuralPattern> patterns = new ArrayList<>();

    // 使用模式提取网络发现神经激活模式
    PatternExtractionModel patternModel = neuralService.getPatternExtractionModel();
    List<ActivationPattern> activationPatterns = patternModel.extractPatterns(
        neuralOutput.getHiddenStates());

    for (ActivationPattern activationPattern : activationPatterns) {
        NeuralPattern pattern = new NeuralPattern(
            activationPattern.getPatternType(),
            activationPattern.getNeurons(),
            activationPattern.getStrength()
        );
        patterns.add(pattern);
    }

    return patterns;
}

/**
 * 估计不确定性
 */
private UncertaintyEstimation estimateUncertainty(NeuralOutput neuralOutput, 
                                                SymbolicReasoningResult symbolicResult) {
    UncertaintyEstimation uncertainty = new UncertaintyEstimation();

    // 神经网络不确定性（基于softmax熵）
    double neuralUncertainty = calculateNeuralUncertainty(neuralOutput);
    uncertainty.setNeuralUncertainty(neuralUncertainty);

    // 符号推理不确定性（基于规则置信度和冲突）
    double symbolicUncertainty = calculateSymbolicUncertainty(symbolicResult);
    uncertainty.setSymbolicUncertainty(symbolicUncertainty);

    // 总体不确定性
    double overallUncertainty = (neuralUncertainty + symbolicUncertainty) / 2;
    uncertainty.setOverallUncertainty(overallUncertainty);

    return uncertainty;
}

/**
 * 融合事实
 */
private List<FusedFact> fuseFacts(List<SymbolicFact> symbolicFacts, 
                                List<NeuralFact> neuralFacts) {
    List<FusedFact> fusedFacts = new ArrayList<>();

    // 基于相似度匹配和融合事实
    for (SymbolicFact symbolicFact : symbolicFacts) {
        Optional<NeuralFact> matchingNeuralFact = findMatchingNeuralFact(
            symbolicFact, neuralFacts);

        if (matchingNeuralFact.isPresent()) {
            // 融合匹配的事实
            FusedFact fusedFact = fuseMatchingFacts(symbolicFact, matchingNeuralFact.get());
            fusedFacts.add(fusedFact);
        } else {
            // 仅符号事实
            FusedFact fusedFact = new FusedFact(symbolicFact, null, symbolicFact.getConfidence());
            fusedFacts.add(fusedFact);
        }
    }

    // 添加仅神经的事实
    for (NeuralFact neuralFact : neuralFacts) {
        if (fusedFacts.stream().noneMatch(ff -> 
            ff.getNeuralFact() == neuralFact)) {
            FusedFact fusedFact = new FusedFact(null, neuralFact, neuralFact.getConfidence());
            fusedFacts.add(fusedFact);
        }
    }

    return fusedFacts;
}

/**
 * 检测冲突
 */
private List<Conflict> detectConflicts(SymbolicReasoningResult symbolicResult,
                                     NeuralReasoningResult neuralResult) {
    List<Conflict> conflicts = new ArrayList<>();

    // 检测符号推理和神经推理之间的冲突
    for (SymbolicFact symbolicFact : symbolicResult.getRuleResult().getInferredFacts()) {
        for (NeuralFact neuralFact : neuralResult.getNeuralOutput().getPredictedFacts()) {
            if (isConflicting(symbolicFact, neuralFact)) {
                Conflict conflict = new Conflict(
                    symbolicFact, neuralFact, 
                    calculateConflictSeverity(symbolicFact, neuralFact)
                );
                conflicts.add(conflict);
            }
        }
    }

    return conflicts;
}

/**
 * 解决冲突
 */
private List<ConflictResolution> resolveConflicts(List<Conflict> conflicts, 
                                                ReasoningContext context) {
    List<ConflictResolution> resolutions = new ArrayList<>();

    for (Conflict conflict : conflicts) {
        ConflictResolution resolution = resolveConflict(conflict, context);
        resolutions.add(resolution);
    }

    return resolutions;
}

// 辅助方法
private float[] averagePooling(List<float[]> vectors) {
    if (vectors.isEmpty()) return new float[0];

    int dimension = vectors.get(0).length;
    float[] result = new float[dimension];

    for (float[] vector : vectors) {
        for (int i = 0; i < dimension; i++) {
            result[i] += vector[i];
        }
    }

    for (int i = 0; i < dimension; i++) {
        result[i] /= vectors.size();
    }

    return result;
}

private double calculateNeuralUncertainty(NeuralOutput neuralOutput) {
    // 基于输出概率分布的熵计算不确定性
    double[] probabilities = neuralOutput.getProbabilities();
    double entropy = 0.0;

    for (double p : probabilities) {
        if (p > 0) {
            entropy -= p * Math.log(p);
        }
    }

    return entropy / Math.log(probabilities.length); // 归一化熵
}

private double calculateSymbolicUncertainty(SymbolicReasoningResult symbolicResult) {
    // 基于规则置信度和冲突数量计算不确定性
    double avgRuleConfidence = symbolicResult.getRuleResult().getFiredRules().stream()
            .mapToDouble(Rule::getConfidence)
            .average().orElse(1.0);

    int conflictCount = symbolicResult.getRuleResult().getConflicts().size();
    double conflictPenalty = Math.min(conflictCount * 0.1, 0.5);

    return 1.0 - (avgRuleConfidence * (1 - conflictPenalty));
}

private Optional<NeuralFact> findMatchingNeuralFact(SymbolicFact symbolicFact, 
                                                  List<NeuralFact> neuralFacts) {
    return neuralFacts.stream()
            .filter(nf -> isMatching(symbolicFact, nf))
            .max(Comparator.comparingDouble(nf -> 
                calculateFactSimilarity(symbolicFact, nf)));
}

private boolean isMatching(SymbolicFact symbolicFact, NeuralFact neuralFact) {
    // 基于语义相似度的匹配
    double similarity = calculateFactSimilarity(symbolicFact, neuralFact);
    return similarity > 0.7; // 相似度阈值
}

private double calculateFactSimilarity(SymbolicFact sf, NeuralFact nf) {
    float[] sfEmbedding = embeddingModel.embed(sf.toString());
    float[] nfEmbedding = embeddingModel.embed(nf.toString());
    return similarityCalculator.calculate(sfEmbedding, nfEmbedding);
}

private FusedFact fuseMatchingFacts(SymbolicFact symbolicFact, NeuralFact neuralFact) {
    double symbolicWeight = symbolicFact.getConfidence();
    double neuralWeight = neuralFact.getConfidence();
    double totalWeight = symbolicWeight + neuralWeight;

    double fusedConfidence = (symbolicWeight * symbolicFact.getConfidence() + 
                            neuralWeight * neuralFact.getConfidence()) / totalWeight;

    return new FusedFact(symbolicFact, neuralFact, fusedConfidence);
}

private boolean isConflicting(SymbolicFact symbolicFact, NeuralFact neuralFact) {
    // 检查事实是否冲突（如相反的结论）
    // 简化实现
    return symbolicFact.getConclusion().equals("true") && 
           neuralFact.getPrediction().equals("false");
}

private double calculateConflictSeverity(SymbolicFact symbolicFact, NeuralFact neuralFact) {
    // 基于置信度差异计算冲突严重性
    return Math.abs(symbolicFact.getConfidence() - neuralFact.getConfidence());
}

private ConflictResolution resolveConflict(Conflict conflict, ReasoningContext context) {
    // 基于上下文和置信度解决冲突
    double symbolicConfidence = conflict.getSymbolicFact().getConfidence();
    double neuralConfidence = conflict.getNeuralFact().getConfidence();

