Relevant bibliographies by topics / Interaction multi-agents

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Interaction multi-agents'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Interaction multi-agents"

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Sherstyugina, Anastasiya, and Roman Nesterov. "Discovering Process Models from Event Logs of Multi-Agent Systems Using Event Relations." Proceedings of the Institute for System Programming of the RAS 35, no. 3 (2023): 11–32. http://dx.doi.org/10.15514/ispras-2023-35(3)-1.

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The structure of a process model directly discovered from an event log of a multi-agent system often does not reflect the behavior of individual agents and their interactions. We suggest analyzing the relations between events in an event log to localize actions executed by different agents and involved in their asynchronous interaction. Then, a process model of a multi-agent system is composed from individual agent models between which we add channels to model the asynchronous message exchange. We consider agent interaction within the acyclic and cyclic behavior of different agents. We develop an algorithm that supports the analysis of event relations between different interacting agents and study its correctness. Experimental results demonstrate the overall improvement in the quality of process models discovered by the proposed approach in comparison to monolithic models discovered directly from event logs of multiagent systems.
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Liu, Yong, Weixun Wang, Yujing Hu, Jianye Hao, Xingguo Chen, and Yang Gao. "Multi-Agent Game Abstraction via Graph Attention Neural Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7211–18. http://dx.doi.org/10.1609/aaai.v34i05.6211.

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In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.
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Bucher, Andreas, Mateusz Dolata, Sven Eckhardt, Dario Staehelin, and Gerhard Schwabe. "Talking to Multi-Party Conversational Agents in Advisory Services: Command-based vs. Conversational Interactions." Proceedings of the ACM on Human-Computer Interaction 8, GROUP (February 16, 2024): 1–25. http://dx.doi.org/10.1145/3633072.

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Interacting with a conversational agent (CA) is becoming a major paradigm for human-technology interaction. Yet, ways for interacting with CAs are still forming, especially in situations involving more than one human. Starting an interaction with a CA might involve a wakeword and command. Alternatively, it could become active based on implicit requests and context information. Hence, CA designers face a serious dilemma: explicit commands disturb a natural conversation flow, while implicit requests might cause inadequate CA behavior. This study explores this dilemma and discusses observations from a project featuring a CA for financial advisory services. Advisors initially envisioned a CA that ''blends with the background'' and acts on context information. However, when engaging with a CA, they used conversational interactions in one part of the encounter and command-based interactions in another. We discuss this observation and contrast it against previous literature. This insight has implications for design and research.
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Li, Guangyu, Bo Jiang, Hao Zhu, Zhengping Che, and Yan Liu. "Generative Attention Networks for Multi-Agent Behavioral Modeling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 7195–202. http://dx.doi.org/10.1609/aaai.v34i05.6209.

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Understanding and modeling behavior of multi-agent systems is a central step for artificial intelligence. Here we present a deep generative model which captures behavior generating process of multi-agent systems, supports accurate predictions and inference, infers how agents interact in a complex system, as well as identifies agent groups and interaction types. Built upon advances in deep generative models and a novel attention mechanism, our model can learn interactions in highly heterogeneous systems with linear complexity in the number of agents. We apply this model to three multi-agent systems in different domains and evaluate performance on a diverse set of tasks including behavior prediction, interaction analysis and system identification. Experimental results demonstrate its ability to model multi-agent systems, yielding improved performance over competitive baselines. We also show the model can successfully identify agent groups and interaction types in these systems. Our model offers new opportunities to predict complex multi-agent behaviors and takes a step forward in understanding interactions in multi-agent systems.
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de Hauwere, Yann-Michaël, Sam Devlin, Daniel Kudenko, and Ann Nowé. "Context-sensitive reward shaping for sparse interaction multi-agent systems." Knowledge Engineering Review 31, no. 1 (January 2016): 59–76. http://dx.doi.org/10.1017/s0269888915000193.

