CN113544707A - 用于连续检测、诊断和优化的深度因果学习 - Google Patents
用于连续检测、诊断和优化的深度因果学习 Download PDFInfo
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Abstract
本发明公开了一种用于多变量学习和优化的系统和方法,该系统和方法基于对过程决策的随机化多变量比较的一个或多个假设重复生成自组织实验单元(SOEU),该过程决策将被提供给系统的用户。将SOEU注入到系统中以生成关于过程决策的量化推断。响应于注入SOEU,识别量化推断内的至少一个置信区间,并且基于该至少一个置信区间迭代地修改SOEU以识别系统内的过程决策的至少一个因果交互。因果交互可用于系统性能的测试、诊断和优化。
Description
技术领域
本发明涉及多变量学习和优化过程,以识别并利用过程决策和结果之间的因果关系,并且更具体地,涉及同时执行这些操作。
背景技术
多变量学习旨在将随机化受控实验的基本构建块转换成全自动过程,该全自动过程合理地运用决策的定时、顺序和特定参数的自然可变性、自组织实验单元,自动生成关于其正在操作的系统的因果知识,并且同时运用该知识来持续优化预期效用。当应用于复杂的现实世界系统(具有快速变化的变量之间的关系的方向、量值和时空范围)时,多变量学习和优化为在多个领域中的不确定性下进行的决策提供了框架,包括电子商务、医学、商业、制造、能源网、电力系统、运输、数据网络、集群机器人和基础设施。
发明内容
本文公开了用于多变量学习和优化过程的系统、设备、软件和方法。
一种用于实施方案的多变量学习和优化的系统包括存储器和耦接到该存储器的处理器,其中,该处理器被配置为:接收对过程决策的随机化多变量比较的一个或多个假设,该过程决策将被提供给系统的用户;基于该一个或多个假设重复生成自组织实验单元(SOEU);将该SOEU注入系统中以生成关于过程决策的量化推断;响应于注入该SOEU,识别该量化推断内的至少一个置信区间;并且基于该至少一个置信区间迭代地修改SOEU以识别该系统内的过程决策的至少一个因果交互。
附图说明
在未必按比例绘制的附图中,类似的数字可描述不同视图中的类似部件。具有不同字母后缀的相似标号可表示类似部件的不同实例。在附图中,以举例的方式示出了一些方案,其中:
图1是示出根据各种示例的用于多变量学习和优化的系统的图示;
图2是根据各种示例的系统的软件模块和核心过程的框图;
图3是根据各种示例的用于多变量学习和优化的计算机实现的方法的流程图。
图4是用于优化组合状态和时间过程的计算机实现的方法的流程图;
图5是用于优化队列优先级和分配资源的计算机实现的方法的流程图;并且
图6是用于优化由交互产生的传感器网络、关系和结果的计算机实现的方法的流程图。
具体实施方式
对于以下定义术语的术语表,除非在权利要求书或说明书中的别处提供不同的定义,否则整个申请应以这些定义为准。
术语表。
术语“自变量(IV)”和“外部变量(EV)”通常分别用作由用户操纵的变量和不受用户控制的变量。自变量和外部变量可以是离散的或连续的。EV可以是已被选为在实验对照物之外(例如,控制搜索空间的维度)的“IV”。
术语因变量(DV)通常用于指表征系统对过程决策的响应的变量。它可以是效用的直接量度、复杂效用函数的一个输入,也可仅仅表示系统的状态,该系统可以与效用的某个度量相关联,也可以不与效用的某个度量相关联。DV还可以表示依次采取过程决策组时的中间目的。例如,DV可通常对应于提供对复杂系统的响应的可见性的传感器网络。
与实验单元一起使用的术语“水平”通常用作自变量(IV)的特征或选项的状态。例如,特性可以定义为具有两个水平,其中第一水平意味着该特征在实验单元中处于活动状态,而第二水平则被定义为其不处于活动状态。然后,可以定义附加的状态或情形,而不仅限于IV是活动的或不活动的。例如,可以通过离散化连续的IV来定义水平。
术语“重复地”通常用于在具有或不具有特定序列的情况下不断地出现。例如,过程可以恒定地或迭代地遵循一组按指定顺序的步骤,或者随机地或非顺序地遵循这些步骤。另外,可不按相同的频率执行全部步骤,例如,处理分配的执行频率可比更新因果学习的频率更高,并且后者的频率可随时间推移而改变,例如,当运用阶段成为主导时和/或当计算能力/速度要求随时间推移而改变时。
“能够交换的”或“可交换的”通常被部署为相对于过程决策分配的结果在统计学上是等同的。
术语“因果作用”或“因果效应/关系/交互/推断”通常用于指过程决策(例如IV水平)相对于不存在过程决策对过程的因变量和/或其总体结果/性能/效用的效果。
