Multi‐agent distributed intelligent system based on fuzzy decision making

Generally decision making for solving ill‐structured problems in DSS takes place in uncertain situations. The main drawbacks of existing traditional DSS are inefficiencies associated with dealing with complex models and large databases. Usually a fuzzy DSS has many input variables and, hence, its knowledge base, containing the totality of fuzzy rules, is very large. Large rule base leads to disadvantages in speed, reliability, and complexity of DSS. This paper introduces an alternative concept for designing fuzzy DSS based on multi‐agent distributed artificial intelligent technology and fuzzy decision making. The main idea of the proposed DSS is based on granulation of the overall system intelligence between cooperative autonomous intelligent agents capable of competing and cooperating with each other in order to propose a total solution to the problem and organization (combining individual solutions) of the proposed solution into the final solution. It is supposed that every agent in DSS is characterized by a set of fuzzy criteria of unequal importance and definition of a “winner” agent is based on multi‐criteria fuzzy decision making involving unequal objectives. © 2000 John Wiley & Sons, Inc.     

Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Hypoglycaemia is a medical term for a body state with a low level of blood glucose. It is a common and serious side effect of insulin therapy in patients with diabetes. In this paper, we propose a system model to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus (TIDM) patients. The resulting model is a fuzzy inference system (FIS). The heart rate (HR), corrected QT interval of the electrocardiogram (ECG) signal (QT_c), change of HR, and change of QT_c are used as the input of the FIS to detect the hypoglycaemic episodes. An intelligent optimiser is designed to optimise the FIS parameters that govern the membership functions and the fuzzy rules. The intelligent optimiser has an implementation framework that incorporates two wavelet mutated differential evolution optimisers to enhance the training performance. A multi-objective optimisation approach is used to perform the training of the FIS in order to meet the medical standards on sensitivity and specificity. Experiments with real data of 16 children (569 data points) with TIDM are studied in this paper. The data are randomly separated into a training set with 5 patients (l99 data points), a validation set with 5 patients (177 data points) and a testing set with 5 patients (193 data points). Experiment results show that the proposed FIS tuned by the proposed intelligent optimiser can offer good performance of classification.     

A distributed semantic network model for a collaborative intelligent system

A methodology based on topology theory to model a semantic network for a collaborative system is given. This framework is used to support the creation of a semantic network and to define the associated intelligent cooperative system. Our methodology is illustrated via a set of agents whose knowledge-base is a semantic network. By a series of functions applied on a base of entities, issued from the application domain, a family of sets are synthesized with their subspaces correlated. The resultant subspaces and their relations form a network of elementary and complex concepts that can be naturally represented with the IDEF1x language. A prototype Multi-Agent System (MAS), set up with the Zeus platform,¹ was developed for the Process Plan domain, which was used as a case study. Full correspondence among the subspaces, the semantic network IDEF1x information model and the MAS implementation is obtained by employing this framework.     

A communication model based on the 2-tuple fuzzy linguistic representation for a distributed intelligent agent system on Internet

 Internet users are assisted by means of distributed intelligent agents in the information gathering process to find the fittest information to their needs. In this paper we present a distributed intelligent agent model where the communication of the evaluation of the retrieved information among the agents is carried out by using linguistic operators based on the 2-tuple fuzzy linguistic representation as a way to endow the retrieval process with a higher flexibility, uniformity and precision. The 2-tuple fuzzy linguistic representation model allows to make processes of computing with words without loss of information.     

Convenient T-S fuzzy model with enhanced performance using a novel swarm intelligent fuzzy clustering technique

To automatically extract T-S fuzzy models with enhanced performance from data is an interesting and important issue for fuzzy system modeling. In this paper, a novel methodology is proposed for this issue based on a three-step procedure. Firstly, the idea of variable length genotypes is introduced to the artificial bee colony (ABC) algorithm to derive a so-called Variable string length Artificial Bee Colony (VABC) algorithm. The VABC algorithm can be used to solve a kind of optimization problems where the length of the optimal solutions is not known as a priori. Secondly, fuzzy clustering without knowing cluster number as a priori is viewed as such kind of optimization problem. Thus, a novel version of Fuzzy C-Means clustering technique (VABC-FCM), holding powerful global search ability, is proposed based on the VABC algorithm. Use of VABC allows the encoding of variable cluster number. This makes VABC-FCM not require a priori specification of the cluster number. Finally, the proposed VABC-FCM algorithm is used to extract T-S fuzzy model from data. Such VABC-FCM based convenient T-S fuzzy model extraction methodology does not require a specification of rule number as a priori. Some artificial data sets are applied to validate the performance of the convenient T-S fuzzy model. The experimental results show that the proposed convenient T-S fuzzy model has low approximation error and high prediction accuracy with appreciate rule number. Moreover, the convenient T-S fuzzy model is used to model the characteristics of superheated steam temperature in power plant, and the results suggest the powerful performance of the proposed method.     

