), 葛列众 浙江大学 心理科学研究中心, 杭州 310058
收稿日期:2020-03-27出版日期:2020-09-15发布日期:2020-07-24通讯作者:许为E-mail:xuwei11@zju.edu.cnEngineering psychology in the era of artificial intelligence
XU Wei(), GE Liezhong Center for Psychological Sciences, Zhejiang University, Hangzhou 310058, China
Received:2020-03-27Online:2020-09-15Published:2020-07-24Contact:XU Wei E-mail:xuwei11@zju.edu.cn摘要/Abstract
摘要： 智能技术为智能时代的工程心理学研究和应用提供了新的机遇。为此, 系统提出了智能时代工程心理学的工作框架。该工作框架包括工程心理学研究和应用的对象、核心问题空间、学科理念、研究重点、应用范围、方法等。智能时代的人机关系呈现出一种新的形式: 人机组队式的人机合作关系。“以人为中心的人工智能”应该是智能时代工程心理学的学科理念。针对智能技术, 近期工程心理学研究者开始开展围绕新型人机关系的理论框架和基本问题、人机组队中的心理结构和决策控制、人机交互等方面的研究工作。为有效支持智能系统的研发, 概括总结了一些工程心理学新方法和提升的方法。最后, 针对当前工程心理学所面对的一些挑战提出了具体的建议。
图/表 7
表1AI的三次浪潮和发展的阶段特征(来源: 许为, 2019b)
| 第一次浪潮(上世纪50~70年代) | 第二次浪潮(上世纪80~90年代) | 第三次浪潮(2006年~ ) | |
|---|---|---|---|
| 主要技术和方法 | 早期“符号主义和联结主义”学派, 产生式系统, 知识推理, 专家系统 | 统计模型在语音识别、机器翻译的研究, 神经网络的初步应用, 专家系统 | 深度学习技术在语音识别、数据挖掘、自然语言处理、模式识别等方面的突破, 大数据, 计算力等 |
| 用户需求 | 无法满足 | 无法满足 | 开始提供有用的、解决实际问题的AI应用解决方案 |
| 工作重点 | 技术探索 | 技术提升 | 技术提升, 应用落地场景, 伦理化设计, 前端应用, 人机交互技术等 |
| 阶段特征 | 学术主导 | 学术主导 | 技术提升+应用+以人为中心 |
表1AI的三次浪潮和发展的阶段特征(来源: 许为, 2019b)
| 第一次浪潮(上世纪50~70年代) | 第二次浪潮(上世纪80~90年代) | 第三次浪潮(2006年~ ) | |
|---|---|---|---|
| 主要技术和方法 | 早期“符号主义和联结主义”学派, 产生式系统, 知识推理, 专家系统 | 统计模型在语音识别、机器翻译的研究, 神经网络的初步应用, 专家系统 | 深度学习技术在语音识别、数据挖掘、自然语言处理、模式识别等方面的突破, 大数据, 计算力等 |
| 用户需求 | 无法满足 | 无法满足 | 开始提供有用的、解决实际问题的AI应用解决方案 |
| 工作重点 | 技术探索 | 技术提升 | 技术提升, 应用落地场景, 伦理化设计, 前端应用, 人机交互技术等 |
| 阶段特征 | 学术主导 | 学术主导 | 技术提升+应用+以人为中心 |
表2自动化与自主化之间工程心理学特征的比较 (修改自: 许为, 2020)
| 工程心理学特征 | 自动化 | 半自主化(针对特定场景、任务) | 全自主化 |
|---|---|---|---|
| 实例: 一般办公软件, 自动化生产线, 自动化飞机驾驶舱 | 实例: 智能音箱, 智能决策系统, 自动驾驶车(L2及以上) | 实例: 科幻电影《终结者》中的Skynet机器人 | |
| 感应环境的能力 | 比较有限 | 先进的多模态感应 | 更先进的多模态感应 |
| 认知能力(知觉整合、模式识别、学习、推理、决策等) | 没有 | 有部分 | 有 (包括自主设定目标、调整策略、资源分配等) |
| 执行操作的能力 | 人工激活操作, 根据预定不变的规则执行操作 | 人工激活操作, 独立执行操作 | 自主激活操作、独立执行操作等 |
| 对不可预测环境的自适应能力 | 没有 | 有部分 | 有 |
| 系统操作结果 | 具确定性 | 具不确定性 | 具不确定性 |