“正性”通常被定义为意指不小于零或具有非零的发生概率或选择概率。
术语“混杂”和“混杂因素”用于指与自变量和因变量均具有关联的变量,包括效用函数。通过随机选择过程决策,系统地消除它们。
术语“偏差”和“偏差因素”包括霍桑效应、顺序效应/残留效应、需求特征、外部变量和/或可修改自变量的水平的效果的任何其他因素。
除非另外指明,否则本说明书和实施方案中所使用的表达量或特性测量等的所有数字在所有情况下均应理解成由术语“约”来修饰。因此,除非有相反的说明,否则在上述说明书和所附实施方案列表中示出的数值参数可根据本领域的技术人员利用本公开的教导内容寻求获得的期望特性而变化。
示例性实施方案。
一般来讲,人类和许多机器学习的实现均在概率不确定性的条件下做出决策。在不引入有意或无意的偏差或无序的假设的情况下,通过被动观察识别数据集内的模式、推理或关系是具有挑战性的。数据集可以带来附加的挑战,因为其可以引入1)选择或采样偏差,2)混杂变量,以及3)缺乏方向性证据。受控实验旨在通过引入随机化、区组化和平衡方面来消除混杂和偏差,但仍然受到提供有形结果(即,确保高内部效度和外部效度)所需的大量先验知识以及由现实世界决策施加的刚性约束的阻碍。自适应实验以顺序方式执行一个或多个步骤,并且通常需要在可以解决后续步骤之前结束先前步骤。本文所述的技术通过将受控和自适应实验转换成通过自组织实验重复地分析数据和优化内部和外部效度的非顺序过程来克服被动观察和自适应实验。自组织过程合理地运用决策的定时、顺序和参数的自然可变性来自动定量并明确地推断具有精心控制的机会成本的因果关系,其中成本可包括操作风险。自然可变性意味着不确定性自然地导致决策制定的变化,例如制造过程中的正常操作范围。自组织的自适应学习系统和方法优于现有自适应实验技术的优点包括在条件或交互初始未知、不完整或作为假设估计并且随时间推移而被学习的情况下对贫困输入进行操作的能力。另一个优点是其对错误假设的稳健性,包括非平稳系统和过程中的时间的影响、应当实施或可以实施过程决策的最佳持续时间、应当分析或可以分析的最佳历史实验数量,以及外部因素(例如,消费者时尚或趋势、季节变化、自然灾难或人为灾难、健康状态等)的效果。对时空上不连续的因果关系(例如,局部效应和/或延迟效应)的迭代探究是优于现有技术的另一个优点,并且在理解和优化具有对于所关注的特定领域最具有效性的粒度水平的过程决策方面至关重要。
该系统和方法提供对因果作用的实时理解和现场理解以及量化,同时提供全自动操作控制和综合风险调整多目标优化。
自组织系统和方法的行为是稳健的、高效的、可扩展的,并且在复杂的现实世界系统包括那些受空间-时间关系的变化影响的系统上有效操作的,从而为认知自动化提供通用的规范分析平台。
一般用例包括连续测试和校准(例如,连续调整过程控制回路的校准参数以使与目标的偏差最小化)、连续诊断(例如,连续调整信号处理和分类参数以使假阳性/阴性发现最小化)和连续优化(例如,连续调整过程决策以使目标函数最大化)。
实施方案包括用于优化多个平台和/或子系统(例如,用于整体优化的集中式车辆级别过程控制单元和分布式子系统/过程控制单元)上的目标的系统和方法。系统输入可包括候选动作、决策、策略和系统约束(包括软约束和硬约束,诸如安全的/优选的过程窗口、物理限制或响应时间要求),以用于说明可以组合和实施过程决策的方式和原因。输入还可以包括关于例如商业目标、历史背景和先前发现/学习(从先前实施的深度因果学习和/或其他人工智能(AI)/机器学习技术,包括从观察数据/历史数据中学习)的初始假设,以及过程决策具体实施与其对过程结果的影响之间的时间差。根据一些实施方案的方法可指定用于跨空间、时间和其他属性对过程决策进行组合和定序的协议。根据一些实施方案的其他方法可识别所实现的过程决策和系统结果之间的因果关系,同时优化总体效用(例如,收入、利润、总体健康、效率、安全性、可靠性等)。即使当潜在的物理因果关系/机制是未知时,该系统也可被配置为由人类行为表示的任何目标函数/目的。深度因果学习能够同时监测多个因变量的因果效应,其中一些可以是整体目标函数的一部分,而另一些可以是局部或中间响应、约束或目标函数的一部分。因此,其结合了无模型强化学习(对最终目的的决策/动作的预期效用的估计)和基于模型的强化学习(独立于其效用/价值的原因与结果关系的估计)的有益效果。这允许系统立即且灵活地响应效用函数的变化,该效用函数作为时间(例如,尖峰与离峰需求)、空间(例如,不同的局部操作目标、约束或风险容限)或其他属性的函数。