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AbstractPotential-based reward shaping is a commonly used approach in reinforcement learning to direct exploration based on prior knowledge. Both in single and multi-agent settings this technique speeds up learning without losing any theoretical convergence guarantees. However, if speed ups through reward shaping are to be achieved in multi-agent environments, a different shaping signal should be used for each context in which agents have a different subgoal or when agents are involved in a different interaction situation.This paper describes the use of context-aware potential functions in a multi-agent system in which the interactions between agents are sparse. This means that, unknown to the agentsa priori, the interactions between the agents only occur sporadically in certain regions of the state space. During these interactions, agents need to coordinate in order to reach the global optimal solution.We demonstrate how different reward shaping functions can be used on top of Future Coordinating Q-learning (FCQ-learning); an algorithm capable of automatically detecting when agents should take each other into consideration. Using FCQ-learning, coordination problems can even be anticipated before the actual problems occur, allowing the problems to be solved timely. We evaluate our approach on a range of gridworld problems, as well as a simulation of air traffic control.
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Emelyanov, Viktor V. "Organization of the Agents Interaction in Multi-Agents of Production Coordination System." IFAC Proceedings Volumes 33, no. 17 (July 2000): 485–89. http://dx.doi.org/10.1016/s1474-6670(17)39450-8.

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Dushkin, Roman. "Multi-agent systems for cooperative ITS." Тренды и управление, no. 1 (January 2021): 42–50. http://dx.doi.org/10.7256/2454-0730.2021.1.34169.

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This article presents an original perspective upon the problem of creating intelligent transport systems in the conditions of using highly automated vehicles that freely move on the urban street-road networks. The author explores the issues of organizing a multi-agent system from such vehicles for solving the higher level tasks rather than by an individual agent (in this case – by a vehicle). Attention is also given to different types of interaction between the vehicles or vehicles and other agents. The examples of new tasks, in which the arrangement of such interaction would play a crucial role, are described. The scientific novelty is based on the application of particular methods and technologies of the multi-agent systems theory from the field of artificial intelligence to the creation of intelligent transport systems and organizing free-flow movement of highly automated vehicles. It is demonstrated the multi-agent systems are able to solve more complex tasks than separate agents or a group of non-interacting agents. This allows obtaining the emergent effects of the so-called swarm intelligence of the multiple interacting agents. This article may be valuable to everyone interested in the future of the transport sector.
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ZHANG, Kun, Yoichiro MAEDA, and Yasutake TAKAHASHI. "Learning Model Considering the Interaction among Heterogeneous Multi-Agents." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 24, no. 5 (2012): 1002–11. http://dx.doi.org/10.3156/jsoft.24.1002.

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Zhang, Kun, Yoichiro Maeda, and Yasutake Takahashi. "Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 7 (September 20, 2011): 896–903. http://dx.doi.org/10.20965/jaciii.2011.p0896.

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Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward redistribution among agents to reinforce group behavior, and we propose a method of constructing a multi-agent system with an autonomous group creation ability. This is able to strengthen the cooperative behavior of the group as social agents.
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Penner, Robin R. "Multi-Agent Societies for Collaborative Interaction." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 40, no. 15 (October 1996): 762–66. http://dx.doi.org/10.1177/154193129604001503.

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The application of a multi-agent architecture to the design and operation of automated process management systems is proving to be a fruitful method of facilitating human-system collaboration. The agent architecture we are developing is intended to be applied in environments where humans and automated systems jointly perform information intensive tasks, and is based on an organization of multiple agents, where both human and software agents are integrated members in groups akin to human societies. Important features of our architecture include an organization based on social structures, a user interface model based on a collaborative interaction metaphors, and a situated action paradigm for agent behavior.
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Dissertations / Theses on the topic "Interaction multi-agents"

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Kumar, Rohit. "Socially Capable Conversational Agents for Multi-Party Interactive Situations." Research Showcase @ CMU, 2011. http://repository.cmu.edu/dissertations/162.

