Hani Hagras

A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots

Autonomous mobile robots navigating in changing and dynamic unstructured environments like the outdoor environments need to cope with large amounts of uncertainties that are inherent of natural environments. The traditional type-1 fuzzy logic controller (FLC) using precise type-1 fuzzy sets cannot fully handle such uncertainties. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce a better performance. In this paper, we present a novel reactive control architecture for autonomous mobile robots that is based on type-2 FLC to implement the basic navigation behaviors and the coordination between these behaviors to produce a type-2 hierarchical FLC. In our experiments, we implemented this type-2 architecture in different types of mobile robots navigating in indoor and outdoor unstructured and challenging environments. The type-2-based control system dealt with the uncertainties facing mobile robots in unstructured environments and resulted in a very good performance that outperformed the type-1-based control system while achieving a significant rule reduction compared to the type-1 system.

Creating an ambient-intelligence environment using embedded agents

The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.

A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments

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 users particularized behaviors in the environment. The users 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 users 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.

Toward General Type-2 Fuzzy Logic Systems Based on zSlices

Higher order fuzzy logic systems (FLSs), such as interval type-2 FLSs, have been shown to be very well suited to deal with the high levels of uncertainties present in the majority of real-world applications. General type-2 FLSs are expected to further extend this capability. However, the immense computational complexities associated with general type-2 FLSs have, until recently, prevented their application to real-world control problems. This paper aims to address this problem by the introduction of a complete representation framework, which is referred to as zSlices-based general type-2 fuzzy systems. The proposed approach will lead to a significant reduction in both the complexity and the computational requirements for general type-2 FLSs, while it offers the capability to represent complex general type-2 fuzzy sets. As a proof-of-concept application, we have implemented a zSlices-based general type-2 FLS for a two-wheeled mobile robot, which operates in a real-world outdoor environment. We have evaluated the computational performance of the zSlices-based general type-2 FLS, which is suitable for multiprocessor execution. Finally, we have compared the performance of the zSlices-based general type-2 FLS against type-1 and interval type-2 FLSs, and a series of results is presented which is related to the different levels of uncertainty handled by the different types of FLSs.

A Type-2 Fuzzy Ontology and Its Application to Personal Diabetic-Diet Recommendation

It has been widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like personal diabetic-diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. This paper proposes a novel ontology model, which is based on interval type-2 fuzzy sets (T2FSs), called type-2 fuzzy ontology (T2FO), with applications to knowledge representation in the field of personal diabetic-diet recommendation. The T2FO is composed of 1) a type-2 fuzzy personal profile ontology ( type-2 FPPO); 2) a type-2 fuzzy food ontology ( type-2 FFO); and 3) a type-2 fuzzy-personal food ontology (type-2 FPFO). In addition, the paper also presents a T2FS-based intelligent diet-recommendation agent ( IDRA), including 1) T2FS construction; 2) a T2FS-based personal ontology filter; 3) a T2FS-based fuzzy inference mechanism; 4) a T2FS-based diet-planning mechanism; 5) a T2FS-based menu-recommendation mechanism; and 6) a T2FS-based semantic-description mechanism. In the proposed approach, first, the domain experts plan the diet goal for the involved diabetes and create the nutrition facts of common Taiwanese food. Second, the involved diabetics are requested to routinely input eaten items. Third, the ontology-creating mechanism constructs a T2FO, including a type-2 FPPO, a type-2 FFO, and a set of type-2 FPFOs. Finally, the T2FS-based IDRA retrieves the built T2FO to recommend a personal diabetic meal plan. The experimental results show that the proposed approach can work effectively and that the menu can be provided as a reference for the involved diabetes after diet validation by domain experts.

