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Situation-aware model for multi-objective decision making in ambient intelligence

机译:环境智能多目标决策的现状感知模型

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The emergence of automated environments to add intelligence to the decision making process on real world problems with multiple and conflicting objectives is an actual issue. In some cases, these objectives have the same importance and no easy prioritization can be done. For dealing with reasoning on every-day activities, systems for ambient intelligence (AmI) often deal with multi-objective problems. This paper presents a situation-aware model to support multi-objective decision making for objectives with equal importance in AmI. By using contextual data from the environment, a system based on this model identifies the situation of interest; performs multi-objective decision without assigning weights to the objectives, and performs an action to control the environment. To verify the model, a system was developed aiming to manage the multi-objective problem of thermal comfort and energy consumption of an office. As results, this work developed an L-fuzzy library (used in the decision module) and shows that the inclusion of this intelligence in AmI systems allows the achievement of both objectives, without the need of giving priority to one or to the other aspect. (C) 2019 Elsevier B.V. All rights reserved.
机译:自动化环境的出现将智能增添了多种和冲突目标的现实世界问题的决策过程是一个实际问题。在某些情况下,这些目标具有相同的重要性,并且可以进行简单的优先级。为了处理每日活动的推理,环境情报(AMI)的系统通常会处理多目标问题。本文介绍了一个情况感知模型,以支持在AMI中具有同等重视的目标的多目标决策。通过使用来自环境的上下文数据,基于此模型的系统标识了感兴趣的情况;在不为目标分配权重的情况下执行多目标决定,并执行控制环境的操作。为了验证该模型,开发了一个系统,旨在管理办公室的热舒适和能量消耗的多目标问题。结果,这项工作开发了一个L-Fuzzy库(在决策模块中使用),并表明在AMI系统中包含这种智能允许实现两个目标,而无需优先考虑一个或另一个方面。 (c)2019年Elsevier B.V.保留所有权利。

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