Samia Nefti

Intelligent Adaptive Mobile Robot Navigation

This paper deals with the application of a neuro-fuzzy inference system to a mobile robot navigation in an unknown, or partially unknown environment. The final aim of the robot is to reach some pre-defined goal. For this purpose, a sort of a co-operation between three main sub-modules is performed. These sub-modules consist in three elementary robot tasks: following a wall, avoiding an obstacle and running towards the goal. Each module acts as a Sugeno–Takagi fuzzy controller where the inputs are the different sensor information and the output corresponds to the orientation of the robot. The rule-base is generated by the controller after some learning process based on a neural architecture close to that used by Wang and Menger. This leads to adaptive neuro-fuzzy inference systems (ANFIS) (one for each module). The adaptive navigation system (ANFIS), based on integrated reactive-cognitive parts, learns and generates the required knowledge for achieving the desired task. However, the generated rule-base suffers from redundancy and abundance of data, most of which are less useful. This makes the assignment of a linguistic label to the associated variable difficult and sometimes counter-intuitive. Consequently, a simplification phase allowing elimination of redundancy is required. For this purpose, an algorithm based on the class of fuzzy c-means algorithm introduced by Bezdek and we have developed an inclusion structure. Experimental results confirm the meaningfulness of the elaborated methodology when dealing with navigation of a mobile robot in unknown, or partially unknown environment.

A fuzzy trust model for e-commerce

It is argued that e-commerce has not reached its full potential and trust was often cited as the main reason why many customers are still skeptical about some online vendors. Many trust models have been developed, but most are subjective and did not take into account the vagueness and ambiguity of the domain and the specificity of customers. We have developed a model that attempts to identify the information customers expect to find on a vendors website to increase their trust and hence the likelihood of a transaction to take place. In this paper, we present a method based on fuzzy logic to evaluate trust in e-commerce. We argue that fuzzy logic is suitable for trust evaluation as it takes into account the uncertainties within e-commerce data and like human relationships, trust is often expressed by linguistics terms rather then numerical values. We validated the system using two case studies.

Systems Control With Generalized Probabilistic Fuzzy-Reinforcement Learning

Reinforcement learning (RL) is a valuable learning method when the systems require a selection of control actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems and RL, the environment is considered to be deterministic. In many problems, however, the consequence of an action may be uncertain or stochastic in nature. In this paper, we propose a novel RL approach to combine the universal-function-approximation capability of fuzzy systems with consideration of probability distributions over possible consequences of an action. The proposed generalized probabilistic fuzzy RL (GPFRL) method is a modified version of the actor-critic (AC) learning architecture. The learning is enhanced by the introduction of a probability measure into the learning structure, where an incremental gradient-descent weight-updating algorithm provides convergence. Our results show that the proposed approach is robust under probabilistic uncertainty while also having an enhanced learning speed and good overall performance.

A New Fuzzy Set Merging Technique Using Inclusion-Based Fuzzy Clustering

This paper proposes a new method of merging parameterized fuzzy sets based on clustering in the parameters space, taking into account the degree of inclusion of each fuzzy set in the cluster prototypes. The merger method is applied to fuzzy rule base simplification by automatically replacing the fuzzy sets corresponding to a given cluster with that pertaining to cluster prototype. The feasibility and the performance of the proposed method are studied using an application in mobile robot navigation. The results indicate that the proposed merging and rule base simplification approach leads to good navigation performance in the application considered and to fuzzy models that are interpretable by experts. In this paper, we concentrate mainly on fuzzy systems with Gaussian membership functions, but the general approach can also be applied to other parameterized fuzzy sets.

An agent based system for activity monitoring on network - ABSAMN

Computer network as a force drags its customers to share more and more resources while on the other hand managing such network recourses is a challenging job for an IT professional and perhaps becoming difficult humanly. In this paper, we have proposed an agent based system for activity monitoring on network (ABSAMN) for the monitoring of resources over a network, suitable for network of networks; commonly known as CAN (campus area network). Multi-agent system is a system composed of several agents, collectively capable of achieving goals that are difficult to achieve by an individual agent or monolithic system. We propose the use of multi-agent system to ensure proper system operation by watching for inconsistencies in user activities, node level activity, internet monitoring, and system configuration. The system is fully autonomous and once initialized with the given rules and domain knowledge ABSAMN manages resources on its own with the help of mobile agents. We have evaluated this architecture on the university campus having seven labs equipped with 20-300 number of PCs in various labs. Results were very promising and support the implementation of the solution.

