Showing papers by "Lakhmi C. Jain published in 2009"

Innovations in Swarm Intelligence

Abstract: Over the past two decades, swarm intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm intelligence models are inspired by social behaviours of simple agents interacting among themselves as well as with the environment, e.g., flocking of birds, schooling of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals. The main aim of this research book is to present a sample of recent innovations and advances in techniques and applications of swarm intelligence. Among the topics covered in this book include: particle swarm optimization and hybrid methods, ant colony optimization and hybrid methods, bee colony optimization, glowworm swarm optimization, and complex social swarms, application of various swarm intelligence models to operational planning of energy plants, modeling and control of nanorobots, classification of documents, identification of disease biomarkers, and prediction of gene signals. The book is directed to researchers, practicing professionals, and undergraduate as well as graduate students of all disciplines who are interested in enhancing their knowledge in techniques and applications of swarm intelligence.

Cooperative multi-robot path planning using differential evolution

Abstract: This paper provides an alternative approach to the co-operative multi-robot path planning problem using parallel differential evolution algorithms. Both centralized and distributed realizations for multi-robot path planning have been studied, and the performances of the methods have been compared with respect to a few pre-defined yardsticks. The distributed approach to this problem out-performs its centralized version for multi-robot planning. Relative performance of the distributed version of the differential evolution algorithm has been studied with varying numbers of robots and obstacles. The distributed version of the algorithm is also compared with a PSO-based realization, and the results are competitive.

Nonlinear dynamics and chaos methods in neurodynamics and complex data analysis

Abstract: In this paper, we review modern nonlinear dynamical methods used in neuroscience and complex data analysis. We start with the general description of nonlinear dynamics, its geometrical (and topological) picture, as well as its extreme case, deterministic chaos, including its most popular models and methods: Lorenz attractor, Lyapunov exponents, and Kolmogorov–Sinai entropy.

Intelligent agents in the evolution of Web and applications

Abstract: The Evolution of Intelligent Agents within the World Wide Web.- A Multi-agent System Based on Evolutionary Learning for the Usability Analysis of Websites.- Towards Norm-Governed Behavior in Virtual Enterprises.- e-JABAT - An Implementation of the Web-Based A-Team.- Adaptive and Intelligent Agents Applied in the Taking of Decisions Inside of a Web-Based Education System.- A Resource Discovery Method Based on Multiple Mobile Agents in P2P Systems.- Browsing Assistant for Changing Pages.- Considering Resource Management in Agent-Based Virtual Organization.- BDI Agents: Flexibility, Personalization, and Adaptation for Web-Based Support Systems.- Ontology for Agents and the Web.- Ontology Agents and Their Applications in the Web-Based Education Systems: Towards an Adaptive and Intelligent Service.- An Evolutionary Approach for Intelligent Negotiation Agents in e-Marketplaces.- Security of Intelligent Agents in the Web-Based Applications.

Knowledge Processing and Decision Making in Agent-Based Systems

Abstract: Knowledge processing and decision making in agent-based systems constitute the key components of intelligent machines. The contributions included in the book are: Innovations in Knowledge Processing and Decision Making in Agent-Based Systems Towards Real-World HTN Planning Agents Mobile Agent-Based System for Distributed Software Maintenance Software Agents in New Generation Networks: Towards the Automation of Telecom Processes Multi-agent Systems and Paraconsistent Knowledge An Agent-based Negotiation Platform for Collaborative Decision-Making in Construction Supply Chain An Event-Driven Algorithm for Agents at the Web A Generic Mobile Agent Framework Toward Ambient Intelligence Developing Actionable Trading Strategies Agent Uncertainty Model and Quantum Mechanics Representation Agent Transportation Layer Adaptation System Software Agents to Enable Service Composition through Negotiation Advanced Technology Towards Developing Decentralized Autonomous Flexible Manufacturing Systems

Computational Intelligence in Medical Decisions Making

Abstract: Computation intelligence paradigms including artificial neural networks, fuzzy systems, evolutionary computing techniques, intelligent agents and so on provide a basis for human like reasoning in medical systems. Approximate reasoning is one of the most effective fuzzy systems. The compositional rule of inference founded on the logical law modus ponens is furnished with a true conclusion, provided that the premises of the rule are true as well. Even though there exist different approaches to an implication, being the crucial part of the rule, we modify the early implication proposed in our practical model concerning a medical application. The approximate reasoning system presented in this work considers evaluation of a risk in the situation when physicians weigh necessity of the operation on a patient. The patient’s clinical symptom levels, pathologically heightened, indicate the presence of a disease possible to recover by surgery. We wish to evaluate the extension of the operation danger by involving particularly designed fuzzy sets in the algorithm of approximate reasoning.