因果作用被测量为与在一组自组织实验单元上或其内部存在相对于不存在过程决策或设置相关联的结果的统计学上的显著差异。计算结果的差值并将其存储为d-score,并且通过计算每个d-score分布的平均值周围的置信区间来完成统计显著性的评估,该置信区间定量过程决策或设置的因果效应的预期值及其周围的不确定性(并且表示推断精度的量度或程度)。在这种情况下,无偏差置信区间的计算是相对简单的,因为随机化消除了任何混杂因素,并且自组织实验单元允许对偏差因素和效应修饰因子进行自识别和管理。判读和自适应使用置信区间以自动理解和运用过程决策、定时和持续时间的特定影响,这可允许通过概率匹配进行透明且最佳的遗憾管理。尤其,一个或多个置信区间的计算允许风险调整的优化,因为其定量对应过程决策的最佳和最坏情况下的预期效用。这些因果学习、强化学习和深度学习特征的组合是这些系统和方法与当前解决方案的局限性之间的有利区别。根据本发明的方法和系统可识别并调整会混杂、偏差和/或掩蔽原因与结果知识并限制优化结果的错误输入(例如,错误假设),以及监测和动态地适应过程决策和操作结果之间的因果关系的变化(例如,由于设备故障、磨损、天气事件等)。
图1是示出根据各种示例的用于多变量学习和优化的系统100的图示。系统100包括存储器102耦接到存储器102的处理器104。处理器104可以从用户界面110接收包括对过程决策/设置的多变量比较的一个或多个假设106的输入。假设106也可从存储器102检索。输入还可以包括过程决策/设置元素,该过程决策/设置元素也可以存储在存储器102中或从该存储器访问。过程决策/设置将被提供给所关注的系统114并在其上进行优化,以使商业目标最大化。
处理器104和存储器102可以是包括用于输入假设106的用户界面110的用户系统116的一部分。例如,用户系统116可以是在装置上或在云环境中运行应用程序的移动装置(例如,智能电话、膝上型计算机或其他移动装置)或固定装置(即,台式计算机、服务器),该装置或云环境显示用户界面110并通过有线或无线网络连接到所关注的系统114。在另一实施方案中,处理器104和存储器102可以在所关注的用户系统118上操作。所关注的用户系统118将从在移动装置或固定装置上操作的用户界面110接收输入,该用户界面在装置上或云环境中运行应用程序(例如,在云上的容器或虚拟机上运行的API)。包括过程决策元素的假设106将被直接存储在所关注的用户系统118中并在其中被处理。用户系统116和所关注的用户系统118也可以同时操作,这意味着数据在它们之间可互换地存储和处理。
处理器104可以基于一个或多个假设106重复生成自组织实验单元(SOEU)112。SOEU112(其将在下文中相对于图3和相关联的表更详细地描述)用于定量过程决策之内和过程决策之间的推断。
至少一个SOEU112可以包括相应SOEU112将在系统(例如,所关注的系统114)中处于活动状态的持续时间。在一些实施方案中,处理器104可以生成多个SOEU112,其持续时间基于均匀分布、泊松分布、高斯分布、二项式分布或任何其他分布来随机选择。在其他实施方案中,SOEU的持续时间可以是最长的允许持续时间,并且同时记录所有中间持续时间。处理器104还可以动态地修改(即,增加或减少)SOEU112之间的潜在持续时间,直到SOEU112对后续SOEU112的残留效应减低或基本消除,假设该效应很大程度上是可逆的。处理器104可基于量化推断(例如,渐进地选择使测量效应的统计显著性最大化的持续时间)或响应于因果评估的肯定结果或否定结果(即,通过将运用决策的效用与基线决策进行比较来评估外部效度,其中基线决策可以是如相对于图2更详细地定义的探究决策)来增加或减少至少一个SOEU112的持续时间。
所关注的系统114可包括网站或移动应用程序。例如,所关注的系统114可以是优化企业对企业(B2B)目标的企业管理系统、生产线、车辆控制单元或任何其他复杂且动态的系统。每个SOEU112可代表系统的一个实例(例如,单条生产线上的时隙)或群体的一个元素(例如,连接的汽车车队中的一辆汽车、集群中的一台机器人)。处理器104可以基于这些实验组之间的过程决策效应的变化的量化推断将SOEU112分组为区组或集群。量化推断基于包含在单个SOEU中的过程决策的特征以及SOEU本身的特征,诸如一年中的时间、地理位置和其他外部变量。处理器104可为每个SOEU集群识别不同的因果交互,并且因此选择每个集群内的最佳过程决策,从而实现粒度“个性化”过程优化。在一些实施方案中,利用因果学习是最可迁移的学习形式之一的事实,过程决策对给定所关注的系统114或SOEU组112的因果效应的知识可迁移和/或推广到具有相似特征的新的所关注的系统或SOEU组。在其他实施方案中,可在不同的SOEU组上同时实现深度原因学习的多个实例。在这种情况下,由于需要解决可能存在于多个实例与其不同因果模型之间的任何潜在冲突,因而迁移学习变得复杂。这通过利用以下事实来实现:深度因果学习提供关于学习质量的透明度,具体地,通过置信区间学习的精度和通过基线监测学习的准确度,这反过来又提供了一套客观的度量标准,以协调和解决学习中的冲突,并实现跨SOEU的协作学习。