A cloud‐based responsive replenishment system in a franchise business model using a fuzzy logic approach

In today's volatile markets, increasingly unpredictable customer demand is exerting great challenges to responsive replenishment. The complexity of responsive replenishment is higher when the business is global in which demand in both domestic and overseas markets has to be catered for. The emergence of cloud computing has eased the difficulties as it allows nearly real‐time access to a universal platform for information sharing between franchisors and franchisees, creating huge opportunities for understanding global market needs for responsive replenishment. Considering the existence of uncertainties due to the fluctuating demands, fuzzy logic is useful in providing decision support for replenishment in uncertain environments. This paper presents a cloud‐based responsive replenishment system to manage operation data of a franchise business using cloud computing, and for analysis using fuzzy logic in order to provide franchisors with the required inventory levels. To the best of our knowledge, this is the first study that applies cloud computing and artificial intelligence techniques in franchising. A pilot run of the system is conducted in an education company, which is considered to be a good representation of an industry operating with a franchise model. The results show that the system allows franchisors to formulate effective responsive replenishment strategies.     

A hybrid fuzzy intelligent agent‐based system for stock price prediction

Stock price prediction is an important task for most investors and professional analysts. However, it is a tough problem because of the uncertainties involved in prices. This paper presents a four‐layer fuzzy multiagent system (FMAS) architecture to develop a hybrid artificial intelligence model based on the coordination of intelligent agents performing data preprocessing and function approximation tasks for next‐day stock price prediction. The first layer is dedicated to metadata creation. The second layer is aimed at data preprocessing using stepwise regression analysis and self‐organizing map neural network clustering for modularizing prediction problems. The third layer is aimed at model building for each cluster using genetic fuzzy systems and evaluating built models to choose the best evolved fuzzy system for each cluster. Finally, the fourth layer provides model analysis and knowledge presentation. The capability of FMAS is evaluated by applying it on stock price data gathered from IT and airline sectors and comparing the outcomes with the results of other methods. The results show that FMAS outperforms all previous methods, so it can be considered as a suitable tool for stock price prediction problems. © 2012 Wiley Periodicals, Inc.     

Supplier selection model for commodities procurement. Optimised assessment using a fuzzy decision support system

The selection of the most suitable supplier for a procurement process has a markedly strategic aspect for a company. Within this ambit, the literature review shows a lack of uniformity in the terms used to define the phases or components of a procurement process as well as in the election of the critical variables used to select the most suitable supplier. Furthermore, this literature shows a wide variety of individual and integrated methodologies that have been developed so far in an attempt to optimise such a selection.This work proposes a new suppliers’ evaluation-and-selection model. The model homogenises the terminology involved in such processes and fulfils three main goals. First, it allows the joint assessment and comparison among new and historical suppliers, identifying the key evaluation factors in each case. Second, it allows the inherent knowledge about evaluation to be flexibly adapted to the type of product to be purchased – in this paper “basic products” – according to Kraljic's terminology (a major issue in procurement management and not taken into account by any of the models proposed so far). Finally, a FDSS is proposed to make the model operational. The proposed method is robust enough to improve the main shortcomings of more simplistic methods (e.g. those based on weights) and eases the comprehension of the embedded knowledge within the supplier evaluation processes. Simultaneously, this method avoids the complexity of real-life implementation that many of some more sophisticated hybrid methods proposed in recent times – not free of certain additional disadvantages. Finally, the practical usefulness of the proposed method is ascertained through an empirical test in a specific business environment.     

