Multi-Agents Systems and Territory: Concepts, Methods and Applications

This paper analyses the multi-agents systems that are now considered the best tool to simulate and study real world. We review the main characteristics of a multi-agents system, namely interactions and cooperations of agents, communications and behaviours between them and finally the schedule of actions and jobs assignment to agents. The multi-agents system approach is increasingly applied in social and economic sciences; so we study mainly the territorial applications. In these applications new characteristics arise from the consideration of territory (land and space where the agents live or territory as an agent in itself, that evolves in the time). We study possible new applications of multi-agents applied to the territory (for instance, to define town planning policies or to locate dangerous facilities). Furthermore we study new tools to make operational multi-agents systems (mainly Swarm, the toolkit of Santa Fe Institute). With Swarm we present two kind of territorial applications: with located agents (fixed in space) and with not located agents (moving in the space). Finally we show the results of these applications.

An Electronic Market Space Architecture Based On Intelligent Agents And Data Mining Technologies

This paper presents an overview of current trends in electronic Business (E-Business), and discusses how an enterprise can use the Electronic Market space based on intelligent agents and data mining techniques to its strategic advantage. We define an agency as a multi-agent system created by integrating agents, selected from a library of reusable agents that have formed a federation. A federation of agents comprises of a set of registered agents, witch are themselves complete knowledge-based system [1].multi-agent system, e-business, data mining, artificial neural networks

Multi-Agent Motion Planning and Object Transportation under High Level Goals

This paper presents a hybrid control framework for the motion planning of a multi-agent system including N robotic agents and M objects, under high level goals. In particular, we design control protocols that allow the transition of the agents as well as the transportation of the objects by the agents, among predefined regions of interest in the workspace. This allows us to abstract the coupled behavior of the agents and the objects as a finite transition system and to design a high-level multi-agent plan that satisfies the agents' and the objects' specifications, given as temporal logic formulas. Simulation results verify the proposed framework.Comment: To appear in the World Congress of the International Federation of Automatic Control (IFAC), Toulouse, France, July 201

Improving the Scalability of Multi-Agent Systems

There is an increasing demand for designers and developers to construct ever larger multi-agent systems. Such systems will be composed of hundreds or even thousands of autonomous agents. Moreover, in open and dynamic environments, the number of agents in the system at any one time will fluctuate significantly. To cope with these twin issues of scalability and variable numbers, we hypothesize that multi-agent systems need to be both /self-building/ (able to determine the most appropriate organizational structure for the system by themselves at run-time) and /adaptive/ (able to change this structure as their environment changes). To evaluate this hypothesis we have implemented such a multi-agent system and have applied it to the domain of automated trading. Preliminary results supporting the first part of this hypothesis are presented: adaption and self-organization do indeed make the system better able to cope with large numbers of agents

Education in Accounting using an Interactive System

This paper represents a summary of a research report and the results of developing an educational software, including a multi-agent system for teaching accounting bases and financial accounting. The paper describes the structure of the multi-agent system, defined as a complex network of s-agents. Each s-agent contains 6 pedagogical agents and a coordinator agent. We have defined a new architecture (BeSGOTE) that extends the BDI architecture for intelligent agents and we have defined a mixing-up relation among the accounts, presenting the way in which it can be used for testing students.Computer Aided Education, Multi-Agent System, Artificial Intelligence, Accounting Education