一旦生成,处理器104就可以将SOEU112持续地注入到所关注的系统114中,根据下文相对于图3所述的方法和标准迭代地修改SOEU112,并且识别所关注的系统114内的过程决策的至少一个因果交互。处理器104可以初始均匀的方式和迭代不太均匀的方式与由置信区间定量的相对预期效用的证据的量成比例地将决策分配给SOEU112。这样做时,确保了每个决策以最佳方式自动定向到改善目标函数(“运用决策”)或改善学习质量(“探究决策”)。
处理器104可以利用如下所述的限定的过程设置、决策和/或策略基于与至少一个假设106相关的包含的实验单元的均匀概率分布生成至少一组SOEU112。
假设106可以包括用于所关注的系统114的目标。目标可以包括系统优化的风险调整性能度量。示例包括但不限于:效率、安全性、可靠性、寿命和利润。
假设106可包括识别过程子系统或具体细节的过程控制元素。示例包括但不限于:设置、阈值、固有特征或它们的组合。
假设106可以包括对过程决策的时间约束或特定约束。时间约束可涉及在系统中决策将处于活动状态或不活动状态(例如,仅在一天或一年中的特定时间适当时)的时间和持续时间。对决策的约束可包括控制变量的存在或不存在、现有的最佳实践、安全要求或它们的组合。
当附加信息变得可用时,当系统分析和优化因果推断时,或者当约束和/或商业目标/操作目标随时间推移而改变时,可以初始定义假设106,然后手动或自动地反复更新该假设。
用户界面110是基于网络或基于应用程序,或基于云的门户网站,用户访问该门户网站以输入用于系统的假设106。用户界面110可以被呈现为监视器或智能电话显示器上的图形用户窗口。用户将通过用于访问系统的装置上的键盘或虚拟键盘输入假设106。
系统100的部件可以在通过基于本地、组或基于云的网络连接到系统的同时在固定装置(例如,台式计算机或服务器)和/或移动装置(例如,智能电话或膝上型计算机)上操作。系统100的一个或多个部件还可以在连接和方向已被所关注的系统114接收之后在固定装置和/或移动装置上操作。
图2是用于供处理器104执行的多变量学习和优化系统100的软件模块和自组织核心过程的框图。
软件模块和自组织过程包括:目标目的模块202;过程决策元素模块204;标准数据模块206;最大/最小时间可及范围数据模块208;和过程决策约束模块210。目标目的模块202、过程决策元素模块204、标准数据模块206、最大/最小时间可及范围数据模块208和过程决策约束模块210可以提供足够的结构以开始生成SOEU112(图1),而不需要穷举、具体细节和精度。
在一些情况下,还基于历史数据(无论是观察的还是干预的,如DOE)初始化置信区间,例如对于可能更不愿意从无到有的制造应用。在一些情况下,例如当响应时间非常短并且通信和/或计算能力有限、间歇性或非常宝贵时,实验分批次预先生成。
在方法实施之前、期间和之后的任何时间或在合理情况下,例如,当系统和方法在边界条件的最大值(如由约束限定)下操作且效应的影响尚未稳定时,人类监管者或人工智能(AI)代理211可调整过程控制/决策元素和过程约束。在一些实施方案中,处理器104可提供(例如,向显示装置)将由人类监管者或AI代理采取的潜在动作的指示,或者可基于预设规则采取此类动作(例如,将边界处的搜索空间增加10%)。对假设或目标的反馈或更新也可以手动地或自动地接受(即,由社交媒体站点接收的消费者评论或趋势)。
处理器104可以另外提示或使用户能够提供候选决策/设置的持续优先化列表或队列。一个示例包括何时基于最高重要性变量的证据(如通过统计分析、随机森林、贝叶斯网络或其他方式确定)使原始搜索空间的维度保持较小,然后随时间推移进行细化。当将此类队列提供给处理器104,则当引入新选项不会不利地影响最佳性时,处理器104可以合理地引入新选项。类似地,当处理器104检测到选项几乎没有或没有益处时,可以移除候选决策/设置,从而提示人类操作者查看这些移除的选项。
