Une approche multi-agent pour la segmentation d'images de profondeur

National audienceDans cet article, nous présentons et nous évaluons une approche multi-agent pour la segmentation d’images de profondeur. L’approche consiste en l’utilisation d’une population d’agents autonomes pour la segmentation d’une image de profondeur en ses différentes régions planes. Les agents s’adaptent aux régions sur lesquelles ils se déplacent, puis effectuent des actions coopératives et compétitives produisant une segmentation collective de l’image. Un champ de potentiel artificiel est introduit afin de coordonner les mouvements des agents et de leur permettre de s’organiser autour des pixels d’intérêt. Les résultats expérimentaux obtenus par des images réelles montrent le potentiel de l’approche proposée pour l’analyse des images de profondeurs, et ce vis-à-vis de l’efficacité de segmentation et de la fiabilité des résultats

Rumor Diffusion in an Interests-Based Dynamic Social Network

To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency

Using MAS to detect retinal blood vessels

The segmentation of retinal vasculature by color fundus images analysis is crucial for several medical diagnostic systems, such as the diabetic retinopathy early diagnosis. Several interesting approaches have been done in this field but the obtained results need to be improved. We propose therefore a new approach based on an organization of agents. This multi-agent approach is preceded by a preprocessing phase in which the fundamental filter is an improved version of the Kirsch derivative. This first phase allows the construction of an environment where the agents are situated and interact. Then, edges detection emerged from agents’ interaction. With this study, competitive results as compared with those present in the literature were achieved and it seems that a very efficient system for the diabetic retinopathy diagnosis could be built using MAS mechanisms.Fundação para a Ciência e a Tecnologia (FCT

Combining Exception Handling and Replication for Improving the Reliability of Agent Software

Abstract. Exception handling and replication are two complementary mechanisms that increase software reliability. Exception handling helps programmers in controlling situations in which the normal execution flow of a program cannot continue. Replication handles system failures through redundancy. Combining both techniques is a first step towards building a trustworthy software engineering framework. This paper presents some of the results from the Facoma project. It proposes the specification of an exception handling system for replicated agents as an adaptation of the Sage proposal. It then describes its implementation in the Dimax replicated agent environment

Engineering Multi-Agent Systems: State of Affairs and the Road Ahead

The continuous integration of software-intensive systems together with the ever-increasing computing power offer a breeding ground for intelligent agents and multi-agent systems (MAS) more than ever before. Over the past two decades, a wide variety of languages, models, techniques and methodologies have been proposed to engineer agents and MAS. Despite this substantial body of knowledge and expertise, the systematic engineering of large-scale and open MAS still poses many challenges. Researchers and engineers still face fundamental questions regarding theories, architectures, languages, processes, and platforms for designing, implementing, running, maintaining, and evolving MAS. This paper reports on the results of the 6th International Workshop on Engineering Multi-Agent Systems (EMAS 2018, 14th-15th of July, 2018, Stockholm, Sweden), where participants discussed the issues above focusing on the state of affairs and the road ahead for researchers and engineers in this area