Type-2 Fuzzy Logic based Systems for Adaptive Learning and Teaching within Intelligent E-Learning Environments

The recent years have witnessed an increased interest in e-learning platforms that incorporate adaptive learning and teaching systems that enable the creation of adaptive learning environments to suit individual student needs. The efficiency of these adaptive educational systems relies on the methodology used to accurately gather and examine information pertaining to the characteristics and needs of students and relies on the way that information is processed to form an adaptive learning context. The vast majority of existing adaptive educational systems do not learn from the users’ behaviours to create white-box models to handle the high level of uncertainty and that could be easily read and analysed by the lay user. The data generated from interactions, such as teacher–learner or learner–system interactions within asynchronous environments, provide great opportunities to realise more adaptive and intelligent e-learning platforms rather than propose prescribed pedagogy that depends on the idea of a few designers and experts. Another limitation of current adaptive educational systems is that most of the existing systems ignore gauging the students' engagements levels and mapping them to suitable delivery needs which match the students' knowledge and preferred learning styles. It is necessary to estimate the degree of students’ engagement with the course contents. Such feedback is highly important and useful for assessing the teaching quality and adjusting the teaching delivery in small and large-scale online learning platforms. Furthermore, most of the current adaptive educational systems are used within asynchronous e-learning contexts as self-paced e-learning products in which learners can study in their own time and at their own speed, totally ignorant of synchronous e-learning settings of teacher-led delivery of the learning material over a communication tool in real time. This thesis presents novel theoretical and practical architectures based on computationally lightweight T2FLSs for lifelong learning and adaptation of learners’ and teachers’ behaviours in small- and large-scale asynchronous and synchronous e-learning platforms. In small-scale asynchronous and synchronous e-learning platforms, the presented architecture augments an engagement estimate system using a noncontact, low-cost, and multiuser support 3D sensor Kinect (v2). This is able to capture reliable features including head pose direction and hybrid features of facial expression to enable convenient and robust estimation of engagement in small-scale online and onsite learning in an unconstrained and natural environment in which users are allowed to act freely and move without restrictions. We will present unique real-world experiments in large and small-scale e-learning platforms carried out by 1,916 users from King Abdul-Aziz and Essex universities in Saudi Arabia and the UK over the course of teaching Excel and PowerPoint in which the type 2 system is learnt and adapted to student and teacher behaviour. The type-2 fuzzy system will be subjected to extended and varied knowledge, engagement, needs, and a high level of uncertainty variation in e-learning environments outperforming the type 1 fuzzy system and non-adaptive version of the system by producing better performance in terms of improved learning, completion rates, and better user engagements

A fog-based hybrid intelligent system for energy saving in smart buildings

In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, to constantly improve its performance by learning users’ needs. The effectiveness of our approach is validated in the application scenario of a smart home by extensive experiments on real sensor traces. Experimental results show that our system achieves substantial energy savings in the management of a smart environment, whilst satisfying users’ needs and preferences

A novel dual surface type-2 fuzzy logic controller for a micro robot

Over the last few years there has been an increasing interest in the area of type-2 fuzzy logic sets and systems in academic and industrial circles. Within robotic research the majority of type-2 fuzzy logic investigations has been centred on large autonomous mobile robots, where resource availability (memory and computing power) is not an issue. These large robots usually have a variation of a Unix operating system on board. This allows the implementation of complex fuzzy logic systems to control the motors. Specifically the implementation of interval and geometric type-2 fuzzy logic controllers is of interest as they are shown to outperform type-1 fuzzy logic controllers in uncertain environments. However when it comes to using micro robots it is not practical to use type-1 and type-2 fuzzy logic controllers, due to the lack of memory and the processor time needed to calculate a control output value. The choice of motor controller is usually either fixed pre-set values, a variable scaled value or a PID controller to generate wheel velocities. In this research novel ways of implementing type-1 and interval type-2 fuzzy logic controllers on micro robots with limited resources are investigated. The solution thatis being proposed is the use of pre-calculated 3D surfaces generated by an off-line Fuzzy Logic System covering the expected ranges of the input and output variables. The surfaces are then loaded into the memory of the micro robots and can be accessed by the motor controller. The aim of the research is to test if there is an advantage of using type-2 fuzzy logic controllers implemented as surfaces over type-1 and PID controllers on a micro robot with limited resources. Control surfaces were generated for both type-1 and average interval type-2 fuzzy logic controllers. Each control surface was then accessed using bilinear interpolation to provide the crisp output value that was used to control the motor. Previously when this method has been used a single surface was employed to hold the information. This thesis presents the novel approach of the dual surface type-2 fuzzy logic controller on micro robots. The lower and upper values that are averaged for the classic interval type-2 controller are generated as surfaces and installed on the micro robots. The advantage is that nuances and features of both the lower and upper surfaces are available to be exploited, rather than being lost due to the averaging process. Having conducted the experiments it is concluded that the best approach to controlling micro robots is to use fuzzy logic controllers over the classical PID controllers where ever possible. When fuzzy controllers are used then type-2 fuzzy controllers (dual or single surface) should be used over type-1 fuzzy controllers when applied as surfaces on micro robots. When a type-2 fuzzy controller is used then the novel dual surface type-2 fuzzy logic controller should be used over the classic average surface. The novel dual surface controller offers a dynamic, weighted, adaptive and superior response over all the other fuzzy controllers examined

Dynamic Profile-Selection for zSlices based type-2 fuzzy agents controlling multi-user Ambient Intelligent Environments

Ambient Intelligence (AmI) is a vision that refers to an information technology paradigm where a physical environment is aware of its human occupants' presence/context and is sensitive, adaptive and responsive to their needs. Physical environments that are augmented with AmI are called Ambient Intelligent Environments (AIEs) which are deemed to be intelligent because the system should be able to recognise human occupants, reason with context and program itself to meet the occupants' needs by learning from their behaviour [1]. However, there is a need also to deal with real-world scenarios which involve multiple users occupying a given AIE. In order to handle multi-user AIEs and control them, there is a need to have agents that are able to learn the user(s) behaviours and handle the intra and inter-user uncertainties as people have different preferences and profiles which continuously change. In this paper, we present a zSlices based type-2 fuzzy agent which employs zSlices general type-2 fuzzy systems to learn the user(s) preferences and profiles and handle the encountered intra and inter-user uncertainties. The agent will behave according to a learned user profile that is unique to an individual user or a group of users and so the profile-selection problem manifests when the set of users in an AIE changes (i.e. when people enter/ leave an AIE). The proposed agent employs a novel strategy that we call Dynamic Profile-Selection that uses a cloud-based profile repository in order to support the agent activity in multiple AIEs. To demonstrate the proposed approach, we have conducted real-world experiments on two distinct AIEs which are the intelligent apartment (iSpace) and the intelligent Classroom (iClassroom) located at the University of Essex. © 2012 IEEE

Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6. - 7. Dezember 2012

Dieser Tagungsband enthält die Beiträge des 22. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) der vom 6. - 7. Dezember 2012 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für - Fuzzy-Systeme, - Künstliche Neuronale Netze, - Evolutionäre Algorithmen und - Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen