Domain-specific trust for context-aware BDI Agents: preliminary work

Context-aware systems are capable of perceiving the physical environment where they are deployed and adapt their behavior accordingly. Multiagent systems based on the BDI architecture can be used to process contextual information in the form of beliefs. Contextual information can be divided and structured in the form of information domains. Information and experience sharing enables a single agent to receive data on different information domains from another agent. In this scenario, establishing a trust model between agents can take into account the relative perceptions each agent has of the others, as well as different trust degrees for different information domains. The objective of this work is to adapt an epistemic model to be used by agents with their belief revision in order to establish a mechanism of domain-specific relative trust attribution. Such mechanism will allow for each agent to possess different trust degrees associated with other agents regarding different information domains.info:eu-repo/semantics/publishedVersio

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

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Smart Mobility. Une approche utilisant la planification et la coordination multi-agents

Dans cette thèse, nous proposons une approche auto-adaptative pour construire un système intelligent de gestion des feux de circulation à utiliser dans les intersections. Cette approche s'appuie sur l'architecture des systèmes multi-agents (MAS) et le paradigme de l'agent BDI pour modéliser, concevoir et mettre en œuvre un SMA pour le contrôle du trafic capable de prendre en charge un mécanisme de régulation distribué et collaboratif tout en tenant compte des changements dynamiques du flux de trafic. Nos recherches concernent les domaines des systèmes sensibles au contexte, des agents intelligents, des systèmes multi-agents, de la planification collaborative entre agents, des architectures distribuées, du raisonnement contextuel et de l'intelligence ambiante. Nos contributions incluent un modèle général pour l'augmentation contextuelle des agents intelligents, un mécanisme de planification collaborative pour les agents contextuels et un cadre pour fournir la planification des agents en tant que service dans des architectures distribuées et faiblement couplées. Ce travail, lorsqu'il est combiné, aboutit à un agent collaboratif apte à être utilisé dans des scénarios impliquant la gestion et le contrôle des feux de circulation. Nos expériences cumulées, ainsi que l'évolution de notre architecture d'agents, aboutissent à un SMA qui permet aux agents d'optimiser la réalisation de leurs intentions individuelles et est capable de s'appuyer sur des processus auxiliaires tels que l'apprentissage automatique pour améliorer la qualité globale du système. Notre approche est validée par des expériences localisées, des simulations et des expériences reflétant des scénarios du monde réel.In this thesis, we propose a self-adaptive approach to build a smart traffic light management system to be used in intersections. This approach relies on the multiagent systems (MAS) architecture and the BDI agent paradigm to model, design, and implement a MAS for traffic control able to support a distributed and collaborative regulation mechanism while taking into account dynamic changes in the traffic flow. Our research involves the domains of context-aware systems, intelligent agents, multiagent systems, collaborative planning among agents, distributed architectures, contextual reasoning, and ambient intelligence. Our contributions include a general model for intelligent agents contextual augmentation, a collaborative planning mechanism for contextual agents, and a framework to provide agent planning as a service in distributed, loosely-coupled architectures. This work, when combined, results in a collaborative agent apt to be used in scenarios involving management and control of traffic lights. Our cumulative experiments, together with the evolution of our agent architecture, result in a MAS that (i) allows the agents to optimize the realization of their individual intentions and (ii) is able to rely on auxiliary processes such as machine learning (reinforcement learning) to improve the overall quality of the system. Our complete approach is validated by localized experiments, simulations, and experiments reflecting real-world scenarios

Gestion d'intentions multiples pour agents ambiants coopératifs

International audienceAmbient Intelligent (AmI) environments dynamically provide contextual information to intelligent agents that interact with them. In such environments, could these agents cooperate to improve their goal achievement, considering multiple intentions from several agents? With multiple agents, cooperation will depend on each agent's own intentions. Agents adapt to dynamic changes in the environment using context-aware planning mechanisms such as the Contextual Planning System (CPS), which proposes an optimal plan for a single agent based on the current context. In this paper we present the Collective CPS (CCPS), an opportunistic cooperative planning mechanism for multiple agents in AmI environments. CCPS allows agents to partially delegate their own plans or to collaborate with other agents' plans during their execution, while retaining individual planning capabilities. A working scenario is shown for a realistic AmI environment, such as a smart Campus.L'environnement dynamique des systèmes ambiants offre des informations contextuelles aux agents intelligents qui s'y déploient. Dans de tels environnements, ces agents peuvent-ils collaborer pour mieux atteindre leurs objectifs individuels et collectifs, et ce en considérant leurs intentions multiples ? Cette coopération dépendra fortement des intentions des agents. Dans cet article, nous proposons de doter les agents ambiants d'un mécanisme de planification contextuelle appelé CPS qui peut s'étendre dans un contexte collectif. Nous présentons d'abord le CPS qui génère des plans contextuels optimaux pour un seul agent tout en satisfaisant plusieurs de ses intentions et en préservant la consistance du plan. Ensuite, nous étendons ce mécanisme coopératif de planification pour prendre en considération plusieurs agents ambiants. Appelé CCPS (collective CPS), il permet aux agents de déléguer partiellement leur plan et de collaborer durant l'exécution de leurs plans. Un scenario de travail extrait du Campus Intelligent est implémenté et discuté