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

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

Adaptive Agents and Multi-Agent Systems, IEEE Computer Society

International audienceNew information systems and recent applications (grid computing, Web Services, and so on) are often distributed, large-scale, open, heterogeneous, and characterized by a dynamic environment. To model these complex systems, researchers have spent much effort during the last few years on multiagent systems. The aim is to model complex distributed systems as a set of (possibly organized) software agents that interact in a common environment. The decomposition of a system into a number of agents lets the system react and adapt better in a changing environment. Moreover, organized structures ("social" structures) can emerge from interactions between agents, which in turn constrain and coordinate the agents' behavior. A multiagent system takes its metaphors of interaction from social systems rather than using the metaphor of the isolated thinker that early artificial intelligence researchers preferred. An important issue when dealing with this increasing complexity is to build adaptive agents and multiagent systems. Agents and multiagent systems must be aware of their own capabilities and of changes to other agents and their environment. To remain effective, agents must be able to adapt their structures and knowledge while they execute