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首页> 外文期刊>Journal of ambient intelligence and smart environments >Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
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Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction

机译：使用进化特征提取来推断基于ECA的环境智能规则

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One of the goals in Ambient Intelligence is to enable Intelligent Environments to take decisions based on the perceived context. In our previous work, we successfully explored how the inhabitants can communicate their own preferences with the environment using Event-Condition-Action (ECA) rules. The easiness of the communication language combined with an appropriate explanation mechanism gives trust to the Intelligent Environment actions. However, defining every preference, and maintaining them up-to-date can be cumbersome. Therefore, a complementary mechanism is required to learn from user behavior and adapt to small changes without being explicitly requested for. Inferring behaviors effectively from data collected from sensors in an Intelligent Environment is a challenging problem. The main issues include primitive representation of data, the necessity of a high number of sensors, and dealing with few training data collected in a short time. We present MFE3/GADfi, an evolutionary constructive induction method to ease inferring inhabitants' preferences from data collected from simple sensors. We show that this method detects successfully relevant sensors and constructs highly informative features that abstract relations among them. The constructed features, in addition to improving significantly the learning accuracy, break down and encapsulate the performance of inhabitants into decision trees that can easily be converted to ECA rules for further use in the Intelligent Environment. Comparing the empirical results show that our method can reduce a large set of complex ECA rules that represent the preferences to a smaller set of simple ECA rules.
机译：环境智能的目标之一是使智能环境能够根据感知的环境做出决策。在我们之前的工作中，我们成功地探索了居民如何使用事件条件行动（ECA）规则与环境交流自己的喜好。通信语言的简便性与适当的解释机制相结合，使人们对“智能环境”行动产生了信任。但是，定义每个首选项并保持最新状态可能很麻烦。因此，需要一种补充机制来从用户行为中学习并适应较小的更改，而无需明确要求。从智能环境中从传感器收集的数据中有效推断行为是一个具有挑战性的问题。主要问题包括数据的原始表示，大量传感器的必要性以及在短时间内处理少量训练数据。我们提出了MFE3 / GADfi，这是一种进化性的建设性诱导方法，可简化从简单传感器收集的数据中推断居民的偏好。我们表明，该方法可以成功检测到相关的传感器，并构建高度信息化的特征，以抽象它们之间的关系。所构建的功能除了可以显着提高学习准确性之外，还可以将居民的表现分解并将其封装到决策树中，这些决策树可以轻松转换为ECA规则，以在智能环境中进一步使用。比较实证结果表明，我们的方法可以减少大量复杂的ECA规则，这些规则代表了对较小的一组简单ECA规则的偏好。

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