Key Problems of Intent Recognition Research: A Survey on Activity, Plan and Goal Recognition

doi:10.16182/j.issn1004731x.joss.25-0157

Abstract

Abstract:

With the development of artificial intelligence technology, realizing intent recognition in human-computer interaction has become one of the key challenges. In this paper, the current research status of three fields was systematically sorted out, namely activity recognition, plan recognition, and goal recognition, and the progress from the problem proposal to the current development was analyzed. The main research approaches in each field were reviewed, and a survey of research on activity recognition, a development overview of plan recognition, and aretrospective analysis of hotspots in goal recognition were conducted. This general view of the problem helped to clarify and analyze the research landscape, aiming to provide inspiration for a unified approach to the problem of unifying activity, plan, and goal recognition in human-computer interaction. The paper on future research directions was provided, which pointed out that constructing a neuro-symbolic fusion reasoning framework, generating and refining robust prior knowledge, developing intent gaming theory in adversarial environments, and promoting technology iteration driven by cross-domain applications will be the focus of future research.

Key words: intent recognition, activity recognition, plan recognition, goal recognition, human-machine interaction

CLC Number:

TP391.9

Zhang Yi, Xu Kai, Li Shuilin, Chen Dejun, Zeng Yunxiu, Peng Yong. Key Problems of Intent Recognition Research: A Survey on Activity, Plan and Goal Recognition[J]. Journal of System Simulation, 2026, 38(3): 620-650.

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概率模型	非概率模型
贝叶斯网络	支持向量机
马尔可夫模型	决策树
条件随机场	K近邻
马尔可夫决策过程等	人工神经网络等

模型		方法	面临的挑战	解决的问题
逻辑推理		溯因推理	缺少真实数据	识别危险的活动
		线性时序溯因	复杂，计算成本高	识别视频监控识别
		因果贝叶斯网络	动态环境不适应	识别老年人的活动
		扩展贝叶斯溯因	统计关系模型推理效率低	一阶逻辑和概率图模型
机器学习	概率	分层动态贝叶斯网络	难适应用户行为的突然变化	用户日常活动的识别推断
		分层马尔可夫模型	状态层次增加，计算效率低	分层模型共享相同子结构
		无分层隐马尔科夫	特征选择耗时	机器人识别用户活动
		条件随机场	无法适应动态环境	识别人类活动
	非概率	决策树	位置和传感器数据的多样性	智能手机传感器识别活动
	非概率	支持向量机	复杂背景和动态变化识别差	空间-时间特征识别活动
深度学习	判别式	全连接神经网络	过拟合，计算复杂	识别活动
		卷积神经网络		自动识别人类活动
		循环神经网络		原始传感器数据识别
	生成式	深度信念网络	难以选择适合的分类特征	自动学习适合的特征
		深度信念网络	难以捕捉细微的表情变化	自动学习区分表情的特征
		深度玻耳兹曼机	资源受限设备上高资源消耗	智能手表上的活动识别
		深度玻耳兹曼机	不同传感器数据的差异性	多传感器数据融合
		自编码器	非线性目标的识别挑战	动态识别玩家动作
		自编码器	传感器噪声导致的低识别率	提高活动识别准确性

领域模型	观察序列	被识别者假设
经典规划领域	完全可观	辅助识别
随机领域模型	部分缺失	阻止识别
连续领域模型	混合噪声	锁孔识别
最优控制领域	完全错误	完全理性
近似不完整学习	混合未知	近似最佳

被识别者态度		描述	应用场景
意识	合作识别	被识别者主动提供信息，促进识别者识别	合作性被识别者
	对抗识别	被识别者故意隐藏信息，阻碍识别者识别	对抗性被识别者
	非合作非对抗识别	被识别者对识别不关心，不提供也不隐藏信息	一般被识别者
最优性	严格最优	被识别者行为完全理性，选择最优计划	确定性环境
	近似最优	被识别者行为近似理性，选择接近最优的计划	不确定性环境
	无最优性假设	不假设被识别者的最优性，不关心被识别者行为	非理性或部分理性被识别者
	有限理性	被识别者的规划资源有限，行为可能非最优但可解释	复杂或资源受限环境

用法	性能指标	定义
目的识别活动识别	准确率	正确识别的目的或活动的数量与总测试数量的比例
	精确率	预测为正确的目的或活动的实例中，实际正确的比例
	召回率	在所有实际的目的或活动中，算法正确识别出的比例
	F1分数	查准率和查全率的调和平均数
计划识别	运行时间	算法完成识别任务所需的时间
	产生的假设数量	算法认为最可能的计划数量
	算法复杂度	算法的时间复杂度与空间复杂度
目的识别设计	识别目的最大步骤数	用于衡量算法在最坏情况下能够区分不同目标的能力
计划识别设计	识别计划最大步骤数	用于衡量算法在最坏情况下能够区分不同计划的能力