Innovations in Web Personalization

Abstract: The diffusion of the Web and the huge amount of information available online have given rise to the urgent need for systems able to intelligently assist users, when they browse the network. Web personalization offers this invaluable opportunity, representing one of the most important technologies required by an ever increasing number of real-world applications. This chapter presents an overview of the Web personalization in the endeavor of Intelligent systems.

Advances in Swarm Intelligence

Abstract: In this chapter, advances in techniques and applications of swarm intelligence are presented. An overview of different swarm intelligence models is described. The dynamics of each swarm intelligence model and the associated characteristics in solving optimization as well as other problems are explained. The application and implementation of swarm intelligence in a variety of different domains are discussed. The contribution of each chapter included in this book is also highlighted.

Experimental analysis on Sarsa(λ) and Q(λ) with different eligibility traces strategies

Abstract: Temporal difference learning and eligibility traces are two mechanisms for solving reinforcement learning problems. The temporal difference technique bootstraps the state value or state-action value at every step as with dynamic programming, and learns by sampling episodes from experience as in the Monte Carlo approach. Eligibility traces is a mechanism that offers a means for recording the degree of which state is eligible for undergoing learning process. This paper aims to investigate the underlying mechanism of eligibility traces strategies using on-policy and off-policy learning algorithms. In doing so, the performance metrics can be obtained by defining the learning problem in a simulation environment, in conjunction with different learning algorithms. However, measuring learning performance and analysing sensibility are very expensive because such performance metrics can only be obtained by running an experiment with different parameter values. This paper proposes a comparative study for analysing the mechanism of eligibility traces. The objective of this paper is to compare and investigate the influences on performance caused by those different approaches.

Recent Advances in Decision Making

Abstract: Intelligent paradigms are increasingly finding their ways in the design and development of decision support systems. This book presents a sample of recent research results from key researchers. The contributions include: Introduction to intelligent systems in decision making - A new method of ranking intuitionistic fuzzy alternatives - Fuzzy rule base model identification by bacterial memetic algorithms - Discovering associations with uncertainty from large databases - Dempster-Shafer structures, monotonic set measures and decision making - Interpretable decision-making models - A general methodology for managerial decision making - Supporting decision making via verbalization of data analysis results using linguistic data summaries - Computational intelligence in medical decisions making. This book is directed to the researchers, graduate students, professors, decision makers and to those who are interested to investigate intelligent paradigms in decision making.

A neural network based approach for protein structural class prediction

Abstract: The paper provides an alternative approach to protein structural class prediction employing artificial neural network. Existing works on protein structural class prediction are computationally intensive. The method employs SOFM for extraction of representative feature vectors, for the four different structural classes and then uses Principal Component Analysis for finding optimum feature vector dimension. Nearest neighborhood classification technique is finally utilised; to classify these protein datapoints to their respective classes. The proposed work presented in this paper, maintains the same level of classification accuracy in minimum computation time, as it employs most prominent and reduced number of feature set for classification.

Complex Systems in Knowledge-based Environments: Theory, Models and Applications

Abstract: The book presents the latest research on the theory, models and applications of complex systems in knowledge-based environment The amount of information is increasing at an exponential rate Thus, our systems are getting complex day by day For example the world wide web carries practically infinite information The real challenge is to reinvent theory to deal with complex systems The provisional contents in this book will be based on the theory and practical applications of complex systems in knowledge-based environment The contents will be based on but not limited to: - Advanced Knowledge-Based paradigms in complex systems; - Information models and architectures of complex systems; - Intelligent agents in complex systems design; - Multi-media systems and practical applications