处理器104还可以针对改变过程控制的成本可能不为零的事实进行调整(例如,时间和人工)。这些成本可成为目标函数的一部分和由处理器104测量的目的,从而允许进行资源分配优化,其中添加、移除或更改过程控制的成本与感知的未来/潜在值平衡。
目标目的模块202接收、存储、显示和修改系统将优化的一个或多个性能度量。这些目的的范围可以从简单度量(例如,效率、寿命、可靠性、销售额、收入、毛利、销售成本(COGS)等)到多个度量的加权组合或任何其他函数转换(例如,考虑复杂的成本因素、供应链问题、库存可用性等),并表示实验期间监测的因变量。它们可表示总体目的(例如,总系统效率)或局部和/或中间目的(例如,子系统的效率或顺序阶段的效率)。如果被指定,则一些度量及其对应的用户分配的权重(即,重要性值)可被组合成多目标效用函数。用户分配的权重可以表示为数字或百分比。可以在商业目标改变(即,积极的市场渗透以使收入最大化、最大化功效与能源效率)的任何时间点修改或完善多目标效用函数。
控制元素模块204接收、存储、显示和修改用户提供的控制选项,包括可能的控制的组合搜索空间的完整阵列。控制元素是设置、动作、策略等的特定实例,其以技术语言或领域特定语言定义控制决策并且表示可用于实验的自变量(IV)。控制元素可以通过用户界面(即,图1的用户界面110)手动输入或更新,或者从另一程序应用程序或平台(例如,社交媒体网站、过程历史库、数据库等)自动粘贴、导入、复制或上传到系统中。例如,控制元素可包括历史库中的现有过程标签名称及其相关联的级别。在另一个示例中,控制元素可以包括之前从未实现的新元素,诸如由人类生成的创造性内容元素或生成性对抗网络。重要的是,例如当决策制定的不确定性范围和/或过程控制约束随时间改变时,或者又如,当操作者基于约束、最佳实践、现有知识或专家直觉选择所有可能性的子集时,可动态地更新(例如,添加、删除和/或修改)控制元素而不影响系统已经学习到的内容。控制元素模块204还接收、存储、显示和修改用户提供的外部变量(EV)以供自组织过程使用。外部变量可以直接从另一个应用程序(例如,历史库、天气服务、传感器数据等)导入,也可以首先是提取相关信息的过程(例如,使用自然语言处理、情绪分析、过滤、统计和机器学习分析等)。
标准数据模块206接收、存储、修改和表示过去的或历史的过程控制变量和性能度量(对应于限定的目标目的),描述在正常操作条件下的过程控制不确定性和在系统的具体实施之前的系统/过程性能。可选地,该数据可用于初始化系统,包括其初始决策变化或搜索空间,并且潜在地,还包括其初始学习,从而可以合并(来自先前的具体实施或来自其他技术)效用的先验知识。标准数据可以通过用户界面(即,图1的用户界面110)手动输入,或者从另一程序或平台(例如,来自制造过程历史库的正常操作范围)自动导入、复制或上传到系统中。
最大/最小时间和空间可及范围数据模块208接收、存储、修改和表示模块204的因果效应在整个系统中扩散和衰减的最大和最小程度的初始估计。在这种情况下,扩散是指由于可被检测到的特定控制元素而导致的结果和因变量的变化所需的时间量,并且因此对应于实验单元被激活的最小时间量。在这种情况下,衰减是指特定控制元素基本上清理了系统的结果的时间量(即,在假设效应是可逆的情况下,不可被检测到),并且因此对应于在另一个实验单元被激活之前,实验单元被去激活的最小时间量。在许多情况下,效应可能不是完全可逆的(例如,电池在每次充电/放电实验中自然衰减,或营销活动可能影响未来竞争者的行为/响应),并且相应的推断出的因果效应可能随时间推移而变得不稳定,在这种情况下,数据包含窗口(下文进一步描述)将相应地调整。也可以存在系统定义或用户定义的实验频率和持续时间(无论是时间还是时间百分比),以用于指定实验处于活动状态和不活动状态的时间。该模块用于定义实验单元的空间和时间特征的初始搜索空间。
过程决策约束模块210涉及用户或系统提供的一组过程控制规则,这些规则限制控制元素的总体组合搜索空间。过程控制约束模块210接收、存储、修改和表示用户或系统定义的约束。约束条件包括用户定义的或系统指定的规则、规定、最佳实践和模型,这些规则、规定、最佳实践和模型定义了允许的过程控制元素的边界(或限制)。约束可以是“软”的,并且作为罚分函数结合在总体品质因素(例如,能量效率、寿命等)中,也可以是“硬”的,这意味着系统将会坚守(即,永不违反)而不会偏离或考虑其他证据。约束包括但不限于:可以具体实施过程控制元素的时间和位置(例如,总体系统约束与局部子系统约束);对多重性和同现性的约束(例如,当过程控制元素无法重复或一起使用时);以及具体实施平台所规定的约束(例如,自动化的水平和/或具体实施的成本)。可以在具体实施期间更新约束,因为可以对推断进行定量,例如,以探究在边界处或边界附近对效用的影响。过程控制元素和约束是代理通过约束或扩展系统中可用于实验的选项范围来管理风险与回报的机会。
