Reallocation Problems in Agent Societies: A Local Mechanism to Maximize Social Welfare

Resource reallocation problems are common in real life and therefore gain an increasing interest in Computer Science and Economics. Such problems consider agents living in a society and negotiating their resources with each other in order to improve the welfare of the population. In many studies however, the unrealistic context considered, where agents have a flawless knowledge and unlimited interaction abilities, impedes the application of these techniques in real life problematics. In this paper, we study how agents should behave in order to maximize the welfare of the society. We propose a multi-agent method based on autonomous agents endowed with a local knowledge and local interactions. Our approach features a more realistic environment based on social networks, inside which we provide the behavior for the agents and the negotiation settings required for them to lead the negotiation processes towards socially optimal allocations. We prove that bilateral transactions of restricted cardinality are sufficient in practice to converge towards an optimal solution for different social objectives. An experimental study supports our claims and highlights the impact of a realistic environment on the efficiency of the techniques utilized.Resource Allocation, Negotiation, Social Welfare, Agent Society, Behavior, Emergence

Optimized Hidden Markov Model based on Constrained Particle Swarm Optimization

International audienceAs one of Bayesian analysis tools, Hidden Markov Model (HMM) has been used to in extensive applications. Most HMMs are solved by Baum-Welch algorithm (BWHMM) to predict the model parameters, which is difficult to find global optimal solutions. This paper proposes an optimized Hidden Markov Model with Particle Swarm Optimization (PSO) algorithm and so is called PSOHMM. In order to overcome the statistical constraints in HMM, the paper develops re-normalization and re-mapping mechanisms to ensure the constraints in HMM. The experiments have shown that PSOHMM can search better solution than BWHMM, and has faster convergence speed

The Reputation Evaluation Based on Optimized Hidden Markov Model in E-Commerce

Nowadays, a large number of reputation systems have been deployed in practical applications or investigated in the literature to protect buyers from deception and malicious behaviors in online transactions. As an efficient Bayesian analysis tool, Hidden Markov Model (HMM) has been used into e-commerce to describe the dynamic behavior of sellers. Traditional solutions adopt Baum-Welch algorithm to train model parameters which is unstable due to its inability to find a globally optimal solution. Consequently, this paper presents a reputation evaluation mechanism based on the optimized Hidden Markov Model, which is called PSOHMM. The algorithm takes full advantage of the search mechanism in Particle Swarm Optimization (PSO) algorithm to strengthen the learning ability of HMM and PSO has been modified to guarantee interval and normalization constraints in HMM. Furthermore, a simplified reputation evaluation framework based on HMM is developed and applied to analyze the specific behaviors of sellers. The simulation experiments demonstrate that the proposed PSOHMM has better performance to search optimal model parameters than BWHMM, has faster convergence speed, and is more stable than BWHMM. Compared with Average and Beta reputation evaluation mechanism, PSOHMM can reflect the behavior changes of sellers more quickly in e-commerce systems

Dealing with the right of way in advanced traffic simulation. A characterization of drivers’ behaviours in a multi-agent approach

With the emergence of ADAS and autonomous vehicle, the need of simulation software to test advanced systems and models is unavoidable. The objective of this work is to characterize, in a multi-agent approach, the traffic vehicles behaviours, and to model them in a versatile traffic simulator, serving as a testing tool for integration in the traffic module of the SCANeR StudioTM simulation software. The algorithm will bring more natural interactions between vehicles in simulation and allow the emergence of new relevant situations for autonomous vehicles, observable in real, such as collision risk situations or even accidents, which have for now to be scripted in scenarios and do not occur naturally

