Adaptive multiagent system for seismic emergency management

Presently, most multiagent frameworks are typically programmed in Java. Since the JADE platform has been recently ported to .NET, we used it to create an adaptive multiagent system where the knowledge base of the agents is managed using the CLIPS language, also called from .NET. The multiagent system is applied to create seismic risk scenarios, simulations of emergency situations, in which different parties, modeled as adaptive agents, interact and cooperate.adaptive systems, risk management, seisms.

Design patterns for multi-agent simulations

The advent of mobile agent technology has brought along a few difficulties in designing a stable, efficient and scalable system for a certain problem. Agent-based simulations prove to be powerful tools for economic analyses. In this paper we aim at describing a set of design patterns which were specifically built for agents and multi-agent systems. The details of each design pattern discussed are presented and the possible applications and known issues are noted. In order to aid the software designers, we provide some examples of the basic implementation of these patterns using the JADE multi-agent framework.intelligent agent, multi-agent design, multi-agent simulation.

Intelligent medical robot society

Any treatment on long or short term duration and/or complexity begin to involve more and more complex hardware and software pieces of equipment. Most of them begin to have various degree of mobility. Although the medical staff has enough trouble in handling them some times. In this paper we propose a complex robot society to deserve a medical center. The evolution of human computer interface and of the complex expert systems with medical application drive us to idea that a dedicated medical society of intelligent agents can be created

Symbolic Deductive Reasoning using Connectionist Models

In this paper, we try to combine the possibility of symbolic deductive reasoning with the learning capability of the connectionist models. We introduce several algorithms for learning relations between concepts and finding paths in a transitive manner between learned concepts. An application that implements the proposed model is described and a number of case studies are presented

Stacked Heterogeneous Neural Networks for Time Series Forecasting

A hybrid model for time series forecasting is proposed. It is a stacked neural network, containing one normal multilayer perceptron with bipolar sigmoid activation functions, and the other with an exponential activation function in the output layer. As shown by the case studies, the proposed stacked hybrid neural model performs well on a variety of benchmark time series. The combination of weights of the two stack components that leads to optimal performance is also studied

Інтелектуальні медичні суспільні роботи

Any treatment on long or short term duration and/or complexity begin to involve more and more complex hardware and software pieces of equipment. Most of them begin to have various degree of mobility. Although the medical staff has enough trouble in handling them some times. In this paper we propose a complex robot society to deserve a medical center. The evolution of human computer interface and of the complex expert systems with medical application drive us to idea that a dedicated medical society of intelligent agents can be created.Будь-яке лікування на довгий або короткий термін тривалості та / або складність починають залучати все більше і більше складних апаратних і програмних одиниць обладнання. Більшість з них починають мати різну ступінь мобільності. Хоча медичний персонал має достатньо проблем у поводженні з ними кілька разів. У цій роботі ми пропонуємо складне суспільство роботів, щоб заслужити медичний центр. Еволюція людського інтерфейсу комп'ютера і складних систем експерт медичного застосування вести нас до думки, що виділених медичним суспільством інтелектуальних агентів може бути створений

Agent-Based Framework with Optimized Knowledge Queries

In this paper a technique for optimizing the way in which an intelligent agent makes its own representation about the knowledge that resides onto servers is presented, in order to acquire a significant decrease in the number of inter-agent messages