    ResolutionStrategy strategy;
    ResolvedFact resolvedFact;

    if (symbolicConfidence > neuralConfidence + 0.1) {
        strategy = ResolutionStrategy.PREFER_SYMBOLIC;
        resolvedFact = new ResolvedFact(conflict.getSymbolicFact(), symbolicConfidence);
    } else if (neuralConfidence > symbolicConfidence + 0.1) {
        strategy = ResolutionStrategy.PREFER_NEURAL;
        resolvedFact = new ResolvedFact(conflict.getNeuralFact(), neuralConfidence);
    } else {
        strategy = ResolutionStrategy.COMPROMISE;
        // 创建妥协事实
        resolvedFact = createCompromiseFact(conflict, context);
    }

    return new ConflictResolution(conflict, strategy, resolvedFact);
}

private ResolvedFact createCompromiseFact(Conflict conflict, ReasoningContext context) {
    // 创建妥协方案（如平均预测）
    // 简化实现
    double avgConfidence = (conflict.getSymbolicFact().getConfidence() + 
                          conflict.getNeuralFact().getConfidence()) / 2;
    return new ResolvedFact(conflict.getSymbolicFact(), avgConfidence);
}

private ConstraintModel buildConstraintModel(FusedResult fusedResult, ReasoningContext context) {
    // 构建约束满足问题模型
    ConstraintModel model = new ConstraintModel();

    // 添加变量
    for (FusedFact fact : fusedResult.getFusedFacts()) {
        model.addVariable(fact.toVariable());
    }

    // 添加约束
    for (Constraint constraint : context.getConstraints()) {
        model.addConstraint(constraint);
    }

    // 添加冲突解决约束
    for (ConflictResolution resolution : fusedResult.getConflictResolutions()) {
        model.addConstraint(createResolutionConstraint(resolution));
    }

    return model;
}

private Constraint createResolutionConstraint(ConflictResolution resolution) {
    // 创建基于冲突解决策略的约束
    // 简化实现
    return new LogicalConstraint(resolution.getStrategy().name());
}

private double calculateFusedConfidence(SymbolicReasoningResult symbolicResult,
                                      NeuralReasoningResult neuralResult) {
    double symbolicConfidence = 1.0 - symbolicResult.getRuleResult().getUncertainty();
    double neuralConfidence = 1.0 - neuralResult.getUncertainty().getOverallUncertainty();

    // 加权融合
    return 0.6 * symbolicConfidence + 0.4 * neuralConfidence;
}

// 数据类
@Data
public static class ReasoningResult {
    private boolean success;
    private String errorMessage;
    private SymbolicReasoningResult symbolicResult;
    private NeuralReasoningResult neuralResult;
    private FusedResult fusedResult;
    private ConstraintSolution constraintSolution;
    private Explanation explanation;
    private long reasoningTime;
}

public enum ResolutionStrategy {
    PREFER_SYMBOLIC, PREFER_NEURAL, COMPROMISE, DEFER
}

}
五、 规则学习与知识更新

1. 神经规则学习器

java
// NeuralRuleLearner.java
@Service
@Slf4j
public class NeuralRuleLearner {

private final RuleExtractionModel ruleModel;
private final RuleEvaluationService evaluationService;
private final KnowledgeBase knowledgeBase;

public NeuralRuleLearner(RuleExtractionModel ruleModel,
                       RuleEvaluationService evaluationService,
                       KnowledgeBase knowledgeBase) {
    this.ruleModel = ruleModel;
    this.evaluationService = evaluationService;
    this.knowledgeBase = knowledgeBase;
}

/**
 * 从神经网络中提取符号规则
 */
public RuleExtractionResult extractRules(NeuralNetwork network, ExtractionConfig config) {
    log.info("从神经网络提取符号规则，网络架构: {}", network.getArchitecture());

    RuleExtractionResult result = new RuleExtractionResult();

    try {
        // 1. 激活模式分析
        List<ActivationPattern> activationPatterns = analyzeActivationPatterns(network, config);
        result.setActivationPatterns(activationPatterns);

        // 2. 决策边界提取
        List<DecisionBoundary> decisionBoundaries = extractDecisionBoundaries(network, config);
        result.setDecisionBoundaries(decisionBoundaries);

        // 3. 规则生成
        List<SymbolicRule> extractedRules = generateRules(activationPatterns, decisionBoundaries, config);
        result.setExtractedRules(extractedRules);

        // 4. 规则评估和精炼
        List<RefinedRule> refinedRules = evaluateAndRefineRules(extractedRules, network, config);
        result.setRefinedRules(refinedRules);

        // 5. 规则优化
        List<OptimizedRule> optimizedRules = optimizeRules(refinedRules, config);
        result.setOptimizedRules(optimizedRules);

        result.setSuccess(true);

    } catch (Exception e) {
        log.error("规则提取失败", e);
        result.setSuccess(false);
        result.setErrorMessage(e.getMessage());
    }

    return result;
}

/**
 * 分析神经激活模式
 */
private List<ActivationPattern> analyzeActivationPatterns(NeuralNetwork network, ExtractionConfig config) {
    List<ActivationPattern> patterns = new ArrayList<>();

    // 使用规则提取模型分析激活模式
    ActivationAnalysisResult analysisResult = ruleModel.analyzeActivations(
        network.getActivations(), config);

    for (NeuronCluster cluster : analysisResult.getClusters()) {
        ActivationPattern pattern = new ActivationPattern(
            cluster.getNeurons(),
            cluster.getActivationStrength(),
            cluster.getPatternType()
        );
        patterns.add(pattern);
    }

    return patterns;
}

/**
 * 提取决策边界
 */
private List<DecisionBoundary> extractDecisionBoundaries(NeuralNetwork network, ExtractionConfig config) {
    List<DecisionBoundary> boundaries = new ArrayList<>();

    // 分析网络决策边界
    BoundaryAnalysisResult boundaryResult = ruleModel.analyzeDecisionBoundaries(
        network, config);

    for (FeatureBoundary featureBoundary : boundaryResult.getBoundaries()) {
        DecisionBoundary boundary = new DecisionBoundary(
            featureBoundary.getFeature(),
            featureBoundary.getThreshold(),
            featureBoundary.getDirection(),
            featureBoundary.getConfidence()
        );
        boundaries.add(boundary);
    }

    return boundaries;
}

/**
 * 生成符号规则
 */
private List<SymbolicRule> generateRules(List<ActivationPattern> activationPatterns,
                                       List<DecisionBoundary> decisionBoundaries,
                                       ExtractionConfig config) {
    List<SymbolicRule> rules = new ArrayList<>();

    // 从激活模式生成规则
    for (ActivationPattern pattern : activationPatterns) {
        if (pattern.getStrength() > config.getMinPatternStrength()) {
            SymbolicRule rule = generateRuleFromPattern(pattern, decisionBoundaries);
            if (rule != null) {
                rules.add(rule);
            }
        }
    }

    // 从决策边界生成规则
    for (DecisionBoundary boundary : decisionBoundaries) {
        if (boundary.getConfidence() > config.getMinBoundaryConfidence()) {
            SymbolicRule rule = generateRuleFromBoundary(boundary);
            if (rule != null) {
                rules.add(rule);
            }
        }
    }

    return rules;
}

/**
 * 从激活模式生成规则
 */
private SymbolicRule generateRuleFromPattern(ActivationPattern pattern, 
                                           List<DecisionBoundary> boundaries) {
    // 将神经激活模式转换为逻辑规则
    List<RuleCondition> conditions = new ArrayList<>();

    for (Neuron neuron : pattern.getNeurons()) {
        // 找到与神经元相关的决策边界
        Optional<DecisionBoundary> relatedBoundary = boundaries.stream()
                .filter(b -> b.getFeature().equals(neuron.getFeature()))
                .findFirst();

        if (relatedBoundary.isPresent()) {
            RuleCondition condition = new RuleCondition(
                relatedBoundary.get().getFeature(),
                relatedBoundary.get().getDirection(),
                relatedBoundary.get().getThreshold(),
                neuron.getActivationLevel()
            );
            conditions.add(condition);
        }
    }

    if (conditions.isEmpty()) {
        return null;
    }

    // 创建规则
    String conclusion = inferConclusion(pattern, conditions);
    double confidence = pattern.getStrength() * conditions.stream()
            .mapToDouble(RuleCondition::getConfidence)
            .average().orElse(1.0);

    return new SymbolicRule(conditions, conclusion, confidence, RuleSource.NEURAL_EXTRACTION);
}

