Life Long Learning Approach for Type-2 Fuzzy Embedded Agents in Ambient Intelligent Environments

Abstract

In this paper, we will present a novel system for learning and incrementally adapting type-2 Fuzzy Logic Controllers (FLCs) for agents embedded in Ambient Intelligent Environments (AIEs). The system learns the rules and the type-2 Membership Functions (MFs) for the type-2 FLC that models the user behavior. Over long term operations, the agent incrementally adapts the type-2 FLC rules and MFs in a life long learning mode to accommodate for the short term and long term uncertainties encountered in AIEs. We will present unique experiments carried out by different users over the course of the year in the Essex intelligent Dormitory (iDorm) which is a real AIE test bed. We will show how the type-2 agent learnt and adapted to the occupant's behavior, whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 fuzzy agents while using smaller rule bases.