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Since the inception of AI research, great strides have been made towards achieving the goal of extending natural language conversation as a medium of interaction with machines. Today, we find many Conversational Agents (CAs) situated in various aspects of our everyday life such as information access, education and entertainment. However, most of the existing work on CAs has focused on agents that support only one user in each interactive session. On the other hand, people organize themselves in groups such as teams of co-workers, family and networks of friends. With the mass-adoption of Internet based communication technologies for group interaction, there is an unprecedented opportunity for CAs to support interactive situations involving multiple human participants. Support provided by these CAs can make the functioning of some of these groups more efficient, enjoyable and rewarding to the participants. Through our work on supporting various Multi-Party Interactive Situations (MPIS), we have identified two problems that must be addressed in order to embed effective CAs in such situations. The first problem highlights the technical challenges involving the development of CAs in MPIS. Existing approaches for modeling agent behavior make assumptions that break down in multi-party interaction. As a step towards addressing this problem, this thesis contributes the Basilica software architecture that uses an event-driven approach to model conversation as an orchestration of triggering of conversational behaviors. This architecture alleviates the technical problems by providing a rich representational capability and the flexibility to address complex interaction dynamics. The second problem involves the choice of appropriate agent behaviors. In MPIS, agents must compete with human participants for attention in order to effectively deliver support and interventions. In this work, we follow a model of human group interaction developed by empirical research in small group communication. This model identifies two fundamental processes in human group interaction, i.e., Instrumental (Task-related) and Expressive (Social-Emotional). Behaviors that constitute this expressive process hold the key to managing and regulating user attention and serve other social functions in group interaction. This thesis describes two socially capable conversational agents that support users in collaborative learning and group decision making activities. Their social capabilities are composed of a set of behaviors based on the Social-Emotional interaction categories identified by work in small group communication. These agents demonstrate the generalizability of our methodology for designing and implementing social capabilities across two very different interactive situations. In addition to the implementation of these agents, the thesis presents a series of experiments and analysis conducted to investigate the effectiveness of these social capabilities. First and foremost, these experiments show significant benefits of the use of socially capable agents on task success and agent perception across the two different interactive situations listed above. Second, they investigate issues related to the appropriate use of these social capabilities specifically in terms of the amount and timing of the constituent social behaviors. Finally, these experiments provide an understanding of the underlying mechanism that explains the effects that social capabilities can achieve.
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Riberio, Alexandre Moretto. "Un modèle d'interaction dynamique pour les systèmes multi-agents." Université Joseph Fourier (Grenoble), 2000. http://www.theses.fr/2000GRE10035.

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Cette thèse traite le problème de l'interaction dans les Systèmes Multi-Agents. L'approche choisie consiste à analyser l'interaction comme un composant à part entière dans la conception d'un système. Nous présentons un modèle d'interaction où le mécanisme d'interaction joue un rôle dynamique, c'est-à-dire qui une partie de la gestion de l'interaction est déplacée de l'agent vers le mécanisme d'interaction. Le transfert se fait sous la forme d'une délégation des tâches, concernant l'interaction. La répartition de l'interaction permet le développement des interactions indépendamment des agents, ce qui rend possible l'évolution des interactions sans avoir besoin de modifier les agents (on peut alors augmenter les possibilités d'interaction d'un système sans pour autant augmenter la complexité des agents eux-mêmes). Cette répartition, par la modularisation qu'elle induit, aide aussi à la gestion de la complexité de tels systèmes. C'est à travers le remplacement des messages par des Messages Actifs que le modèle d'interaction devient Dynamique. Les messages actifs jouent un rôle central dans l'interaction, ils reçoivent la responsabilité de mener à terme une interaction. Pour évaluer notre modèle d'interaction, nous montrons qu'il contribue à la résolution d'un certain nombre de problèmes qui restent ouverts, comme l'interaction entre des agents hétérogènes. Il est aussi possible d'observer qu'il présente quelques avantages par rapport aux approches existantes. Par exemple, la prise en compte du suivi des protocoles par le mécanisme d'interaction, les donne un autre statut, puisqu'ils sont définis à l'extérieur des agents et sont détachés de leurs comportements. Nous avons testé et évalué le Modèle d'Interaction Dynamique à travers deux applications : un prototype de système de prise de rendez-vous et «Baghera», un environnement informatique pour l'apprentissage et l'enseignement à distance de la démonstration en géométrie
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Pin, Paolo <1974&gt. "Four multi-agents economic models: from evolutionary competition to social interaction." Doctoral thesis, Università Ca' Foscari Venezia, 2007. http://hdl.handle.net/10579/396.