/**
 * 从决策边界生成规则
 */
private SymbolicRule generateRuleFromBoundary(DecisionBoundary boundary) {
    RuleCondition condition = new RuleCondition(
        boundary.getFeature(),
        boundary.getDirection(),
        boundary.getThreshold(),
        boundary.getConfidence()
    );

    String conclusion = inferConclusionFromBoundary(boundary);

    return new SymbolicRule(
        List.of(condition), 
        conclusion, 
        boundary.getConfidence(),
        RuleSource.DECISION_BOUNDARY
    );
}

/**
 * 评估和精炼规则
 */
private List<RefinedRule> evaluateAndRefineRules(List<SymbolicRule> extractedRules,
                                               NeuralNetwork network,
                                               ExtractionConfig config) {
    List<RefinedRule> refinedRules = new ArrayList<>();

    for (SymbolicRule rule : extractedRules) {
        RuleEvaluationResult evaluation = evaluationService.evaluateRule(rule, network, config);

        if (evaluation.getQualityScore() > config.getMinRuleQuality()) {
            RefinedRule refinedRule = refineRule(rule, evaluation, config);
            refinedRules.add(refinedRule);
        }
    }

    return refinedRules;
}

/**
 * 精炼规则
 */
private RefinedRule refineRule(SymbolicRule rule, RuleEvaluationResult evaluation, ExtractionConfig config) {
    // 基于评估结果精炼规则
    List<RuleCondition> refinedConditions = refineConditions(rule.getConditions(), evaluation);
    String refinedConclusion = refineConclusion(rule.getConclusion(), evaluation);
    double refinedConfidence = calculateRefinedConfidence(rule, evaluation);

    RefinedRule refinedRule = new RefinedRule(
        refinedConditions,
        refinedConclusion,
        refinedConfidence,
        rule.getSource()
    );

    refinedRule.setOriginalRule(rule);
    refinedRule.setImprovement(evaluation.getImprovementPotential());

    return refinedRule;
}

/**
 * 优化规则集
 */
private List<OptimizedRule> optimizeRules(List<RefinedRule> refinedRules, ExtractionConfig config) {
    List<OptimizedRule> optimizedRules = new ArrayList<>();

    // 规则集优化：去除冗余、解决冲突、提高覆盖度
    RuleSetOptimizationResult optimizationResult = evaluationService.optimizeRuleSet(
        refinedRules, config);

    for (RefinedRule rule : optimizationResult.getOptimizedRules()) {
        OptimizedRule optimizedRule = new OptimizedRule(rule);
        optimizedRule.setOptimizationScore(optimizationResult.getOptimizationScores().get(rule));
        optimizedRules.add(optimizedRule);
    }

    return optimizedRules;
}

/**
 * 增量规则学习
 */
public IncrementalLearningResult learnIncrementally(NewData newData, 
                                                  ExistingRuleSet existingRules,
                                                  IncrementalConfig config) {
    log.info("执行增量规则学习，新数据量: {}", newData.size());

    IncrementalLearningResult result = new IncrementalLearningResult();

    try {
        // 1. 新数据模式分析
        NewPatterns newPatterns = analyzeNewPatterns(newData, existingRules, config);
        result.setNewPatterns(newPatterns);

        // 2. 规则更新检测
        RuleUpdateSuggestions updateSuggestions = detectRuleUpdates(existingRules, newPatterns, config);
        result.setUpdateSuggestions(updateSuggestions);

        // 3. 新规则生成
        List<SymbolicRule> newRules = generateNewRules(newPatterns, updateSuggestions, config);
        result.setNewRules(newRules);

        // 4. 规则集整合
        IntegratedRuleSet integratedRules = integrateRules(existingRules, newRules, updateSuggestions, config);
        result.setIntegratedRuleSet(integratedRules);

        result.setSuccess(true);

    } catch (Exception e) {
        log.error("增量规则学习失败", e);
        result.setSuccess(false);
        result.setErrorMessage(e.getMessage());
    }

    return result;
}

// 辅助方法
private String inferConclusion(ActivationPattern pattern, List<RuleCondition> conditions) {
    // 基于激活模式和条件推断规则结论
    // 简化实现
    return "class_" + pattern.getPatternType().name().toLowerCase();
}

private String inferConclusionFromBoundary(DecisionBoundary boundary) {
    // 基于决策边界推断结论
    return "decision_" + boundary.getFeature() + "_" + boundary.getDirection();
}

private List<RuleCondition> refineConditions(List<RuleCondition> conditions, RuleEvaluationResult evaluation) {
    // 基于评估结果精炼条件
    return conditions.stream()
            .filter(condition -> evaluation.getConditionScores().get(condition) > 0.5)
            .map(this::refineSingleCondition)
            .collect(Collectors.toList());
}

private RuleCondition refineSingleCondition(RuleCondition condition) {
    // 精炼单个条件（如调整阈值）
    return new RuleCondition(
        condition.getFeature(),
        condition.getOperator(),
        adjustThreshold(condition.getThreshold(), condition.getConfidence()),
        condition.getConfidence()
    );
}

private double adjustThreshold(double originalThreshold, double confidence) {
    // 基于置信度调整阈值
    double adjustment = (1 - confidence) * 0.1; // 调整幅度
    return originalThreshold * (1 + adjustment);
}

private String refineConclusion(String originalConclusion, RuleEvaluationResult evaluation) {
    // 精炼结论
    return evaluation.getSuggestedConclusion() != null ? 
           evaluation.getSuggestedConclusion() : originalConclusion;
}

private double calculateRefinedConfidence(SymbolicRule rule, RuleEvaluationResult evaluation) {
    // 计算精炼后的规则置信度
    double originalConfidence = rule.getConfidence();
    double qualityScore = evaluation.getQualityScore();
    return (originalConfidence + qualityScore) / 2;
}

private NewPatterns analyzeNewPatterns(NewData newData, ExistingRuleSet existingRules, IncrementalConfig config) {
    // 分析新数据中的模式
    return new NewPatterns(); // 简化实现
}

private RuleUpdateSuggestions detectRuleUpdates(ExistingRuleSet existingRules, NewPatterns newPatterns, IncrementalConfig config) {
    // 检测需要更新的规则
    return new RuleUpdateSuggestions(); // 简化实现
}

private List<SymbolicRule> generateNewRules(NewPatterns newPatterns, RuleUpdateSuggestions updateSuggestions, IncrementalConfig config) {
    // 生成新规则
    return new ArrayList<>(); // 简化实现
}

private IntegratedRuleSet integrateRules(ExistingRuleSet existingRules, List<SymbolicRule> newRules, RuleUpdateSuggestions updateSuggestions, IncrementalConfig config) {
    // 整合新旧规则
    return new IntegratedRuleSet(); // 简化实现
}

// 数据类
@Data
public static class RuleExtractionResult {
    private boolean success;
    private String errorMessage;
    private List<ActivationPattern> activationPatterns;
    private List<DecisionBoundary> decisionBoundaries;
    private List<SymbolicRule> extractedRules;
    private List<RefinedRule> refinedRules;
    private List<OptimizedRule> optimizedRules;
}

@Data
@AllArgsConstructor
public static class SymbolicRule {
    private List<RuleCondition> conditions;
    private String conclusion;
    private double confidence;
    private RuleSource source;
}

public enum RuleSource {
    NEURAL_EXTRACTION, DECISION_BOUNDARY, KNOWLEDGE_BASE, HUMAN_EXPERT
}