iDetect: Content Based Monitoring of Complex Networks using Mobile Agents

With the evolution in computer networks over the last decade, researchers are trying to come up with efficient approaches which can help network administrator in implementing the acceptable use policy for large complex networks. In this paper we have modified An Agent Based System for Activity Monitoring on Network - ABSAMN architecture and proposed iDetect: Content Based Monitoring of Complex Network using Mobile Agents which uses the content (i.e. text, image and video) of the application for categorization purpose. iDetect is implemented in Java using Java Agent Development (JADE) framework and supports platform independence; however, the framework has been tested only on Microsoft Windows (any version) environment. We have evaluated iDetect and ABSAMN on same configuration concurrently at the university campus having four labs equipped with 60-120 number of PCs in various labs; experimental results shows that iDetect efficiently detects known/unknown illegal activities (applications/websites) running on the network as compared to ABSAMN.

Personalized information retrieval system in the framework of fuzzy logic

Due to increase in web-based applications, the need for enhanced information retrieval system that accommodate users needs become crucial. A wide range of commercial information retrieval systems are based on standard Boolean model and at less scale vector models. Although the deficiencies of these models are now part of text-book knowledge, the development of new models still have to overcome the feasibility and testing challenge. This paper advocates a fuzzy based approach for information retrieval where a new model is put forward. Also, its feasibility and performance are demonstrated through a testing with a large-scale university database and whose results are compared to a standard commercial Boolean model.

QUIET: A Methodology for Autonomous Software Deployment using Mobile Agents

Every software setup has an installation wizard that helps the user to install/un-install the software on PCs. Typically user interaction is required and the process cannot proceed without user input. Silent Unattended Installation Package Manager (SUIPM) automates the process of software installation/un-installation and can be used to deploy any software silently without user interaction. In this paper, we have proposed A Methodology for Autonomous Software Deployment using Mobile Agents, which deploys silent unattended installation/un-installation packages efficiently and smartly on networks without user interaction or intervention, suitable for network of networks, commonly known as CAN (campus area network). The system once initialized is fully autonomous and deployment of the software(s) is performed efficiently and autonomously with the help of mobile agents. We have evaluated this architecture on the university campus having 7 laboratories equipped with 20-300 PCs in various laboratories. Results are very promising and support the implementation of the solution.

A Modified Fuzzy Clustering for Documents Retrieval: Application to Document Categorization

The paper advocates the use of a new fuzzy-based clustering algorithm for document categorization. Each document/datum will be represented as a fuzzy set. In this respect, the fuzzy clustering algorithm, will be constrained additionally in order to cluster fuzzy sets. Then, one needs to find a metric measure in order to detect the overlapping between documents and the cluster prototype (category). In this respect, we use one of the interclass probabilistic reparability measures known as Bhattacharyya distance, which will be incorporated in the general scheme of the fuzzy c-means algorithm for measuring the overlapping between fuzzy sets. This enables the introduction of fuzziness in the document clustering in the sense that it allows a single document to belong to more than one category. This is in line with semantic multiple interpretations conveyed by single words, which support multiple membership to several classes. Performances of the algorithms will be illustrated using a case study from the construction sector.

Categorization of malicious behaviors using ontology-based cognitive agents

Every organization uses computer networks (consisting of networks of networks) for resource sharing (i.e. printer, files, etc.) and communication. Computer networks today are increasingly complex, and managing such networks requires specialized expertise. Monitoring systems help network administrators in monitoring and protecting their network by not allowing users to run illegal application or changing the configuration of network nodes. In this paper we have developed an agent based system for activity monitoring on networks (ABSAMN) and proposed Categorization of Malicious Behaviors using Cognitive Agents (CMBCA). This uses ontology to predict unknown illegal applications based on known illegal application behaviors. CMBCA is an intelligent multi agent system used to detect known and unknown malicious activities carried out users over the network. We have compared An Agent Based System for Activity Monitoring on Network (ABSAMN) and Categorization of Malicious Behaviors using Cognitive Agents (CMBCA) concurrently at the university campus having seven labs equipped with 20 to 300 PCs in various labs. Both systems were tested on the same configuration; results indicate that CMBCA outperforms ABSAMN in every aspect.