Knowledge Discovery in Database: Induction Graph and Cellular Automaton

In this article we present the general architecture of a cellular machine, which makes it possible to reduce the size of induction graphs, and to optimize automatically the generation of symbolic rules. Our objective is to propose a tool for detecting and eliminating non relevant variables from the database. The goal, after acquisition by machine learning from a set of data, is to reduce the complexity of storage, thus to decrease the computing time. The objective of this work is to experiment a cellular machine for systems of inference containing rules. Our system relies upon the graphs generated by the SIPINA method. After an introduction aiming at positioning our contribution within the area of machine learning, we briefly present the SIPINA method for automatic retrieval of knowledge starting from data. We then describe our cellular system and the phase of knowledge post-processing, in particular the validation and the use of extracted knowledge. The presentation of our system is mostly done through an example taken from medical diagnosis

A Making-Decision System for an Urban Transportation Network

This paper deals with the real time regulation of traffic within a disturbed transportation system. We show the necessity of a decision support system that detects, analyzes and resolves the unpredicted disturbances. Due to the disturbed aspect of transportation system, we present a multi-agent approach for the regulation process. This approach includes an anytime algorithm, which permits to access to solutions in real time. The quality of the results increases with allocated time. Our system is able to foresee all behaviors according to the environment with which it interacts. These aims offer real guarantees with respect to the temporal deadlines. The main objective is not to search an optimal solution for a disturbance, but to define a set of possible solutions. Key Word: decision-making, anytime algorithm, network transportation, disturbed urban transportation network

Decision making system for regulation of a bimodal urban transportation network, associating a classical and a multi-agent approaches

International audienceTo offer high quality services, when users are increasingly demanding and competition more and more hard, is now a major problem that transportation companies are faced with. So, ensuring a regular traffic needs to identify the randomly occurring disturbances that affect the transportation system and to eliminate or reduce their impacts on the traffic.This paper presents a decision support system TRSS (Traffic Regulation Support System). TRSS is a supervision environment for the regulation of urban transportation system. TRSS (tram and bus) is based on the regulation operator decision-making process. It provides the operator with the information he needs to identify disturbances and evaluate potential corrective actions to be carried out, according to the regulation strategy he has selected.The first part of the paper presents the decision model we work with. The second part deals with the functional model used in the decision support system. Decision support system for transportation and characteristics of a DSS for a transportation system are described in the third part. In the fourth part, we present the components of the decision-making TRSS supervision tool. In the fifth part, we present the criteria of evaluation and the sixth part is devoted to the presentation of the results

MULTI-STAGE OPTIMIZATION IN SUPPLY CHAIN: AN INDUSTRIAL CASE STUDY

International audienc