核心算法方法和过程212使用目标目的模块202、过程决策元素模块204、标准数据模块206、最大/最小时间可及范围数据模块208和过程决策约束模块210来生成定义真实世界过程决策/设置的过程控制标准协议214,以在任何给定时间点应用。过程212和内容特定协议214可以各自访问销售点(即,每个位置的销售)(POS)数据228。核心算法方法和过程212包括以下内容:生成实验单元过程216;处理分配过程218;探究/运用管理过程220;基线监测过程222;数据包含窗口管理过程224;以及实验单元过程226的聚类。
实验单元过程216的生成基于从核心模块202、204、206、208和210接收的输入来识别可交换的时空单元。理想的实验单元的特征在于防止残留效应使所产生的因果知识降级/偏差的最小空间/时间范围。实验单元的生成和执行、自变量和因变量的选择和使用以及空间/时间条件的分配的示例在例如美国专利号9947018(Brooks等人)和美国专利申请公布号2016/0350796(Arsenault等人)中有所描述。
处理分配过程218以遵循统一或预定义的概率分布(例如,与历史或正常操作相关的分布)的分配频率向一个或多个实验单元提供过程控制元素的受控随机分配(诸如,双盲分配、无替换的随机化、反向平衡和区组化),直到检测和运用效用差异。此时,控制元素被分配成使得分配的相对频率匹配由探究/运用管理过程指定的相对频率。处理分配过程独立地管理可交换的实验单元的集群。聚类涉及隔离已被识别为效应修饰因子的每个外部因素的分配、外部因素的组合(例如,如通过主成分分析确定)或外部因素状态/值的组合(例如,如通过条件推断树或其他非监督分类方法确定)。在每个集群内,例如通过将控制元素的存在和不存在分配给如通过倾向性匹配确定的一对类似实验单元,区组化可用于进一步减少由于外部因素引起的可变性。聚类和区组化两者均有助于消除潜在效应修饰因子的可变性和偏差,并且两者均为“自组织”过程的组成部分。
过程控制分配在实验单元内的残留效应由过程216和过程218可操作地且自适应地管理。残留效应意味着一种过程控制分配的效应污染了下一分配的测量效应。为了消除残留效应,分配的持续时间必须符合效应的最大/最小时间可及范围。例如,如果min=0h且max=4h,则可以生成持续时间为4h、频率为1/8的实验单元(例如,在8h时间段内使用最后4h)。在另一个示例中,如果min=4h且max=4h,则可以生成持续时间为1h、频率为1/5的实验单元(例如,在5h时间段内使用最后一个小时)。最佳实验单元持续时间和频率也可以取决于效应是持久的(即,在实验持续时间内随时间推移是稳定的)还是瞬时的(即,在实验持续时间内随时间推移而变化)。
探究/运用管理过程220分析置信区间并使用概率匹配、合理选择理论或其他技术来调整分配频率。对于每个新的分配,系统需要决定是否将实验分配用于进行最大概率的最佳决策(即,使回报最大化)或用于改善概率估计的精度(即,使遗憾最小化)。在一些情况下,过程220可基于置信区间之间的重叠来调节分配频率,其中,置信区间之间的较小重叠导致更频繁地使用与最高效用相关联的水平。系统还可改变运用分配的积极性,并将其置于实验对照物下,以找到积极性参数,该积极性参数相对于通过基线监测确定的探究分配,使效用最大化。监测运用分配和探究分配之间的效用差距提供了对系统的超参数的最佳性的客观量度。当执行过程决策的成本(包括风险和机会成本)在自变量水平上不一致时,计算Bonferroni校正的置信区间,使得需要更多证据来运用风险更大或成本更高的控制元素。
基线监测过程222通过周期性随机分配连续地实时分析一个或多个基线,以提供效用改善的无偏差量度。基线可根据所需定量的度量而被定义为不同的基线,例如,在推断系统的超参数的效用时,基线可被定义为纯探究,或者在推断探究分配/运用分配的效用时,基线可被定义为现有决策制定过程。除了如上所述分配的实验单元之外,系统还通过统计功效分析连续确定准确监测这些基线分配和探索分配/运用分配之间,或探索和运用之间的性能差异所需的基线实验单元的数量。根据标准操作范围数据对基线实验单元进行随机采样。基线监测过程提供了对内部超参数的效用的无偏差量度,诸如聚类粒度、数据包含窗口以及探究/开发积极性,从而允许客观且动态地调谐此类参数。基线试验还确保探究由约束限定的整个搜索空间,从而提供针对非凸起空间中的优化问题的解决方案。