}
六、 可解释决策生成

1. 解释生成器

java
// ExplanationGenerator.java
@Service
@Slf4j
public class ExplanationGenerator {

private final TemplateEngine templateEngine;
private final EvidenceCollector evidenceCollector;
private final JustificationBuilder justificationBuilder;

public ExplanationGenerator(TemplateEngine templateEngine,
                          EvidenceCollector evidenceCollector,
                          JustificationBuilder justificationBuilder) {
    this.templateEngine = templateEngine;
    this.evidenceCollector = evidenceCollector;
    this.justificationBuilder = justificationBuilder;
}

/**
 * 生成决策解释
 */
public Explanation generateExplanation(ReasoningResult reasoningResult, ReasoningContext context) {
    log.info("为决策生成解释，决策类型: {}", context.getDecisionType());

    Explanation explanation = new Explanation();

    try {
        // 1. 收集证据
        List<Evidence> evidence = collectEvidence(reasoningResult, context);
        explanation.setEvidence(evidence);

        // 2. 构建论证链
        ArgumentChain argumentChain = buildArgumentChain(reasoningResult, evidence, context);
        explanation.setArgumentChain(argumentChain);

        // 3. 生成自然语言解释
        String naturalLanguage = generateNaturalLanguageExplanation(argumentChain, context);
        explanation.setNaturalLanguageExplanation(naturalLanguage);

        // 4. 生成可视化解释
        VisualExplanation visualExplanation = generateVisualExplanation(argumentChain, context);
        explanation.setVisualExplanation(visualExplanation);

        // 5. 生成对比解释
        ContrastiveExplanation contrastive = generateContrastiveExplanation(reasoningResult, context);
        explanation.setContrastiveExplanation(contrastive);

        // 6. 计算解释质量
        ExplanationQuality quality = evaluateExplanationQuality(explanation, context);
        explanation.setQuality(quality);

        explanation.setSuccess(true);

    } catch (Exception e) {
        log.error("解释生成失败", e);
        explanation.setSuccess(false);
        explanation.setErrorMessage(e.getMessage());
    }

    return explanation;
}

/**
 * 收集决策证据
 */
private List<Evidence> collectEvidence(ReasoningResult reasoningResult, ReasoningContext context) {
    List<Evidence> evidence = new ArrayList<>();

    // 符号推理证据
    evidence.addAll(collectSymbolicEvidence(reasoningResult.getSymbolicResult()));

    // 神经推理证据
    evidence.addAll(collectNeuralEvidence(reasoningResult.getNeuralResult()));

    // 融合推理证据
    evidence.addAll(collectFusedEvidence(reasoningResult.getFusedResult()));

    // 约束求解证据
    evidence.addAll(collectConstraintEvidence(reasoningResult.getConstraintSolution()));

    // 上下文证据
    evidence.addAll(collectContextEvidence(context));

    return evidence;
}

/**
 * 构建论证链
 */
private ArgumentChain buildArgumentChain(ReasoningResult reasoningResult, 
                                       List<Evidence> evidence,
                                       ReasoningContext context) {
    ArgumentChain chain = new ArgumentChain();

    // 构建主要论证
    MainArgument mainArgument = buildMainArgument(reasoningResult, evidence, context);
    chain.setMainArgument(mainArgument);

    // 构建支持论证
    List<SupportingArgument> supportingArguments = buildSupportingArguments(evidence, context);
    chain.setSupportingArguments(supportingArguments);

    // 构建反论证和反驳
    List<CounterArgument> counterArguments = buildCounterArguments(reasoningResult, context);
    chain.setCounterArguments(counterArguments);

    List<Rebuttal> rebuttals = buildRebuttals(counterArguments, evidence, context);
    chain.setRebuttals(rebuttals);

    // 计算论证强度
    double argumentStrength = calculateArgumentStrength(chain, context);
    chain.setOverallStrength(argumentStrength);

    return chain;
}

/**
 * 生成自然语言解释
 */
private String generateNaturalLanguageExplanation(ArgumentChain argumentChain, ReasoningContext context) {
    StringBuilder explanation = new StringBuilder();

    // 主要结论
    explanation.append("基于分析，系统得出以下结论：")
              .append(argumentChain.getMainArgument().getConclusion())
              .append("。\n\n");

    // 主要理由
    explanation.append("主要理由包括：\n");
    for (SupportingArgument supportingArg : argumentChain.getSupportingArguments()) {
        explanation.append("- ").append(supportingArg.getReason()).append("\n");
    }

    // 处理的不确定性
    if (argumentChain.getMainArgument().getUncertainty() > 0.1) {
        explanation.append("\n需要注意的是，这个结论存在一定不确定性（")
                  .append(String.format("%.1f", argumentChain.getMainArgument().getUncertainty() * 100))
                  .append("%），主要因为：\n");

        for (CounterArgument counterArg : argumentChain.getCounterArguments()) {
            explanation.append("- ").append(counterArg.getChallenge()).append("\n");
        }
    }

    // 置信度说明
    explanation.append("\n总体置信度：")
              .append(String.format("%.1f", argumentChain.getOverallStrength() * 100))
              .append("%");

    return explanation.toString();
}

/**
 * 生成可视化解释
 */
private VisualExplanation generateVisualExplanation(ArgumentChain argumentChain, ReasoningContext context) {
    VisualExplanation visual = new VisualExplanation();

    // 创建决策树可视化
    DecisionTreeVisualization treeViz = createDecisionTree(argumentChain);
    visual.setDecisionTree(treeViz);

    // 创建证据网络可视化
    EvidenceNetwork evidenceNetwork = createEvidenceNetwork(argumentChain.getSupportingArguments());
    visual.setEvidenceNetwork(evidenceNetwork);

    // 创建不确定性可视化
    UncertaintyVisualization uncertaintyViz = createUncertaintyVisualization(argumentChain);
    visual.setUncertaintyVisualization(uncertaintyViz);

    return visual;
}

/**
 * 生成对比解释
 */
private ContrastiveExplanation generateContrastiveExplanation(ReasoningResult reasoningResult, 
                                                            ReasoningContext context) {
    ContrastiveExplanation contrastive = new ContrastiveExplanation();

    // 为什么是这个结论而不是其他可能结论
    List<AlternativeScenario> alternatives = generateAlternativeScenarios(reasoningResult, context);
    contrastive.setAlternativeScenarios(alternatives);

    // 关键区分因素
    List<DiscriminatingFactor> discriminators = identifyDiscriminatingFactors(reasoningResult, alternatives);
    contrastive.setDiscriminatingFactors(discriminators);

    // 敏感性分析
    SensitivityAnalysis sensitivity = performSensitivityAnalysis(reasoningResult, context);
    contrastive.setSensitivityAnalysis(sensitivity);

    return contrastive;
}

/**
 * 评估解释质量
 */
private ExplanationQuality evaluateExplanationQuality(Explanation explanation, ReasoningContext context) {
    ExplanationQuality quality = new ExplanationQuality();

    // 完整性评估
    double completeness = evaluateCompleteness(explanation, context);
    quality.setCompleteness(completeness);

    // 可理解性评估
    double comprehensibility = evaluateComprehensibility(explanation, context);
    quality.setComprehensibility(comprehensibility);

    // 可信度评估
    double trustworthiness = evaluateTrustworthiness(explanation, context);
    quality.setTrustworthiness(trustworthiness);

    // 相关性评估
    double relevance = evaluateRelevance(explanation, context);
    quality.setRelevance(relevance);

    // 总体质量
    double overallQuality = (completeness + comprehensibility + trustworthiness + relevance) / 4;
    quality.setOverallQuality(overallQuality);

    return quality;
}

// 证据收集方法
private List<Evidence> collectSymbolicEvidence(SymbolicReasoningResult symbolicResult) {
    List<Evidence> evidence = new ArrayList<>();