A general computational recognition primed decision model with multi-agent rescue simulation benchmark

Analytical decision making strategies rely on weighing pros and cons of multiple options in an unbounded rationality manner. Contrary to these strategies, recognition primed decision (RPD) model which is a primary naturalistic decision making (NDM) approach assumes that experienced and professional decision makers when encounter problems in real operating conditions are able to use their previous experiences and trainings in order to diagnose the problem, recall the appropriate solution, evaluate it mentally, and implement it to handle the problem in a satisficing manner. In this paper, a computational form of RPD, now called C-RPD, is presented. Unified Modeling Language was used as a modeling language to represent the proposed C-RPD model in order to make the implementation easy and obvious. To execute the model, RoboCup Rescue agent simulation environment, which is one of the best and the most famous complex and multi-agent large-scale environments, was selected. The environment simulates the incidence of fire and earthquakes in urban areas where it is the duty of the police forces, firefighters and ambulance teams to control the crisis. Firefighters of SOS team are first modeled and implemented by utilizing C-RPD and then the system is trained using an expert’s experience. There are two evaluations. To find out the convergence of different versions developed during experience adding, some of the developed versions are chosen and evaluated on seven maps. Results show performance improvements. The SOS team ranked first in an official world championship and three official open tournaments.     

A context-driven intelligent database processing system using object-oriented fuzzy cognitive maps

Most data sets that describe and evolve from real-world systems are by nature semiquantitative or qualitative rather than quantitative. This can mean large variations in the significance of results that are derived from this data for decision-making processes given that the original database provides training and prototypical examples that reflect systems of events in the real world. In this article we propose a structure for a Knowledge-Based System (KBS) that is derived using significance within given contextual domains. Data that would ordinarily be classified by simple attribute classification techniques are now categorized by understanding patterns and value distributions for attributes and attribute domains that exist within rich and dense databases such as in the case of census databases<‡> and Geographic Information Systems (GIS)<§> rich by the very number of fields and interpretations, depending on the context in which the data are to be reviewed. The structure we have implemented for capturing and structuring semiquantitative information is the Fuzzy Cognitive Map (FCM). We also reduce the number of false patterns labeled “significant” by incorporating the knowledge used by human experts to find significance within the data. We treat this knowledge as initial background knowledge and as a minimal set for distinguishing significance for particular attribute values within a given context. © 1996 John Wiley & Sons, Inc.     

A new consensus model for group decision making using fuzzy ontology

Involving many people in decision making does not guarantee success. In practice, there are always individuals who try to exert pressure in order to persuade others who could easily be influenced. In these situations, classical group decision making models fail. Thus, there is still the necessity of developing tools to help users reach collective decisions as if they participated in a real face to face meeting. In such a way, a proper negotiation process can lead to successful solutions. Therefore, we propose a new consensus model to deal with the psychology of negotiation by using the power of a fuzzy ontology as weapon of influence in order to improve group decision scenarios making them more precise and realistic. In addition, the use of a fuzzy ontology gives us the possibility to take into account large sets of alternatives.     

An adaptive model of a distributed intrusion detection system

We have proposed an adaptive model of a system for detecting intrusions in a distributed computer network. The basis of the detection system consists of various data-mining methods that make it possible to classify network interaction as normal or anomalous using many attributes extracted from network traffic.     

一种基于移动Agent的分布式入侵检测系统模型MABDIDS

分析了当前的入侵检测技术的发展及存在的主要缺陷,介绍了移动Agent的概念及其优点,提出了一种新的基于移动Agent的分布式入侵检测模型MABDIDS。MABDIDS利用移动Agent的优点,设计了针对主机和网络两种环境而分别具有不同运行机制的两种检测主体,通过将多个监控节点组织成层次结构来协同实现分布式入侵检测,解决了当前分布式入侵检测系统中存在的主要问题。     

Developing a marketing decision model using a knowledge-based system

This paper describes using a knowledge-based system for developing a marketing decision model. The approach used in this study uses a decision table as a knowledge engineering tool. The decision table is used as a means of representing a set of decision rules to construct a developed marketing decision model. To support the modeling process, Prologa, an existing decision table engineering workbench, is used. The developed marketing decision model is used to determine the entrance time of a new product into market by utilizing knowledge-based systems. Presentation of a new product to the market at the best time will provide an advantage to competing companies and will increase their market share.     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.     