Theoretical advances and applications of intelligent paradigms

Abstract: What is an “intelligent paradigm”? While it is difficult to provide an exact definition for this term, the interests in intelligent paradigms research generally focus on designing and developing of computerized machines or systems that exhibit the capabilities of learning from experience, adapting to the surrounding environment, as well as understanding and controlling ones thinking or reasoning process. These are some crucial characteristics of intelligent paradigms so that they can be deployed as usable and useful tools to assist humans in daily activities. Indeed, recent research and development in intelligent paradigms has opened up the way for a number of theoretical advances and successful applications of intelligent techniques and approaches in various domains. Diverse intelligent paradigms are available in the literature, encompassing, to name a few, artificial neural networks, evolutionary algorithms, multiagent systems, artificial immune systems, swarm intelligence, knowledge-based systems, case-based reasoning, as well as hybrid intelligent systems in which these paradigms are contained. Applications of intelligent paradigms also span across various fields, covering, to name a few, information processing, decision making, control and robotics, industrial and medical diagnosis, data mining, e-learning and e-commerce, knowledge management, as well as virtual reality and multimedia. In this special issue, a total of eight articles are collected to showcase a small fraction of some recent advances in theory and application of intelligent paradigms. These articles present research into various intelligent paradigms, with their effectiveness in tackling different real-world problems demonstrated and discussed. A brief outline of each article is as follows. A powered wheelchair is an effective vehicle to help elderly or handicapped people move around the ordinary areas. Song et al. design of an electromyogram (EMG) pattern classifier that is robust against muscular fatigue effects for powered wheelchair control. It is discovered that variations of feature values owing to the effect of muscular fatigue are consistent for sustained duration. This finding leads to a new fatigue compensation method, and the fuzzy Min-Max neural network is employed as a robust EMG-based pattern classifier through the adaptation process of its hyperboxes. The proposed approach demonstrates improved performance for continuous control of powered wheelchair. Path planning is a fundamental problem in mobile robotics. The work by Chakraborty et al. addresses the issue of multi-robot path planning by using parallel differential evolution algorithms. Both centralized and distributed realizations for multi-robot path planning are studied, and the performances of the methods are compared with respect to a few pre-defined yardsticks. Most industrial plants are complex, nonlinear, time varying with time delay, and difficult to control. A fuzzy logic-based system is suitable for their control as it combines measurements, experts’ knowledge and op-

Web Personalization in Intelligent Environments

Abstract: Web is evolving at a speed never experienced by any other discipline before. This research book includes a sample of new research directions on Web personalization in intelligent environments. The contributions include an Introduction to Web Personalization, Semantic Content-based Recommender System, Exploiting ontologies for Web search personalization , How to Derive Fuzzy User Categories for Web Personalization, A Taxonomy of Collaborative-based Recommender SystemandA System for Fuzzy Items Recommendation. This book is directed to the researchers, graduate students, professors and practitioner interested in Web personalization.

Innovations in Neural Information Paradigms and Applications

Abstract: This research book presents some of the most recent advances in neural information processing models including both theoretical concepts and practical applications. The contributions include: Advances in neural information processing paradigms - Self organizing structures - Unsupervised and supervised learning of graph domains - Neural grammar networks - Model complexity in neural network learning - Regularization and suboptimal solutions in neural learning - Neural networks for the classification of vectors, sequences and graphs - Metric learning for prototype-based classification - Ensembles of neural networks - Fraud detection using machine learning - Computational modeling of neural multimodal integration. This book is directed to the researchers, graduate students, professors and practitioner interested in recent advances in neural information processing paradigms and applications.

New Advances in Intelligent Decision Technologies: Results of the First KES International Symposium IDT'09

Abstract: IDT (Intelligent Decision Technologies) seeks an interchange of research on intelligent systems and intelligent technologies which enhance or improve decision making in industry, government and academia The focus is interdisciplinary in nature, and includes research on all aspects of intelligent decision technologies, from fundamental development to the applied system It constitutes a great honor and pleasure for us to publish the works and new research results of scholars from the First KES International Symposium on Intelligent Decision Technologies (KES IDT'09), hosted and organized by University of Hyogo in conjunction with KES International (Himeji, Japan, April, 2009) The symposium was concerned with theory, design, development, implementation, testing and evaluation of intelligent decision systems Its topics included intelligent agents, fuzzy logic, multi-agent systems, artificial neural networks, genetic algorithms, expert systems, intelligent decision making support systems, information retrieval systems, geographic information systems and knowledge management systems These technologies have the potential to support decision making in many areas of management, international business, finance, accounting, marketing, healthcare, military applications, production, networks, traffic management, crisis response and human interfaces

Innovations in Knowledge Processing and Decision Making in Agent-Based Systems

Abstract: This chapter introduces knowledge processing and decision making using agent-based technologies. The importance of creating effective and efficient computerized systems for extracting information and processing knowledge as well as for supporting decision making activities is highlighted. Then, an overview covering agent-based software tools and development methodologies, and usability and challenges of agent-based systems in industrial applications is presented. The contribution of each chapter included in this book is also described.

