7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009) 55 Advances in Intelligent and Soft Computing 55 (original) (raw)

7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS'09)

2009

ADVANCES IN INTELLIGENT AND SOFT COMPUTING 55 7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009) 55 Advances in Intelligent and Soft Computing 55 Th e series "Advances in Intelligent and Soft Computing" contains publications on various areas within so-called soft computing which include fuzzy sets, rough sets, neural networks, evolutionary computations, probabilistic and evidential reasoning, multi-valued logic, and related fi elds. Th e publications within "Advances in Intelligent and Soft Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. Th ey cover signifi cant recent developments in the fi eld, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and worldwide distribution. Th is permits a rapid and broad dissemination of research results.

PAAMS 2009-7th International Conference on Practical Applications of Agents and Multi-Agent Systems

Trends in Practical Applications of Agents and Multiagent Systems

Advances in Intelligent Systems and Computing, 2013

Yves Demazeau et al. (Eds.) ADVANCES IN INTELLIGENT AND SOFT COMPUTING 71 Trends in Practical Applications of Agents and Multiagent Systems 8th International Conference on Practical Applications of Agents and Multiagent Systems 71 Advances in Intelligent and Soft Computing 71 Th e series "Advances in Intelligent and Soft Computing" contains publications on various areas within so-called soft computing which include fuzzy sets, rough sets, neural networks, evolutionary computations, probabilistic and evidential reasoning, multi-valued logic, and related fi elds. Th e publications within "Advances in Intelligent and Soft Computing" are primarily textbooks and proceedings of important conferences, symposia and congresses. Th ey cover signifi cant recent developments in the fi eld, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and worldwide distribution. Th is permits a rapid and broad dissemination of research results. issn 1867-5662 Demazeau et al. (Eds.

A Survey of Multi-Agent based Intelligent Decision Support System for Medical Classification Problems

International Journal of Computer Applications, 2015

There has been growing on big data since last decade for discovering useful trends or patterns that are used in diagnosis and decision making. Intelligent decision support system an automated judgment that supports decision making is composed of human and computer interaction to help in decision making accuracy. Also multi-agent systems (MAS) are collections of independent intelligent entities that collaborate in the joint resolution of a complex problem. Multi-agent intelligent decision support systems can be used to solve large-scale convention problem. This paper is a survey of the recent research in multiagent and intelligent decision support systems to support for classification problems. The purpose of the survey described in this paper is the development of a novel large-scale hybrid medical diagnosis system based on Multi-agent Intelligent Decision Support System (IDSS) for distributed database.

A Framework For Intelligent Multi Agent System Bas

TIntelligent multi agent systems have great potentials to use in different purposes and research areas. One of the important issues to apply intelligent multi agent systems in real world and virtual environment is to develop a framework that support machine learning model to reflect the whole complexity of the real world. In this paper, we proposed a framework of intelligent agent based neural network classification model to solve the problem of gap between two applicable flows of intelligent multi agent technology and learning model from real environment. We consider the new Supervised Multilayers Feed Forward Neural Network (SMFFNN) model as an intelligent classification for learning model in the framework. The framework earns the information from the respective environment and its behavior can be recognized by the weights. Therefore, the SMFFNN model that lies in the framework will give more benefits in finding the suitable information and the real weights from the environment which result for better recognition. The framework is applicable to different domains successfully and for the potential case study, the clinical organization and its domain is considered for the proposed framework

Soft Computing: Theories and Applications

Advances in Intelligent Systems and Computing

The series "Advances in Intelligent Systems and Computing" contains publications on theory, applications, and design methods of Intelligent Systems and Intelligent Computing. Virtually all disciplines such as engineering, natural sciences, computer and information science, ICT, economics, business, e-commerce, environment, healthcare, life science are covered. The list of topics spans all the areas of modern intelligent systems and computing such as: computational intelligence, soft computing including neural networks, fuzzy systems, evolutionary computing and the fusion of these paradigms, social intelligence, ambient intelligence, computational neuroscience, artificial life, virtual worlds and society, cognitive science and systems, Perception and Vision, DNA and immune based systems, self-organizing and adaptive systems, e-Learning and teaching, human-centered and human-centric computing, recommender systems, intelligent control, robotics and mechatronics including human-machine teaming, knowledge-based paradigms, learning paradigms, machine ethics, intelligent data analysis, knowledge management, intelligent agents, intelligent decision making and support, intelligent network security, trust management, interactive entertainment, Web intelligence and multimedia. The publications within "Advances in Intelligent Systems and Computing" are primarily proceedings of important conferences, symposia and congresses. They cover significant recent developments in the field, both of a foundational and applicable character. An important characteristic feature of the series is the short publication time and worldwide distribution. This permits a rapid and broad dissemination of research results.

Computational Agents in Complex Decision Support Systems

2010

The article introduces a general approach to decision making in complex systems and architecture for agent-based decision support systems (DSS). The approach contributes to decentralization and local decision making within a standard work flow. The architecture embodies the logics of the decision developing work flow and is virtually organized as a layered structure, where each level is oriented to solve one of the three following goals: data retrieval, fusion and pre-processing; data mining and evaluation; and, decision making, alerting, solutions and predictions generation. In order to test our approach, we have designed and implemented an agent-based DSS, which deals with an environmental issue. The system calculates the impacts imposed by the pollutants on the morbidity, creates models and makes forecasts by permitting to try possible ways of situation change. We discuss some used data mining techniques, namely, methods and tools for classification, function approximation, association search, difference analysis, and others. Besides, to generate sets of administrative solutions, we develop decision creation and selection work flows, which are formed and then selected in accordance with the maximum of possible positive effect and evaluated by external and internal criteria. To conclude, we show that our system provides all the necessary steps for standard decision making procedure by using computational agents. We use so much traditional data mining techniques, as well as other hybrid methods, with respect to data nature. The combination of different tools enables gaining in quality and precision of the reached models, and, hence, in the recommendations that are based on these models. The received dependencies of interconnections and associations between the factors and dependent variables help correcting recommendations and avoiding errors.

A Framework For Intelligent Multi Agent System Based Neural Network Classification Model

Intelligent multi agent systems have great potentials to use in different purposes and research areas. One of the important issues to apply intelligent multi agent systems in real world and virtual environment is to develop a framework that support machine learning model to reflect the whole complexity of the real world. In this paper, we proposed a framework of intelligent agent based neural network classification model to solve the problem of gap between two applicable flows of intelligent multi agent technology and learning model from real environment. We consider the new Supervised Multi-layers Feed Forward Neural Network (SMFFNN) model as an intelligent classification for learning model in the framework. The framework earns the information from the respective environment and its behavior can be recognized by the weights. Therefore, the SMFFNN model that lies in the framework will give more benefits in finding the suitable information and the real weights from the environment which result for better recognition. The framework is applicable to different domains successfully and for the potential case study, the clinical organization and its domain is considered for the proposed framework.

7th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 2009) 55 Advances in Intelligent and Soft Computing 55 (original) (raw)

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