    // 规则执行证据
    for (FiredRule firedRule : symbolicResult.getRuleResult().getFiredRules()) {
        Evidence ruleEvidence = new Evidence(
            "规则应用: " + firedRule.getRule().getName(),
            firedRule.getConfidence(),
            EvidenceType.RULE_APPLICATION
        );
        ruleEvidence.setDetails(firedRule.getMatchedFacts());
        evidence.add(ruleEvidence);
    }

    // 逻辑推理证据
    for (InferredFact inferredFact : symbolicResult.getQueryResult().getInferredFacts()) {
        Evidence inferenceEvidence = new Evidence(
            "逻辑推导: " + inferredFact.getFact(),
            inferredFact.getConfidence(),
            EvidenceType.LOGICAL_INFERENCE
        );
        evidence.add(inferenceEvidence);
    }

    return evidence;
}

private List<Evidence> collectNeuralEvidence(NeuralReasoningResult neuralResult) {
    List<Evidence> evidence = new ArrayList<>();

    // 神经网络预测证据
    for (NeuralPrediction prediction : neuralResult.getNeuralOutput().getPredictions()) {
        Evidence predictionEvidence = new Evidence(
            "神经网络预测: " + prediction.getLabel(),
            prediction.getConfidence(),
            EvidenceType.NEURAL_PREDICTION
        );
        predictionEvidence.setDetails(prediction.getTopFeatures());
        evidence.add(predictionEvidence);
    }

    // 神经模式证据
    for (NeuralPattern pattern : neuralResult.getPatterns()) {
        Evidence patternEvidence = new Evidence(
            "检测到模式: " + pattern.getPatternType(),
            pattern.getStrength(),
            EvidenceType.NEURAL_PATTERN
        );
        evidence.add(patternEvidence);
    }

    return evidence;
}

private List<Evidence> collectFusedEvidence(FusedResult fusedResult) {
    List<Evidence> evidence = new ArrayList<>();

    // 融合结果证据
    for (FusedFact fusedFact : fusedResult.getFusedFacts()) {
        Evidence fusedEvidence = new Evidence(
            "融合事实: " + fusedFact.toString(),
            fusedFact.getConfidence(),
            EvidenceType.FUSED_RESULT
        );
        evidence.add(fusedEvidence);
    }

    // 冲突解决证据
    for (ConflictResolution resolution : fusedResult.getConflictResolutions()) {
        Evidence resolutionEvidence = new Evidence(
            "冲突解决: " + resolution.getStrategy(),
            resolution.getResolvedFact().getConfidence(),
            EvidenceType.CONFLICT_RESOLUTION
        );
        evidence.add(resolutionEvidence);
    }

    return evidence;
}

private List<Evidence> collectConstraintEvidence(ConstraintSolution constraintSolution) {
    List<Evidence> evidence = new ArrayList<>();

    // 约束满足证据
    if (constraintSolution.isSatisfiable()) {
        Evidence constraintEvidence = new Evidence(
            "所有约束条件得到满足",
            1.0,
            EvidenceType.CONSTRAINT_SATISFACTION
        );
        evidence.add(constraintEvidence);
    } else {
        Evidence constraintEvidence = new Evidence(
            "部分约束条件无法同时满足",
            0.5,
            EvidenceType.CONSTRAINT_VIOLATION
        );
        evidence.add(constraintEvidence);
    }

    return evidence;
}

private List<Evidence> collectContextEvidence(ReasoningContext context) {
    List<Evidence> evidence = new ArrayList<>();

    // 领域知识证据
    for (DomainKnowledge knowledge : context.getDomainKnowledge()) {
        Evidence knowledgeEvidence = new Evidence(
            "领域知识: " + knowledge.getDescription(),
            knowledge.getRelevance(),
            EvidenceType.DOMAIN_KNOWLEDGE
        );
        evidence.add(knowledgeEvidence);
    }

    return evidence;
}

// 论证构建方法
private MainArgument buildMainArgument(ReasoningResult reasoningResult, List<Evidence> evidence, ReasoningContext context) {
    // 基于推理结果构建主要论证
    String conclusion = deriveMainConclusion(reasoningResult, context);
    double confidence = reasoningResult.getFusedResult().getOverallConfidence();
    double uncertainty = 1 - confidence;

    List<Evidence> supportingEvidence = evidence.stream()
            .filter(e -> e.getConfidence() > 0.7)
            .collect(Collectors.toList());

    return new MainArgument(conclusion, confidence, uncertainty, supportingEvidence);
}

private List<SupportingArgument> buildSupportingArguments(List<Evidence> evidence, ReasoningContext context) {
    return evidence.stream()
            .filter(e -> e.getConfidence() > 0.6)
            .map(e -> new SupportingArgument(
                "基于" + e.getType().getDescription(),
                e.getDescription(),
                e.getConfidence()
            ))
            .collect(Collectors.toList());
}

private List<CounterArgument> buildCounterArguments(ReasoningResult reasoningResult, ReasoningContext context) {
    List<CounterArgument> counterArguments = new ArrayList<>();

    // 基于不确定性的反论证
    if (reasoningResult.getNeuralResult().getUncertainty().getOverallUncertainty() > 0.3) {
        counterArguments.add(new CounterArgument(
            "神经网络预测不确定性较高",
            "神经网络的预测置信度较低，可能影响结论可靠性",
            reasoningResult.getNeuralResult().getUncertainty().getOverallUncertainty()
        ));
    }

    // 基于冲突的反论证
    if (!reasoningResult.getFusedResult().getConflicts().isEmpty()) {
        counterArguments.add(new CounterArgument(
            "存在推理冲突",
            "符号推理和神经推理在某些方面存在不一致",
            0.5
        ));
    }

    return counterArguments;
}

private List<Rebuttal> buildRebuttals(List<CounterArgument> counterArguments, List<Evidence> evidence, ReasoningContext context) {
    List<Rebuttal> rebuttals = new ArrayList<>();

    for (CounterArgument counterArg : counterArguments) {
        // 为每个反论证构建反驳
        String rebuttalText = "虽然" + counterArg.getChallenge() + 
                            "，但融合推理和约束求解提供了额外的验证";
        Rebuttal rebuttal = new Rebuttal(counterArg, rebuttalText, 0.7);
        rebuttals.add(rebuttal);
    }

    return rebuttals;
}

// 辅助方法
private String deriveMainConclusion(ReasoningResult reasoningResult, ReasoningContext context) {
    // 从推理结果推导主要结论
    FusedResult fusedResult = reasoningResult.getFusedResult();

    if (fusedResult.getFusedFacts().isEmpty()) {
        return "无法得出明确结论";
    }

    // 选择置信度最高的事实作为主要结论
    FusedFact topFact = fusedResult.getFusedFacts().stream()
            .max(Comparator.comparingDouble(FusedFact::getConfidence))
            .orElse(fusedResult.getFusedFacts().get(0));

    return topFact.toString();
}

private double calculateArgumentStrength(ArgumentChain chain, ReasoningContext context) {
    double mainStrength = chain.getMainArgument().getConfidence();
    double supportStrength = chain.getSupportingArguments().stream()
            .mapToDouble(SupportingArgument::getStrength)
            .average().orElse(1.0);
    double counterImpact = chain.getCounterArguments().stream()
            .mapToDouble(CounterArgument::getImpact)
            .average().orElse(0.0);

    return mainStrength * supportStrength * (1 - counterImpact);
}

private double evaluateCompleteness(Explanation explanation, ReasoningContext context) {
    // 评估解释的完整性
    int expectedElements = 5; // 证据、论证、自然语言、可视化、对比
    int presentElements = 0;

    if (!explanation.getEvidence().isEmpty()) presentElements++;
    if (explanation.getArgumentChain() != null) presentElements++;
    if (explanation.getNaturalLanguageExplanation() != null) presentElements++;
    if (explanation.getVisualExplanation() != null) presentElements++;
    if (explanation.getContrastiveExplanation() != null) presentElements++;

    return (double) presentElements / expectedElements;
}

private double evaluateComprehensibility(Explanation explanation, ReasoningContext context) {
    // 评估解释的可理解性
    String naturalLanguage = explanation.getNaturalLanguageExplanation();
    if (naturalLanguage == null) return 0.0;

