A Fuzzy Embedded Agent-Based Approach for Realizing Ambient Intelligence in Intelligent Inhabited Environments

TLDR

Ambient intelligence in intelligent inhabited environments (IIE) seeks to provide ubiquitous computing intelligence that supports user activities. The study proposes a novel life‑long learning, unsupervised fuzzy approach for intelligent agents embedded in IIE to extract user‑specific membership functions and rules. The method extracts fuzzy membership functions and rules from data, adapts learned behaviors online in a life‑long mode, and is evaluated through offline and online experiments against other approaches. Experiments over five days in a real iDorm test bed show the system learns and adapts to user behavior and outperforms competing methods while operating online in life‑long mode.

Abstract

We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An unsupervised, data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.