| 系统运行中对人工操作的需求 | 需要(特别是设计无法预料的操作场景, 非正常、应急状态) | 需要(设计无法预料的操作场景,非正常、应急状态) | 一般不需要(人应是系统最终决策者) |
表2自动化与自主化之间工程心理学特征的比较 (修改自: 许为, 2020)
| 工程心理学特征 | 自动化 | 半自主化(针对特定场景、任务) | 全自主化 |
|---|---|---|---|
| 实例: 一般办公软件, 自动化生产线, 自动化飞机驾驶舱 | 实例: 智能音箱, 智能决策系统, 自动驾驶车(L2及以上) | 实例: 科幻电影《终结者》中的Skynet机器人 | |
| 感应环境的能力 | 比较有限 | 先进的多模态感应 | 更先进的多模态感应 |
| 认知能力(知觉整合、模式识别、学习、推理、决策等) | 没有 | 有部分 | 有 (包括自主设定目标、调整策略、资源分配等) |
| 执行操作的能力 | 人工激活操作, 根据预定不变的规则执行操作 | 人工激活操作, 独立执行操作 | 自主激活操作、独立执行操作等 |
| 对不可预测环境的自适应能力 | 没有 | 有部分 | 有 |
| 系统操作结果 | 具确定性 | 具不确定性 | 具不确定性 |
| 系统运行中对人工操作的需求 | 需要(特别是设计无法预料的操作场景, 非正常、应急状态) | 需要(设计无法预料的操作场景,非正常、应急状态) | 一般不需要(人应是系统最终决策者) |
图1人机关系跨时代的演变
图1人机关系跨时代的演变表3人机交互与人机组队之间工程心理学特征的比较
| 工程心理学特征 | 人机交互 | 人机组队 |
|---|---|---|
| 主动性 | 只有人主动地启动任务、行动, 机器被动接受 | 人机双方均可主动地启动任务和行动 |
| 方向性 | 只有人对机器的单向信任、情景意识、决策 | 人机双向的信任、情景意识、意图, 分享的决策控制权(人应拥有最终控制权) |
| 互补性 | 人与机之间无智能互补 | 机器智能(模式识别、推理等能力)与人的生物智能(人的信息加工等能力)之间的互补, 优化智能系统设计 |
| 预测性 | 只有人类操作员拥有这些特征 | 人机双方借助行为、情景意识等模型, 预测对方行为、环境和系统的状态 |
| 自适应性 | 只有人类操作员拥有这些特征 | 人机双向适应对方以及操作场景的行为 |
| 目标性 | 只有人类操作员拥有这些特征 | 人机双向均可设置或调整目标 |
| 替换性 | 机器借助于自动化等技术主要替换人的体力任务 | 机器可以替换人的认知、体力任务(人机双向可主动或被动地接管、委派任务) |
| 合作性 | 有限的人机合作 | 更大范围的人机合作 |
表3人机交互与人机组队之间工程心理学特征的比较
| 工程心理学特征 | 人机交互 | 人机组队 |
|---|---|---|
| 主动性 | 只有人主动地启动任务、行动, 机器被动接受 | 人机双方均可主动地启动任务和行动 |
| 方向性 | 只有人对机器的单向信任、情景意识、决策 | 人机双向的信任、情景意识、意图, 分享的决策控制权(人应拥有最终控制权) |
| 互补性 | 人与机之间无智能互补 | 机器智能(模式识别、推理等能力)与人的生物智能(人的信息加工等能力)之间的互补, 优化智能系统设计 |
| 预测性 | 只有人类操作员拥有这些特征 | 人机双方借助行为、情景意识等模型, 预测对方行为、环境和系统的状态 |
| 自适应性 | 只有人类操作员拥有这些特征 | 人机双向适应对方以及操作场景的行为 |
| 目标性 | 只有人类操作员拥有这些特征 | 人机双向均可设置或调整目标 |
| 替换性 | 机器借助于自动化等技术主要替换人的体力任务 | 机器可以替换人的认知、体力任务(人机双向可主动或被动地接管、委派任务) |
| 合作性 | 有限的人机合作 | 更大范围的人机合作 |
图2智能系统工程心理学核心问题的概念空间(修改自: Xu, 2021; 许为, 2020)
图2智能系统工程心理学核心问题的概念空间(修改自: Xu, 2021; 许为, 2020)
图3“以人为中心的AI”概念模型(来源: 许为, 2019b)
图3“以人为中心的AI”概念模型(来源: 许为, 2019b)表4工程心理学新方法或提升的方法与传统方法的比较
 |
表4工程心理学新方法或提升的方法与传统方法的比较
| |
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