A high‐performance face detection system using OpenMP

We present the development of a novel high‐performance face detection system using a neural network‐based classification algorithm and an efficient parallelization with OpenMP. We discuss the design of the system in detail along with experimental assessment. Our parallelization strategy starts with one level of threads and moves to the exploitation of nested parallel regions in order to further improve, by up to 19%, the image‐processing capability. The presented system is able to process images in real time (38 images/sec) by sustaining almost linear speedups on a system with a quad‐core processor and a particular OpenMP runtime library. Copyright © 2009 John Wiley & Sons, Ltd.     

OptiPres: a distributed mobile agent decision support system for optimal patient drug prescription

Prescribing the right drugs for a patient is a difficult task that takes into consideration several factors. The Institute of Medicine (IOM), U.S.A., has reported based on two major studies (1999–2001 & 2006) that prescribing the wrong medication is a big problem, and the effects can sometimes be fatal. To address this problem, we designed and implemented, a distributed intelligent mobile agent-based system by the name, OptiPres. This system will be used by doctors on their smart phones while prescribing medicines. It will assist them in making more informed decisions by either choosing the optimal solution from processing a repository of past decisions or by presenting a set of possible drugs and using criteria specified by them to identify the optimal drug. The evaluation of OptiPres was done by comparing its recommended outcome of three predefined medical scenarios against the recommendations from a group of doctors and the World Health Organization (WHO) manual entitled:‘Guide to Good Prescribing’. The results indicate that OptiPres is effective in prescribing optimal drugs and in reducing the cognitive burden on doctors, especially in subjective decision making contexts where they have to consider multiple parameters.     

A type 2-hesitation fuzzy logic based multi-criteria group decision making system for intelligent shared environments

Intelligent environments aim to maximize the user comfort and safety while achieving other objectives such as energy minimization. Intelligent shared spaces (such as homes, classrooms, offices, libraries, etc.) need to consider the preferences of users from diverse backgrounds. However, there are high levels of uncertainties faced in intelligent shared spaces. Hence, there is a need to employ intelligent decision making systems which can consider the various users preferences and criteria in order to achieve the convenience of the various users while handling the faced uncertainties. Therefore, we propose a Fuzzy Logic-Multi-Criteria Group Decision Making (FL-MCGDM) system which provides a comprehensive valuation from a group of users/decision makers based on the aggregation of users’ opinions and preferences. The proposed FL-MCGDM system employs an interval type-2 fuzzy logic and hesitation index [from Intuitionistic Fuzzy Sets (IFSs)]. We have carried out experiments in the intelligent apartment (iSpace) located in the University of Essex to evaluate various approaches employing group decision making techniques for illumination selection in an intelligent shared environment. It was found that the Footprint of Uncertainty (of interval type-2 fuzzy sets) and hesitation index (of intuitionistic fuzzy sets (IFSs)) are able to provide a measure of the uncertainties present among the various decision makers. The proposed Type 2-Hesitation FL-MCGDM system better agrees with the users’ decision compared to existing fuzzy MCDM including the Fuzzy Logic based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), type-1 FL-MCGDM and interval type-2 in FL-MCGDM.     

Online anomaly detection for multi‐source VMware using a distributed streaming framework

Anomaly detection refers to the identification of patterns in a dataset that do not conform to expected patterns. Such non‐conformant patterns typically correspond to samples of interest and are assigned to different labels in different domains, such as outliers, anomalies, exceptions, and malware. A daunting challenge is to detect anomalies in rapid voluminous streams of data. This paper presents a novel, generic real‐time distributed anomaly detection framework for multi‐source stream data. As a case study, we investigate anomaly detection for a multi‐source VMware‐based cloud data center, which maintains a large number of virtual machines (VMs). This framework continuously monitors VMware performance stream data related to CPU statistics (e.g., load and usage). It collects data simultaneously from all of the VMs connected to the network and notifies the resource manager to reschedule its CPU resources dynamically when it identifies any abnormal behavior from its collected data. A semi‐supervised clustering technique is used to build a model from benign training data only. During testing, if a data instance deviates significantly from the model, then it is flagged as an anomaly. Effective anomaly detection in this case demands a distributed framework with high throughput and low latency. Distributed streaming frameworks like Apache Storm, Apache Spark, S4, and others are designed for a lower data processing time and a higher throughput than standard centralized frameworks. We have experimentally compared the average processing latency of a tuple during clustering and prediction in both Spark and Storm and demonstrated that Spark processes a tuple much quicker than storm on average. Copyright © 2016 John Wiley & Sons, Ltd.