数据包含窗口(DIW)管理过程224分析时间对自变量与效用函数之间的交互的强度和方向的稳定性的影响,从而分析数据在多大程度上代表了系统的当前状态以提供实时决策支持。对于每个自变量,其确定了帕累托最佳数据包含窗口,该窗口提供了精度(使实验功效最大化并缩小置信区间)和准确度(使因果效应的统计显着性最大化并保持高外部效度)之间的权衡。识别帕累托最佳数据包含窗口的示例性方式包括逐步方差分析(ANOVA)、正态性检验和其他统计模型。在该帕累托最佳数据包含窗口上计算置信区间,以防止过程过拟合数据,并使其对潜在的因果机制的动态变化保持高度响应。DIW可以是用户最初基于输入的约束或关于系统的先验知识所定义的。一般来讲,系统是在假定不稳定(即,其并非是100%平稳的)的情况下操作,并且使用基线监测来进一步优化精度和准确度之间的平衡。
实验单元过程226的聚类有条件地优化SOEU注入和过程控制分配,以消除效应修饰因子(实验对照物之外的外部因素)的影响,并提供每个集群内的因果交互的无偏差证据。聚类涉及隔离充当为效应修饰因子的每个单个外部因素的分配、外部因素的组合(例如,如通过主成分分析确定)或外部因素状态/值的组合(例如,如通过条件推断树或其他非监督分类方法确定)。此外,在一些实施方案中,在聚类之前,可首先将外部因素转换(例如,通过趋势和季节性分析以及其他技术)成不同的因素组。聚类还用于通过学习如何基于自变量水平的效应和不能由系统操纵的实验单元的属性(例如,季节或天气效应等)之间的因素交互有条件地分配自变量水平来管理系统中的维度。系统的维度以及因此学习的粒度(即,集群的数量)总是与可用数据的量相当,使得添加更多外部因素不一定增加维度,因为在证据支持聚类的需要之前,它们会被忽略。因此,对可以或应该考虑多少外部因素没有限制,并且一般来讲,对实验单元的特征了解得越多,过程在加强可交换性和消除效应修饰因子的偏差方面越有效。首先识别并聚类具有较大效应的外部因素,同时通过区组化(例如,通过倾向性匹配)来管理其他外部因素,直到累积了足够的数据以进一步聚类。初始假设包括应基于先验知识或事实证明它们确实重要的证据考虑哪些外部变量和实验单元的特征。聚类是通过将实验单元集中成集群来实现的,这些集群具有自变量对效用的影响的最大集群内相似性(即更大的可交换性),并且具有最大集群间的差异,如使用因素方差分析、独立性测试、条件推断树或其他非监督分类技术所识别的。如在探究/运用管理过程和数据包含窗口管理过程的情况下,可以动态地优化集群的数量,以通过将其置于实验对照物下并通过基线监测连续地测试其对效用的影响来使精度和精度两者最大化。在一些情况下,精度和准确度均可受益于聚类,由此聚类减小了估计中的方差和采样偏差。在一些其他情况下,由于效用的证据随时间推移而变化,因此精度和准确度均可通过重组集群来改善,这与人类在其大脑中动态地形成记忆的方式没什么不同。
表1示出了一旦实现系统,核心算法方法和过程212(图2)中的每一者可以按阶段操作的方式。阶段被定义为发起、探究/运用、集群发起和连续集群优化。一旦输入并定义假设106(图1),发起阶段就会发生。假设106可包括或包含过程202、204、206、208和210。该系统开始分析包含在目标目的、过程决策元素、标准数据、最大/最小时间可及范围数据和过程决策约束模块(图2-202、204、206、208和210)中的数据,以定义变量和实验单元宽度。探究/运用阶段开始调整实验单元持续时间、数据包含窗口和分配频率,并通过基线监测评估这些调整的效用。集群发起阶段开始运用跨外部因素的测量效应的方差来改善实验单元的可交换性和学习的粒度。连续集群优化阶段动态地管理实验单元的自组织,并通过基线监测评估其效用。虽然阶段是按顺序描述的,但应当理解,它们可能并不总是以该特定顺序执行,并且两个或更多个阶段可同时发起或执行。
表1:核心算法方法和按阶段的过程实现
因果知识模块230系统地执行核心算法方法和过程212以计算过程控制分配的因果效应周围的置信区间,表示这些效应对多目标优化函数的预期效用和这些估计周围的不确定性。在因果知识模块230中根据d-score分布计算置信区间,该d-score分布是针对每个可交换实验单元的集群内的自变量水平和每个因变量中的每个单独一者或组合计算的。深度因果学习不仅捕获单个效应,而且捕获IV之间的交互效应,并且不假设效应是简单相加的。虽然需要用于多目标优化的一种度量,但计算目标函数中的每个因变量的置信区间可以用于提高学习的透明度和可解释性,并通过提供独立于奖励/效用的因果模型,允许目标函数被动态改变。这样做时,深度因果学习结合了无模型强化学习和基于模型的强化学习的有益效果。