    // 简化评估：基于句子长度和复杂度
    double avgSentenceLength = calculateAverageSentenceLength(naturalLanguage);
    double complexityScore = 1.0 - Math.min(avgSentenceLength / 50, 1.0); // 句子越短越易理解

    return complexityScore;
}

private double evaluateTrustworthiness(Explanation explanation, ReasoningContext context) {
    // 评估解释的可信度
    double evidenceQuality = explanation.getEvidence().stream()
            .mapToDouble(Evidence::getConfidence)
            .average().orElse(0.0);

    double argumentStrength = explanation.getArgumentChain().getOverallStrength();

    return (evidenceQuality + argumentStrength) / 2;
}

private double evaluateRelevance(Explanation explanation, ReasoningContext context) {
    // 评估解释的相关性
    return 0.8; // 简化实现
}

private double calculateAverageSentenceLength(String text) {
    String[] sentences = text.split("[.!?]+");
    if (sentences.length == 0) return 0.0;

    double totalLength = 0.0;
    for (String sentence : sentences) {
        totalLength += sentence.trim().split("\\s+").length;
    }

    return totalLength / sentences.length;
}

// 可视化创建方法（简化实现）
private DecisionTreeVisualization createDecisionTree(ArgumentChain argumentChain) {
    return new DecisionTreeVisualization(); // 简化实现
}

private EvidenceNetwork createEvidenceNetwork(List<SupportingArgument> supportingArguments) {
    return new EvidenceNetwork(); // 简化实现
}

private UncertaintyVisualization createUncertaintyVisualization(ArgumentChain argumentChain) {
    return new UncertaintyVisualization(); // 简化实现
}

private List<AlternativeScenario> generateAlternativeScenarios(ReasoningResult reasoningResult, ReasoningContext context) {
    return new ArrayList<>(); // 简化实现
}

private List<DiscriminatingFactor> identifyDiscriminatingFactors(ReasoningResult reasoningResult, List<AlternativeScenario> alternatives) {
    return new ArrayList<>(); // 简化实现
}

private SensitivityAnalysis performSensitivityAnalysis(ReasoningResult reasoningResult, ReasoningContext context) {
    return new SensitivityAnalysis(); // 简化实现
}

// 数据类
@Data
public static class Explanation {
    private boolean success;
    private String errorMessage;
    private List<Evidence> evidence;
    private ArgumentChain argumentChain;
    private String naturalLanguageExplanation;
    private VisualExplanation visualExplanation;
    private ContrastiveExplanation contrastiveExplanation;
    private ExplanationQuality quality;
}

@Data
@AllArgsConstructor
public static class Evidence {
    private String description;
    private double confidence;
    private EvidenceType type;
    private Object details;

    public Evidence(String description, double confidence, EvidenceType type) {
        this(description, confidence, type, null);
    }
}

public enum EvidenceType {
    RULE_APPLICATION("规则应用"),
    LOGICAL_INFERENCE("逻辑推理"),
    NEURAL_PREDICTION("神经网络预测"),
    NEURAL_PATTERN("神经模式"),
    FUSED_RESULT("融合结果"),
    CONFLICT_RESOLUTION("冲突解决"),
    CONSTRAINT_SATISFACTION("约束满足"),
    CONSTRAINT_VIOLATION("约束违反"),
    DOMAIN_KNOWLEDGE("领域知识");

    private final String description;

    EvidenceType(String description) {
        this.description = description;
    }

    public String getDescription() {
        return description;
    }
}

}
七、 应用场景与REST API

1. 医疗诊断决策支持

java
// MedicalDiagnosisService.java
@Service
@Slf4j
public class MedicalDiagnosisService {

private final NeuroSymbolicReasoner reasoner;
private final NeuralSymbolicConverter converter;
private final MedicalKnowledgeBase medicalKB;

public MedicalDiagnosisService(NeuroSymbolicReasoner reasoner,
                             NeuralSymbolicConverter converter,
                             MedicalKnowledgeBase medicalKB) {
    this.reasoner = reasoner;
    this.converter = converter;
    this.medicalKB = medicalKB;
}

/**
 * 执行神经符号医疗诊断
 */
public MedicalDiagnosisResult diagnose(MedicalCase medicalCase) {
    log.info("执行医疗诊断，患者: {}", medicalCase.getPatientId());

    MedicalDiagnosisResult result = new MedicalDiagnosisResult();

    try {
        // 1. 符号化医疗数据
        SymbolicRepresentation symbolicCase = convertMedicalData(medicalCase);
        result.setSymbolicRepresentation(symbolicCase);

        // 2. 构建推理请求
        ReasoningRequest reasoningRequest = buildDiagnosisRequest(symbolicCase, medicalCase);

        // 3. 执行神经符号推理
        ReasoningResult reasoningResult = reasoner.reason(reasoningRequest);
        result.setReasoningResult(reasoningResult);

        // 4. 生成诊断建议
        DiagnosisRecommendation recommendation = generateDiagnosisRecommendation(reasoningResult, medicalCase);
        result.setRecommendation(recommendation);

        // 5. 生成诊断解释
        MedicalExplanation explanation = generateMedicalExplanation(reasoningResult, medicalCase);
        result.setExplanation(explanation);

        result.setSuccess(true);

    } catch (Exception e) {
        log.error("医疗诊断失败", e);
        result.setSuccess(false);
        result.setErrorMessage(e.getMessage());
    }

    return result;
}

/**
 * 转换医疗数据为符号表示
 */
private SymbolicRepresentation convertMedicalData(MedicalCase medicalCase) {
    SymbolicRepresentation representation = new SymbolicRepresentation();

    // 转换症状
    for (Symptom symptom : medicalCase.getSymptoms()) {
        SymbolicConcept concept = new SymbolicConcept(
            symptom.getName(),
            "Symptom",
            symptom.getSeverity(),
            ConceptType.ENTITY
        );
        concept.addAttribute("duration", symptom.getDuration());
        concept.addAttribute("intensity", symptom.getIntensity());
        representation.addConcept(concept);
    }

    // 转换检验结果
    for (LabTest test : medicalCase.getLabTests()) {
        SymbolicConcept concept = new SymbolicConcept(
            test.getTestName(),
            "LabTest",
            test.getConfidence(),
            ConceptType.ENTITY
        );
        concept.addAttribute("value", test.getValue());
        concept.addAttribute("unit", test.getUnit());
        concept.addAttribute("reference_range", test.getReferenceRange());
        representation.addConcept(concept);
    }

    // 转换病史
    for (MedicalHistory history : medicalCase.getMedicalHistory()) {
        SymbolicConcept concept = new SymbolicConcept(
            history.getCondition(),
            "MedicalHistory",
            1.0,
            ConceptType.ENTITY
        );
        concept.addAttribute("year", history.getYear());
        concept.addAttribute("severity", history.getSeverity());
        representation.addConcept(concept);
    }

    return representation;
}

/**
 * 构建诊断推理请求
 */
private ReasoningRequest buildDiagnosisRequest(SymbolicRepresentation symbolicCase, MedicalCase medicalCase) {
    ReasoningRequest request = new ReasoningRequest();

    // 设置查询：可能的诊断
    request.setQuery("findPossibleDiagnoses");

    // 设置事实：症状、检验结果、病史
    request.setFacts(extractFacts(symbolicCase));

    // 设置规则：医疗诊断规则
    request.setRules(medicalKB.getDiagnosisRules());

    // 设置约束：医疗约束（如排除标准）
    request.setConstraints(medicalKB.getMedicalConstraints());