An Intelligent Decision Support System Using Expert Systems in a MAS

Abstract: Safety onboard airborne platforms rests heavily on the way they are fixed. This fact includes repairs and testing, to reduce its down time. Maintenance practices using these components are achieved using generic and specific test equipment within the existing Maintenance Management System (MMS). This research paper reports the work performed to improve reliability and maintainability of Avionics Systems using an Intelligent Decision Support System (IDSS). In order to understand the shortcomings of the existing system, the prevalent practices and methodologies are researched. The paper reports the significant improvements made by integrating autonomous information sources as knowledge into an IDSS. Improvements are made by automating the existing data collection to create an expert system using intelligent agents. Data Mining techniques and intelligent agents are employed to create an expert system. Using feedback, the IDSS generates forecasts, alerts and warnings prior to system availability being compromised. If the data was stored electronically, Data Mining techniques and intelligent agents could be employed to create an expert system. Using feedback, an IDSS should generate forecasts or warnings prior to system availability being compromised. A Knowledge Base of all aspects of the logistics cycle is created as the system ages, to help make informed decisions about the platform, the Unit Under Test (UUT) or even the environment that supports it.

The Evolution of Intelligent Agents within the World Wide Web

Abstract: The definition of an agent still needs to be agreed upon [1] and the use of multiple agents to form a team is being examined by many researchers [2]. The study of Artificial Intelligence (AI) is diverse because of each domain has encountered a bottleneck or some impasse has forced research to look further a field to find solutions [3]. Agent teaming was one of those choices. Each team consists of one or more agent which form a Multi-Agent System (MAS) [4]. Currently these have a fixed hierarchy and predetermined functionality to achieve specified goals [5]. Ideally that teams should seamlessly interoperate within its environment, autonomously adapt to new tasks and rapidly switch context as required. Learning, cooperation, collaboration and trust are other characteristics that deserve discussion and development, however, the above challenge would represent a significant leap in the natural progression to agent oriented programming.

Advances in Decision Making

Abstract: This chapter presents the application of Computational Intelligence (CI) paradigms for supporting decision making processes. First, the three main CI techniques, i.e., evolutionary computing, fuzzy computing, and neural computing, are introduced. Then, a review of recent applications of CI-based systems for decision making in various domains is presented. The contribution of each chapter included in this book is also described. A summary of concluding remarks is presented at the end of the chapter.

Advances in intelligent methodologies and techniques

Abstract: This chapter introduces a number of intelligent methodologies and techniques stemmed from Artificial Intelligence (AI). An overview of various intelligent models arisen from expert systems, artificial neural networks, fuzzy logic, genetic algorithms, decision trees, and agent technologies is presented. Application examples of these intelligent models in various domains are also presented. Then, the contribution of each chapter included in this book is described. A summary of concluding remarks is presented at the end of the chapter.

A Multi-Agent Classifier System Based on the Trust-Negotiation-Communication Model

Abstract: In this paper, we propose a Multi-Agent Classifier (MAC) system based on the Trust-Negotiation-Communication (TNC) model. A novel trust measurement method, based on the recognition and rejection rates, is proposed. Two agent teams, each consists of three neural network (NN) agents, are formed. The first is the Fuzzy Min-Max (FMM) agent team and the second is the Fuzzy ARTMAP (FAM) agent team. An auctioning method is also used for the negotiation phase. The effectiveness of the proposed model and the bond (based on trust) is measured using two benchmark classification problems. The bootstrap method is applied to quantify the classification accuracy rates statistically. The results demonstrate that the proposed MAC system is able to improve the performances of individual agents as well as the team agents. The results also compare favorably with those from other methods published in the literature.

Interoperability within Multi-Agent System

Abstract: Heuristic computing has consolidated into two streams of research (personification software and smart products) [1]. Cognitive Science is one of these fields and is attracting research effort based on Multi-Agent System (MAS). This research requires the formation of a voluntarily trust relationship in order for collaboration to occur, otherwise the imposed goal(s) may be aborted or fail completely [2,3]. An Agent Transportation Layer Adaption System (ATLAS) communications framework has been constructed to pass messages between separate agent systems. Discussion about confined frameworks have recently been extended to enable individual students associated with our Knowledge-Based and Intellingent Information and Engineering Systems (KES) Centre to fast track the development of their research concepts. A Plug 'n' Play concept based on a multi-agent blackboard architecture forms the basis of this research. This paper highlights the core architecture, we believe is required for Multi-Agent System (MAS) developers to achieve such flexibility. Agent teams can provide the ability to adapt and dynamically organize. The model described, concentrates on the blackboard design constructs to represents all functional blocks required to automate the processes required to complete any decomposed goals. Discussion in this paper is limited to the formative work within the foundation layers of that framework.