置信区间是通过变量在数据包含窗口上被激活和被去激活(即,d-score)时对测量效应之间的差异进行统计测试来计算的。每个自变量和每个集群可存在特定数据包含窗口(即,它们可以全部相同或不同)。持续评估学习与运用的增加值(即,通过缩小置信区间来概率性地捕获多少值),包括添加、编辑、插值或移除自变量的潜在影响。
连续优化模块232通过进一步完善所推荐的控制元素和系统的超参数的有效性来唤起用于识别、监测和改进实验单元过程226的聚类并且探究/运用管理过程220的过程。
图3是根据各种示例的用于过程决策生成和优化的计算机实现的方法300的流程图。方法300可以在软件或固件中实现,以由处理器诸如处理器104执行。方法300包括以下步骤:接收对内容的随机化多变量比较的一个或多个假设,该内容将被提供给系统的用户(302),其中,该用户可以是操作于某一过程中的代理,该代理是人工介导的或自动化的或两者;基于该一个或多个假设重复生成SOEU(304);将SOEU注入到系统中以生成关于内容的量化推断(306);响应于注入SOEU,识别量化推断内的至少一个置信区间(308);并且基于该至少一个置信区间迭代地修改SOEU以识别系统内的内容的至少一个因果交互(310);
图4是用于在不确定性下优化顺序和时间过程控制的组合状态的计算机实现的方法的流程图。
图5是用于在不确定性下优化队列优先级和资源分配的计算机实现的方法的流程图。
图6是用于在不确定性下优化传感器网络拓扑(topography)、校准和/或判读的计算机实现的方法的流程图。
该系统和方法可包括对错误假设的稳健性以及来自随时间推移得到校正的关联强度(即,相关性)的因果知识的初始化。
该系统和方法可包括“无模型”操作,例如,无需知道潜在的因果图或过程机制(例如,某些化学反应如电化学氟化作用)。该系统和方法还可包括“基于模型的”操作,例如,当因果学习提供系统中类似于“数字孪生”的物理连接和因果机制的数学表示时(不同于存在于一切事物和任何事物之间的相关性,包括不存在物理连接和/或不存在因果关系的情况)。
该系统和方法还预期并响应误差(估计)。
Claims (13)
1.一种用于多变量学习和优化的系统,包括:
存储器;和
处理器,所述处理器耦接到所述存储器,所述处理器被配置为:
接收对过程决策的随机化多变量比较的一个或多个假设,所述过程决策将被提供给系统的用户;
基于所述一个或多个假设重复生成自组织实验单元(SOEU);
将所述SOEU注入所述系统中以生成关于所述过程决策的量化推断;
响应于注入所述SOEU,识别所述量化推断内的至少一个置信区间;以及
基于所述至少一个置信区间迭代地修改所述SOEU以识别所述系统内的所述过程决策的至少一个因果交互。
2.根据权利要求1所述的系统,其中所述因果交互用于测试和改善顺序过程控制和时间过程控制的组合状态。
3.根据权利要求1所述的系统,其中所述因果交互用于使系统需求成队列并分配响应。
4.根据权利要求1所述的系统,其中所述因果交互用于测试并改善传感器网络拓扑、校准和判读。
5.根据权利要求1所述的系统,其中所述SOEU持续时间和其他特征随时间推移而受到扰动,以使时间和空间交互最小化并使SOEU的独立性最大化。
6.根据权利要求1所述的系统,其中SOEU特征用于优化集群分配并改善每个集群内的SOEU的可交换性。
7.根据权利要求1所述的系统,其中用于生成量化推断(DIW)的数据的量与所述因果交互在时间上的稳定性成比例。
8.根据权利要求7所述的系统,其中所述DIW比可用数据的总量少10%、20%、40%或80%。
9.根据权利要求1所述的系统,其中所述系统的目标函数随空间和/或时间推移而变化。
10.根据权利要求1所述的系统,其中随机分配用于所述系统的基线监测。
11.根据权利要求10所述的系统,其中所述基线监测用于客观地调谐或优化所述系统的超参数。
12.根据权利要求1所述的系统,其中所述置信区间提供对物理的原因与结果关系的无偏差估计。
13.一种用于所关注的系统管理的计算机实现的方法,包括以下步骤:
将随机化受控信号注入所述所关注的系统中;
确保所述信号注入发生在正常操作范围和约束内;
响应于所述受控信号,监测所述所关注的系统的至少一项性能测量;
计算关于存在不同信号注入相对于不存在不同信号注入对所述所关注的系统的所述性能测量的因果效应的置信区间;以及
基于所计算的置信区间为所述所关注的系统的性能选择最佳信号。
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