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

In this paper, we present a novel type-2 fuzzy systems based adaptive architecture for agents embedded in ambient intelligent environments (AIEs). Type-2 fuzzy systems are able to handle the different sources of uncertainty and imprecision encountered in AIEs to give a very good response. The presented agent architecture uses a one pass method to learn in a nonintrusive manner the users particular behaviors and preferences for controlling the AIE. The agent learns the users behavior by learning his particular rules and interval type-2 Membership Functions (MFs), these rules and MFs can then be adapted online incrementally in a lifelong learning mode to suit the changing environmental conditions and user preferences. We will show that the type-2 agents generated by our one pass learning technique outperforms those generated by genetic algorithms (GAs). 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 agents learnt and adapted to the occupants behavior whilst handling the encountered short term and long term uncertainties to give a very good performance that outperformed the type-1 agents while using smaller rule bases

A Historical Account of Types of Fuzzy Sets and Their Relationships

In this paper, we review the definition and basic properties of the different types of fuzzy sets that have appeared up to now in the literature. We also analyze the relationships between them and enumerate some of the applications in which they have been used.

Interval Type-2 Fuzzy Logic Congestion Control for Video Streaming Across IP Networks

Intelligent congestion control is vital for encoded video streaming of a clip or film, as network traffic volatility and the associated uncertainties require constant adjustment of the bit rate. Existing solutions, including the standard transmission control protocol (TCP) friendly rate control equation-based congestion controller, are prone to fluctuations in their sending rate and may respond only when packet loss has already occurred. This is a major problem, because both fluctuations and packet loss affect the end-users perception of the delivered video. A type-1 (T1) fuzzy logic congestion controller (FLC) can operate at video display rates and can reduce packet loss and rate fluctuations, despite uncertainties in measurements of delay arising from congestion and network traffic volatility. However, a T1 FLC employing precise T1 fuzzy sets cannot fully cope with the uncertainties associated with such dynamic network environments. A type-2 FLC using type-2 fuzzy sets can handle such uncertainties to produce improved performance. This paper proposes an interval type-2 FLC that achieves a superior delivered video quality compared with existing traditional controllers and a T1 FLC. To show the response in different network scenarios, tests demonstrate the response both in the presence of typical Internet cross-traffic as well as when other video streams occupy a bottleneck on an All-Internet protocol (IP) network. As All-IP networks are intended for multimedia traffic, it is important to develop a form of congestion control that can transfer to them from the mixed traffic environment of the Internet. It was found that the proposed type-2 FLC, although it is specifically designed for Internet conditions, can also successfully react to the network conditions of an All-IP network. When the control inputs were subject to noise, the type-2 FLC resulted in an order of magnitude performance improvement in comparison with the T1 FLC. The type-2 FLC also showed reduced packet loss when compared with the other controllers, again resulting in superior delivered video quality. When judged by established criteria, such as TCP-friendliness and delayed feedback, fuzzy logic congestion control offers a flexible solution to network bottlenecks. These findings offer the type-2 FLC as a way forward for congestion control of video streaming across packet-switched IP networks.

Embedded Type-2 FLC for Real-Time Speed Control of Marine and Traction Diesel Engines

Marine propulsion and traction diesel engines operate in highly dynamic and uncertain environments. The current speed controllers for marine/traction diesel engines are based on PID and type-1 fuzzy logic controllers (FLCs) which cannot fully handle the uncertainties associated with such dynamic environments. Type-2 FLCs can handle such uncertainties to produce a better control performance. However, type-2 FLCs have a computational overhead associated with the iterative type-reduction process which can reduce the FLC real-time performance, especially when operating on industrial embedded controllers which have limited computational and memory capabilities. In this paper, we introduce a real-time type-2 FLC that is suited for embedded controllers operating in marine/traction diesel engines. We have conducted numerous experiments where the embedded type-2 FLCs dealt with the uncertainties in real-time and displayed a robust control response that outperformed the PID and type-1 FLCs whilst using smaller rule bases

Evolving spiking neural network controllers for autonomous robots

In this paper we introduce a novel mechanism for controlling autonomous mobile robots that is based on using spiking neural networks (SNNs). The SNNs are inspired by biological neurons that communicate using pulses or spikes. As SNNs have shown to be excellent control systems for biological organisms, they have the potential to produce good control systems for autonomous robots. In this paper we present the use and benefits of SNNs for mobile robot control. We also present an adaptive genetic algorithm (GA) to evolve the weights of the SNNs online using real robots. The adaptive GA using adaptive crossover and mutation converge in a small number of generations to solutions that allow the robots to complete the desired tasks. We have performed many experiments using real mobile robots to test the evolved SNNs in which the SNNs provided a good response.