Defining a Decision-Support Framework in AC3M

Abstract: The prime objective of intelligent agents in Multi-Agent Systems (MAS) is to act. An effective action results from a solid decision-making process. Decision-Support Systems (DSS) are used in MASs to assist in the development of a course of action for an individual or system goal. To ensure decision-making processes between agents remain objective and coherent, coordination model and cooperative problem-solving methodologies need to be implemented. Presently, coordination models have been developed as data-driven, process or control-driven, or hybrid models. Cooperative problem-solving methodologies have been designed to solely focus on allowing agents to share their knowledge which assists in achieving an individual goal or a course of actions. Although coordination and cooperation has been successfully implemented as separate frameworks within intelligent MASs, there is a significant limitation: cognitive modeling within each framework is limited or non-existent. This is a major obstacle within dynamic or unknown environments, as these cognitive environments heavily depend on precise information being made available to make well-informed and instantaneous decisions. This paper shows how the relationship between Belief-Desire-Intention (BDI) and Observe-Orient-Decide-Act (OODA) architectures, coordination and cooperation can promote decision-making processes in MASs. The linking of the decision-making process with coordination and cooperation can ameliorate their lack of cognitive processes. This enhancement is demonstrated by the decision support framework within the Agent Coordination and Cooperation Cognitive Model, or AC3M.

Interoperable Intelligent Agents in a Dynamic Environment

Abstract: Current research conducted at the Knowledge-Based Intelligent Information and Engineering Systems (KES) Centre aims to improve/develop the communication aspects of an agent-oriented architecture that enables agents to automatically adapt their functionality at runtime based on message flows. Rigid designtime constraints can be replaced by a flexible plug-and-play componentized capability. Intelligent Agents (IAs) must possess interoperability and capability to share knowledge and context in order to achieve their goal(s). A concept demonstrator is being developed, using a number of dynamic distributed environments, to show how interoperable Multi-Agent Systems (MASs) can improve data flow in a distributed environment. The agents in this MAS are equipped with a number of sensors that provide data from the environment, which is fused to produce knowledge. The fused information is fed into an inference engine which contains the Subject Mater Expert (SME) knowledge-based required to make decision(s) and/or change some course of action.

Multilingual Agents in a Dynamic Environment

Abstract: Experiments conducted by the Knowledge-Based Intelligent Information and Engineering Systems (KES) Centre use Java to gain its many advantages, especially in a distributed and dynamically scalable environment. Interoperability within and across ubiquitous computing operations has evolved to a level where plug ‘n′ play protocols that invoke common interfaces, provide the flexibility required for effective multi-lingual communications. One example includes: dynamic agent functionality within simulations that automatically adapt to incoming data and/or languages via scripts or messaging to achieve data management and inference. This has been shown using demonstrations at the Centre herein. Many aspects of the model involve web centric transactions, which involve data mining or the use of other types of Intelligent Decision Support System (IDSS). Section One of this paper provides an introduction, Section Two introduces the basic concepts of Decision Support System (DSS), Section Three discusses Intelligent Decision Support System (IDSS) enhancements, Section Four explains how agents use a multi-lingual dynamic environment,while Section Five highlights conclusions and future research direction.

Intelligent Systems and Technologies: Methods and Applications

Abstract: Sun, 09 Dec 2018 09:31:00 GMT intelligent systems and technologies methods pdf Various forms of wireless communications technologies have been proposed for intelligent transportation systems. Radio modem communication on UHF and VHF frequencies ... Mon, 10 Dec 2018 03:04:00 GMT Intelligent transportation system Wikipedia Automation, Control and Intelligent Systems (ACIS) provides readers with a compilation of stimulating and up-to-date articles within the field of intelligent ... Fri, 07 Dec 2018 22:20:00 GMT Automation, Control and Intelligent Systems :: Science ... Artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by ... Sun, 09 Dec 2018 14:46:00 GMT Artificial intelligence Wikipedia ArtsIT 2018 7th EAI International Conference: ArtsIT, Interactivity & Game Creation October 24-26, 2018 Braga, Portugal Wed, 05 Dec 2018 09:22:00 GMT ArtsIT 2018 7th EAI International Conference: ArtsIT ...

Agent Transportation Layer Adaptation System

Abstract: Heuristic computing has consolidated into two streams of research. One that personifies software to exhibit human behaviour and an oher that provides innovative software or smart products [1]. The Turing test [2] was pivotal in providing researchers with a generally accepted method of classifying the work that now defines the major problems pursued within Artificial Intelligence (AI). Cognitive Science is one of these fields and Research in Multi-Agent System (MAS) has revealed that Agents must enter into a voluntarily trust relationship in order to collaborate, otherwise the imposed goal(s) may be aborted or fail completely [3, 4]. Current agent architectures present a finite limit to functionality when supporting one or more of these paradigms.