    // 设置上下文：患者信息
    request.setContext(buildMedicalContext(medicalCase));

    return request;
}

/**
 * 生成诊断建议
 */
private DiagnosisRecommendation generateDiagnosisRecommendation(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    DiagnosisRecommendation recommendation = new DiagnosisRecommendation();

    // 提取可能的诊断
    List<PossibleDiagnosis> possibleDiagnoses = extractPossibleDiagnoses(reasoningResult);
    recommendation.setPossibleDiagnoses(possibleDiagnoses);

    // 推荐进一步检查
    List<RecommendedTest> recommendedTests = recommendFurtherTests(possibleDiagnoses, medicalCase);
    recommendation.setRecommendedTests(recommendedTests);

    // 生成治疗建议
    List<TreatmentOption> treatmentOptions = generateTreatmentOptions(possibleDiagnoses, medicalCase);
    recommendation.setTreatmentOptions(treatmentOptions);

    // 计算诊断置信度
    double overallConfidence = calculateDiagnosisConfidence(possibleDiagnoses);
    recommendation.setOverallConfidence(overallConfidence);

    return recommendation;
}

/**
 * 生成医疗解释
 */
private MedicalExplanation generateMedicalExplanation(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    MedicalExplanation explanation = new MedicalExplanation();

    // 基于推理结果生成医疗解释
    Explanation generalExplanation = reasoningResult.getExplanation();
    explanation.setGeneralExplanation(generalExplanation);

    // 生成针对患者的个性化解释
    String personalizedExplanation = personalizeExplanation(generalExplanation, medicalCase);
    explanation.setPersonalizedExplanation(personalizedExplanation);

    // 生成风险说明
    RiskAssessment riskAssessment = assessRisks(reasoningResult, medicalCase);
    explanation.setRiskAssessment(riskAssessment);

    // 生成后续建议
    FollowupAdvice followupAdvice = generateFollowupAdvice(reasoningResult, medicalCase);
    explanation.setFollowupAdvice(followupAdvice);

    return explanation;
}

// 辅助方法
private List<SymbolicFact> extractFacts(SymbolicRepresentation symbolicCase) {
    return symbolicCase.getConcepts().stream()
            .map(concept -> new SymbolicFact(concept.getName(), concept.getType(), concept.getConfidence()))
            .collect(Collectors.toList());
}

private ReasoningContext buildMedicalContext(MedicalCase medicalCase) {
    ReasoningContext context = new ReasoningContext();
    context.setDomain("medical");
    context.setPatientAge(medicalCase.getPatientAge());
    context.setPatientGender(medicalCase.getPatientGender());
    context.setComorbidities(medicalCase.getComorbidities());
    return context;
}

private List<PossibleDiagnosis> extractPossibleDiagnoses(ReasoningResult reasoningResult) {
    List<PossibleDiagnosis> diagnoses = new ArrayList<>();

    // 从推理结果中提取诊断
    for (FusedFact fact : reasoningResult.getFusedResult().getFusedFacts()) {
        if (fact.toString().contains("diagnosis")) {
            PossibleDiagnosis diagnosis = new PossibleDiagnosis(
                extractDiagnosisName(fact),
                fact.getConfidence(),
                extractSupportingEvidence(fact, reasoningResult)
            );
            diagnoses.add(diagnosis);
        }
    }

    // 按置信度排序
    diagnoses.sort(Comparator.comparingDouble(PossibleDiagnosis::getConfidence).reversed());

    return diagnoses;
}

private List<RecommendedTest> recommendFurtherTests(List<PossibleDiagnosis> diagnoses, MedicalCase medicalCase) {
    List<RecommendedTest> tests = new ArrayList<>();

    // 基于可能的诊断推荐进一步检查
    for (PossibleDiagnosis diagnosis : diagnoses) {
        if (diagnosis.getConfidence() < 0.8) {
            // 置信度不足，推荐鉴别诊断检查
            List<String> differentialTests = medicalKB.getDifferentialTests(diagnosis.getName());
            for (String test : differentialTests) {
                tests.add(new RecommendedTest(test, "鉴别诊断", 0.7));
            }
        }
    }

    return tests;
}

private List<TreatmentOption> generateTreatmentOptions(List<PossibleDiagnosis> diagnoses, MedicalCase medicalCase) {
    List<TreatmentOption> options = new ArrayList<>();

    // 为每个诊断生成治疗选项
    for (PossibleDiagnosis diagnosis : diagnoses) {
        if (diagnosis.getConfidence() > 0.7) {
            List<Treatment> treatments = medicalKB.getRecommendedTreatments(
                diagnosis.getName(), medicalCase);
            for (Treatment treatment : treatments) {
                options.add(new TreatmentOption(treatment, diagnosis.getConfidence()));
            }
        }
    }

    return options;
}

private double calculateDiagnosisConfidence(List<PossibleDiagnosis> diagnoses) {
    if (diagnoses.isEmpty()) return 0.0;

    // 使用最高诊断置信度作为总体置信度
    return diagnoses.get(0).getConfidence();
}

private String extractDiagnosisName(FusedFact fact) {
    // 从事实中提取诊断名称
    return fact.toString().replace("diagnosis_", "");
}

private List<String> extractSupportingEvidence(FusedFact fact, ReasoningResult reasoningResult) {
    // 提取支持该诊断的证据
    List<String> evidence = new ArrayList<>();

    for (Evidence e : reasoningResult.getExplanation().getEvidence()) {
        if (e.getDescription().contains(extractDiagnosisName(fact))) {
            evidence.add(e.getDescription());
        }
    }

    return evidence;
}

private String personalizeExplanation(Explanation generalExplanation, MedicalCase medicalCase) {
    // 个性化解释：考虑患者特定情况
    String baseExplanation = generalExplanation.getNaturalLanguageExplanation();

    return baseExplanation + "\n\n考虑到患者年龄(" + medicalCase.getPatientAge() + 
           "岁)和病史，建议密切监测病情变化。";
}

private RiskAssessment assessRisks(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    RiskAssessment assessment = new RiskAssessment();

    // 评估诊断风险
    double riskScore = calculateRiskScore(reasoningResult, medicalCase);
    assessment.setOverallRisk(riskScore);

    // 识别具体风险
    List<SpecificRisk> specificRisks = identifySpecificRisks(reasoningResult, medicalCase);
    assessment.setSpecificRisks(specificRisks);

    // 生成风险缓解建议
    List<RiskMitigation> mitigations = generateRiskMitigations(specificRisks);
    assessment.setRiskMitigations(mitigations);

    return assessment;
}

private FollowupAdvice generateFollowupAdvice(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    FollowupAdvice advice = new FollowupAdvice();

    // 生成随访建议
    advice.setFollowupSchedule(generateFollowupSchedule(reasoningResult, medicalCase));
    advice.setMonitoringRecommendations(generateMonitoringRecommendations(reasoningResult, medicalCase));
    advice.setWarningSigns(identifyWarningSigns(reasoningResult, medicalCase));

    return advice;
}

// 简化实现的方法
private double calculateRiskScore(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    return 0.3; // 简化实现
}

private List<SpecificRisk> identifySpecificRisks(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    return new ArrayList<>(); // 简化实现
}

private List<RiskMitigation> generateRiskMitigations(List<SpecificRisk> specificRisks) {
    return new ArrayList<>(); // 简化实现
}

private String generateFollowupSchedule(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    return "建议1周后复诊"; // 简化实现
}

private List<String> generateMonitoringRecommendations(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    return List.of("监测体温", "观察症状变化"); // 简化实现
}

private List<String> identifyWarningSigns(ReasoningResult reasoningResult, MedicalCase medicalCase) {
    return List.of("症状加重", "出现新症状"); // 简化实现
}

// 数据类
@Data
public static class MedicalDiagnosisResult {
    private boolean success;
    private String errorMessage;
    private SymbolicRepresentation symbolicRepresentation;
    private ReasoningResult reasoningResult;
    private DiagnosisRecommendation recommendation;
    private MedicalExplanation explanation;
}

@Data
@AllArgsConstructor
public static class PossibleDiagnosis {
    private String name;
    private double confidence;
    private List<String> supportingEvidence;
}

}
八、 生产配置与API服务

1. Spring Boot配置

yaml

application.yml

spring:
application:
name: neuro-symbolic-ai-service

neuro-symbolic:
reasoning:
max-depth: 10
timeout-ms: 30000
enable-explanation: true

converter:
min-confidence: 0.5
enable-grounding: true

rules:
medical:
path: classpath:rules/medical.drl
financial:
path: classpath:rules/financial.drl
industrial:
path: classpath:rules/industrial.drl

knowledge-base:
medical-ontology: classpath:ontology/medical.owl
general-ontology: classpath:ontology/general.owl

server:
port: 8080

logging:
level:
com.example.neurosymbolic: INFO
file:
name: /var/log/neuro-symbolic-service.log

management:
endpoints:
web:
exposure:
include: health,metrics,info
endpoint:
health:
show-details: always

1. REST API控制器

java
// NeuroSymbolicController.java
@RestController
@RequestMapping("/api/neurosymbolic")
@Slf4j
public class NeuroSymbolicController {

private final NeuroSymbolicReasoner reasoner;
private final NeuralSymbolicConverter converter;
private final MedicalDiagnosisService medicalService;

public NeuroSymbolicController(NeuroSymbolicReasoner reasoner,
                             NeuralSymbolicConverter converter,
                             MedicalDiagnosisService medicalService) {
    this.reasoner = reasoner;
    this.converter = converter;
    this.medicalService = medicalService;
}

@PostMapping("/reason")
public ResponseEntity<ReasoningResponse> performReasoning(@RequestBody ReasoningRequest request) {
    try {
        ReasoningResult result = reasoner.reason(request);
        return ResponseEntity.ok(ReasoningResponse.success(result));
    } catch (Exception e) {
        log.error("推理执行失败", e);
        return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body(ReasoningResponse.error(e.getMessage()));
    }
}

@PostMapping("/convert")
public ResponseEntity<ConversionResponse> convertToSymbols(@RequestBody ConversionRequest request) {
    try {
        SymbolicRepresentation representation = converter.extractSymbols(
            request.getData(), request.getConfig());
        return ResponseEntity.ok(ConversionResponse.success(representation));
    } catch (Exception e) {
        log.error("符号转换失败", e);
        return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body(ConversionResponse.error(e.getMessage()));
    }
}

@PostMapping("/medical/diagnose")
public ResponseEntity<MedicalDiagnosisResponse> performMedicalDiagnosis(
        @RequestBody MedicalDiagnosisRequest request) {
    try {
        MedicalDiagnosisService.MedicalDiagnosisResult result = 
            medicalService.diagnose(request.getMedicalCase());
        return ResponseEntity.ok(MedicalDiagnosisResponse.success(result));
    } catch (Exception e) {
        log.error("医疗诊断失败", e);
        return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body(MedicalDiagnosisResponse.error(e.getMessage()));
    }
}

@PostMapping("/explain")
public ResponseEntity<ExplanationResponse> generateExplanation(@RequestBody ExplanationRequest request) {
    try {
        Explanation explanation = explanationGenerator.generateExplanation(
            request.getReasoningResult(), request.getContext());
        return ResponseEntity.ok(ExplanationResponse.success(explanation));
    } catch (Exception e) {
        log.error("解释生成失败", e);
        return ResponseEntity.status(HttpStatus.INTERNAL_SERVER_ERROR)
                .body(ExplanationResponse.error(e.getMessage()));
    }
}

@GetMapping("/health")
public ResponseEntity<HealthResponse> healthCheck() {
    Map<String, Object> details = new HashMap<>();
    details.put("status", "healthy");
    details.put("timestamp", System.currentTimeMillis());
    details.put("services", List.of("reasoning", "conversion", "medical", "explanation"));

    return ResponseEntity.ok(new HealthResponse("success", "服务运行正常", details));
}

// DTO类
@Data
public static class ReasoningRequest {
    private String query;
    private List<SymbolicFact> facts;
    private List<Rule> rules;
    private List<Constraint> constraints;
    private ReasoningContext context;
}

@Data
@AllArgsConstructor
public static class ReasoningResponse {
    private String status;
    private String message;
    private ReasoningResult result;

    public static ReasoningResponse success(ReasoningResult result) {
        return new ReasoningResponse("success", "推理完成", result);
    }

    public static ReasoningResponse error(String message) {
        return new ReasoningResponse("error", message, null);
    }
}

@Data
public static class ConversionRequest {
    private RawData data;
    private ExtractionConfig config;
}

@Data
@AllArgsConstructor
public static class ConversionResponse {
    private String status;
    private String message;
    private SymbolicRepresentation representation;

    public static ConversionResponse success(SymbolicRepresentation representation) {
        return new ConversionResponse("success", "转换完成", representation);
    }

    public static ConversionResponse error(String message) {
        return new ConversionResponse("error", message, null);
    }
}

@Data
public static class MedicalDiagnosisRequest {
    private MedicalCase medicalCase;
}

@Data
@AllArgsConstructor
public static class MedicalDiagnosisResponse {
    private String status;
    private String message;
    private MedicalDiagnosisService.MedicalDiagnosisResult result;

    public static MedicalDiagnosisResponse success(MedicalDiagnosisService.MedicalDiagnosisResult result) {
        return new MedicalDiagnosisResponse("success", "诊断完成", result);
    }

    public static MedicalDiagnosisResponse error(String message) {
        return new MedicalDiagnosisResponse("error", message, null);
    }
}

@Data
public static class ExplanationRequest {
    private ReasoningResult reasoningResult;
    private ReasoningContext context;
}

@Data
@AllArgsConstructor
public static class ExplanationResponse {
    private String status;
    private String message;
    private Explanation explanation;

    public static ExplanationResponse success(Explanation explanation) {
        return new ExplanationResponse("success", "解释生成完成", explanation);
    }

    public static ExplanationResponse error(String message) {
        return new ExplanationResponse("error", message, null);
    }
}

@Data
@AllArgsConstructor
public static class HealthResponse {
    private String status;
    private String message;
    private Map<String, Object> details;
}

}
九、 应用场景与总结

1. 典型应用场景

医疗诊断：结合医学知识和患者数据提供可解释诊断

金融风控：融合规则引擎和深度学习检测复杂欺诈模式

工业质检：将专家经验与视觉检测结合提高检测精度

法律分析：结合法律条文和案例推理提供法律意见

教育评估：整合教育理论和学习数据分析学习效果

1. 系统优势总结

可解释性：提供基于符号逻辑的透明决策过程

知识融合：结合先验知识和数据驱动学习

持续进化：支持规则学习和知识更新

信任建立：通过解释生成增强用户信任

灵活适应：适用于多种复杂决策场景

1. 技术挑战与解决方案

符号神经接口：通过概念提取和落地实现双向转换

推理一致性：使用约束求解和冲突解决维护一致性

解释生成：基于论证理论和证据链生成可信解释

系统性能：通过优化推理算法和缓存提高性能

1. 总结

通过本文的实践，我们成功构建了一个完整的Java神经符号AI系统，具备以下核心能力：

双向转换：神经网络与符号表示间的无缝转换

混合推理：符号推理与神经推理的协同工作

规则学习：从数据中自动学习和优化符号规则

可解释输出：生成透明、可信的决策解释

领域适应：可配置的领域知识和推理规则

神经符号AI代表了AI发展的一个重要方向，将连接主义的感知能力与符号主义的推理能力相结合。Java在企业级系统中的成熟生态与神经符号AI的理论优势相结合，为构建下一代可信AI系统提供了强大的技术基础。随着可解释AI需求的增长，这种基于Java的神经符号架构将在医疗、金融、法律等关键领域发挥越来越重要的作用。