Une Approche Centrée Individu de l’Allocation de Ressources Distribuée

Les problèmes d'allocation de ressources suscitent un intérêt croissant aussi bien en Économie qu'en Informatique. Ordinairement, ils sont résolus par des techniques centralisées, dans lesquelles une entité omnisciente détermine comment allouer les ressources de manière optimale. Cependant, ces approches font des hypothèses qui ne sont pas toujours réaliste. Or, il n'est souvent pas possible d'avoir une entité omnisciente. Certaines applications sont dynamiques et nécessitent une méthode de résolution adaptative qui puisse prendre en compte de nouvelles informations durant la résolution. Ces approches considèrent toujours que les communications entre les participants ne sont pas restreintes, ce qui n'est évidemment pas le cas dans la plupart des cas, comme dans les réseaux pair-à-pair par exemple où un pair ne peut communiquer qu'à un ensemble restreint du système.Dans cette thèse, nous nous focalisons sur les méthodes de ré-allocation distribuées, basée sur des systèmes multi-agents, qui transforment une allocation initiale par des séquences de transactions locales entre agents. Nous cherchons à concevoir des comportements d'agents menant un processus de négociation à une allocation socialement optimale. Cette allocation peut alors être vue comme un phénomène émergent. Nous voulons également identifier les paramètres favorisant l'efficacité des négociations ainsi que ceux qui la restreignent. Nous considérons différentes mesures de bien-être social et nous fournissons les comportements à implémenter pour négocier efficacement dans chaque cas. Nous proposons une méthode adaptative et ``anytime'' où n'importe quel type de réseau d'accointances peut être considéré.Resource allocation problems have been widely studied according to various scenarios in the literature. They are usually solved by means of centralized techniques, where an omniscient entity determines how to optimally allocate resources. However, these solving methods are not well-adapted for applications where privacy is required. Moreover, several assumptions made are not always plausible, which may prevent their use in practice, especially in the context of agent societies. For instance, dynamic applications require adaptive solving processes, which can handle the evolution of initial data. Such techniques never consider restricted communication possibilities whereas many applications are based on. For instance, in peer-to-peer networks, a peer can only communicate to a small subset of the systems.In this thesis, we focus on distributed methods to solve resource allocation problems. Initial allocations evolves step by step thanks to local agent negotiations. We seek to provide agent behaviors leading negotiation processes to socially optimal allocations. In this work, resulting resource allocations can be viewed as emergent phenomena. We also identify parameters favoring the negotiation efficiency. We provide the agent behavior to implement when four different social welfare notions are considered. The original method proposed in thesis is adaptive, anytime and can handle any restriction on agent communication possibilities

Efficient Agent Negotiations in a Realistic Context

International audienceNo abstrac

Egalitarian Negotiations in Agent Societies

International audienceNo abstrac

Modélisation multiniveau du bien-être social dans un SMA: Application aux problèmes d'affectation et d'appariement

International audienceMultiagent Systems (MAS) allow for empirical comparisons between social welfare metrics, but with a preservation of the privacity of invidual preferences, leading to solving protocols for assignment or matching problems. The recent multi-level MAS offer an explicit representation of intermediate viewpoints between the individual and the collective levels. We propose a multi-level welfare model to define relevant welfare metrics for each agent group. Not only matching and assignment problems are handled through the same formalism, but subtle variations can also be addressed. Finally, we outline the general principles for distributed solvers within this modeling.Les Systèmes Multi-Agents (SMA) permettent de définir et de comparer empiriquement des mesures de bien-être social agrégeant des préférences individuelles, tout en protégeant leur caractère privé. Ces travaux ont permis l'élaboration de protocoles de résolution pour les problèmes d'affectation de ressources ou d'appariement. Or, l'émergence récente de SMA multiniveaux offre l'opportunité d'aller plus avant dans cette démarche, en représentant explicitement des points de vue intermédiaires entre l'individu et le collectif. Nous proposons ici une modélisation permettant le choix de métriques pertinentes pour le bien-être de chaque groupe d'agents. Elle permet d'exprimer dans un formalisme homogène des problèmes d'affectation ou d'appariement variés, mais aussi de traiter de même des variantes difficilement formalisables de façon classique. Enfin, nous esquissons des principes généraux pour la construction de solveurs distribués pour ce type de modélisation