公开(公告)号：US08447720B1

公开(公告)日：2013-05-21

申请号：US12755268

申请日：2010-04-06

Applicant: Stuart H. Rubin

Inventor： Stuart H. Rubin

IPC: G06F9/44 ,  G06N5/02

CPC classification number: G06N5/025

Abstract: A method includes receiving a user-specified context comprising one or more natural language contextual antecedents. Then, for each contextual antecedent, a modified contextual antecedent is created by converting each contextual antecedent to a sequence of integers using a word base. Each modified contextual antecedent is compared to each of a plurality of cases stored in a case base, where each case includes one or more case antecedents and one or more case consequents. The case antecedents and case consequents are stored in the case base as sequences of integers representing the respective case antecedents and case consequents. The case having the case antecedents that best match the contextual antecedents is then selected and the case consequents of the selected case are displayed to a user. The user then provides feedback regarding the displayed case consequents. The feedback may be integrated into the contextual antecedent for a new search of the case base. The method is computer-implementable and may be efficiently performed by a distributed processing system.

Abstract translation: 一种方法包括接收包括一个或多个自然语言上下文前提的用户指定的上下文。 然后，对于每个上下文前提，通过使用单词库将每个上下文前缀转换为整数序列来创建修改的上下文前提。 将每个修改的上下文前置与存储在病例库中的多个病例中的每一个进行比较，其中每个病例包括一个或多个病例前提和一个或多个病例结果。 案件前提和案件结果以存储在案件库中的代表各个案件前提和案件结果的整数序列。 然后选择具有最佳匹配上下文前提的案例前提的情况，并且向用户显示所选案例的结果。 然后，用户提供关于显示的案例结果的反馈。 反馈可以整合到上下文前提中，用于对案例库进行新的搜索。 该方法是可计算机实现的，并且可以由分布式处理系统有效地执行。

公开(公告)号：US07925605B1

公开(公告)日：2011-04-12

申请号：US11854638

申请日：2007-09-13

Applicant: Stuart H. Rubin

Inventor： Stuart H. Rubin

IPC: G06F17/00 ,  G06N5/02

CPC classification number: G06N5/025

Abstract: In various embodiments, evolutionary expert systems and methods are disclosed. For example, a method for evolving a rule base of an expert system includes creating a set of meta-rules from a set of first rules associated with the expert system, creating a set of one or more generalized virtual rule candidates based on the set of first rules and the set of meta-rules, filtering the set of generalized virtual rule candidates to remove generalized virtual rule candidates that conflict with at least one rule of the set of first rules to form a set of virtual rules, and incorporating at least one virtual rule of the set of virtual rules into the set of first rules to evolve the first set of rules.

Abstract translation: 在各种实施例中，公开了进化专家系统和方法。 例如，用于演进专家系统的规则库的方法包括从与专家系统相关联的一组第一规则创建一组元规则，基于该集合的一组创建一个或多个广义虚拟规则候选的集合 第一规则和一组元规则，过滤所述广义虚拟规则候选者集合以去除与所述第一规则集合中的至少一个规则冲突的一般化虚拟规则候选，以形成一组虚拟规则，并且将至少一个 将虚拟规则的虚拟规则集合到第一套规则集中来演化第一套规则。

公开(公告)号：US07047226B2

公开(公告)日：2006-05-16

申请号：US10206930

申请日：2002-07-24

Applicant: Stuart H. Rubin

Inventor： Stuart H. Rubin

IPC: G06F17/20 ,  G06F17/30

CPC classification number: G06N5/025

Abstract: A Knowledge Amplifier with Structured Expert Randomization (KASER) that exploits a structured expert randomization principle. One KASER embodiment allows the user to supply declarative knowledge in the form of a semantic tree using single inheritance. Another KASER embodiment includes means for automatically inducing this semantic tree, such as, for example, means for performing randomization and set operations on the property trees that are acquired by way of, for example, database query and user-interaction.

Abstract translation: 具有结构化专家随机化（KASER）的知识放大器，利用结构化专家随机化原理。 一个KASER实施例允许用户使用单个继承以语义树的形式提供声明性知识。 另一种KASER实施例包括用于自动引导该语义树的装置，例如用于对通过例如数据库查询和用户交互获得的属性树执行随机化和设置操作的装置。

公开(公告)号：US09396441B1

公开(公告)日：2016-07-19

申请号：US14041902

申请日：2013-09-30

Applicant: Stuart H. Rubin

Inventor： Stuart H. Rubin

IPC: G06F17/00 ,  G06N99/00

CPC classification number: G06N99/005 ,  G06N5/025

Abstract: The invention is a method and system updating the automated responses of an autonomous system using sensor data from heterogeneous sources. An array of cases representing known situations are stored as data structures in a non-transitory memory. Each case in the array of cases is associated with an action to create a database of identifiable situation-action pairs. The system determines an acceptable range of correctness of partial matches of sensed data for new cases to the data properties of known cases and creates and overwrites now situation-action pairs in a process of autonomous learning of new responses.

Abstract translation: 本发明是使用来自异质源的传感器数据来更新自主系统的自动化响应的方法和系统。 表示已知情况的一系列情况作为数据结构存储在非暂时性存储器中。 案例阵列中的每个案例都与一个动作相关联，以创建一个可识别的情况动作对的数据库。 该系统确定新情况的感测数据与已知情况的数据属性的部分匹配的可接受范围，并在自动学习新响应的过程中创建并覆盖现在的情况动作对。

公开(公告)号：US08655799B1

公开(公告)日：2014-02-18

申请号：US13077442

申请日：2011-03-31

Applicant: Stuart H. Rubin

Inventor： Stuart H. Rubin

IPC: G06F15/18 ,  G06G7/00 ,  G06F17/00 ,  G06N5/02 ,  G05B13/02

CPC classification number: G06N3/126

Abstract: Anticipatory logistics is used to predict observable events and respond to the predictions of the observable events in the control of automated equipment that perform highly repetitive functions such as elevator cars. A set of table entries is obtained, and the table entries are metricized and stored as cell entries. All cell entries are normalized. Ten weighted values to the cell entries are initialized. An algorithmically defined subset of weighted values is normalized and an instruction is selected based on the computed dependency using an algorithm incorporating uniform chance selection for exploratory optimization, such as the Mersenne Twister algorithm. Here, the search space is delimited by careful selection of the salient variables as well as by the algorithm itself, which only relies on chance to find truly novel solutions as time (and space) permit. The anticipatory logistics can be used to predict future events such as elevator car usage and thereby enhance efficiency in provision or utilization of resources.

Abstract translation: 预测物流用于预测可观察事件，并响应对执行高度重复性功能的自动化设备（如电梯轿厢）的可观察事件的预测。 获得一组表条目，并且表条目被度量并存储为单元条目。 所有单元格条目都被归一化。 对单元格条目的十个加权值进行初始化。 将加权值的算法上定义的子集归一化，并且使用包含用于探索性优化的均匀机会选择的算法（例如Mersenne Twister算法），基于所计算的依赖关系来选择指令。 这里，搜索空间通过仔细选择显着变量以及算法本身来界定，算法本身只依赖于随着时间（和空间）许可而找到真正新颖的解决方案的机会。 预期物流可用于预测未来事件，如电梯轿厢使用情况，从而提高资源的提供或利用效率。