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Dinu, Razvan. "Web Agents : towards online hybrid multi-agent systems." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20126/document.

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Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web
Multi-agent systems have been used in a wide range of applications from computer-based simulations and mobile robots to agent-oriented programming and intelligent systems in real environments. However, the largest environment in which software agents can interact is, without any doubt, the World Wide Web and ever since its birth agents have been used in various applications such as search engines, e-commerce, and most recently the semantic web. However, agents have yet to be used on the Web in a way that leverages the full power of artificial intelligence and multi-agent systems, which have the potential of making life much easier for humans. This thesis investigates how this can be changed, and how agents can be brought to the core of the online experience in the sense that we want people to talk and interact with agents instead of "just using yet another application or website". We analyze what makes it hard to develop intelligent agents on the web and we propose a web agent model (WAM) inspired by recent results in multi-agent systems. Nowadays, a simple conceptual model is the key for widespread adoption of new technologies and this is why we have chosen the MASQ meta-model as the basis for our approach, which provides the best compromise in terms of simplicity of concepts, generality and applicability to the web. Since until now the model was introduced only in an informal way, we also provide a clear formalization of the MASQ meta-model.Next, we identify the three main challenges that need to be addressed when building web agents: integration of bodies, web semantics and user friendliness. We focus our attention on the first two and we propose a set of principles to guide the development of what we call strong web agents. Finally, we validate our proposal through the implementation of an award winning platform called Kleenk. Our work is just a step towards fulfilling the vision of having intelligent web agents mediate the interaction with the increasingly complex World Wide Web
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Pauchet, Alexandre. "Modélisation cognitive d'interactions humaines dans un cadre de planification multi-agents." Paris 13, 2006. http://www.theses.fr/2006PA132017.

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Cette thèse s'appuie sur une expérimentation psychologique ayant permis de concevoir des modèles cognitifs de la planification humaine et de l'interaction humaine. Ces deux modèles sont intégrés de façon homogène à une nouvelle architecture d'agent appelée BDIggy, née d'une fusion entre le système Iggy (un système pré-existant de planification humaine) et une architecture BDI étendue à la résolution coope��rative de deux problèmes. Le modèle de l'interaction humaine introduit dans BDIggy est sur deux niveaux : un modèle de l'énoncé et un modèle du discours liés par une sémantique des performatives. Dans BDIggy, l'interaction, la planification et les connaissances s'entrelacent grâce aux concepts BDI. L'architecture BDIggy, qui permet de simuler la résolution du problème, est validée par un test "à la Turing".
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Ouadou, Kamel Eddine. "Amf : Un modèle d'architecture multi-Agents Multi-Facettes pour interfaces homme-machine et les outils associés." Ecully, Ecole centrale de Lyon, 1994. http://www.theses.fr/1994ECDL0038.

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Un modele d'architecture pour interfaces homme-machine propose une structuration du logiciel interactif (li) avec comme objectif de faciliter l'elaboration de l'interface homme-machine de qualite, d'automatiser cette elaboration et prendre naturellement en compte des standards. Plusieurs modeles ont ete proposes, mais aucun d'entre eux n'est encore arrive a satisfaire l'ensemble des objectifs identifies. Nous avons etudie, dans le cadre de cette these, les principaux aspects qui interviennent dans l'elaboration d'un logiciel interactif et qui conditionnent son architecture. (des styles et modeles d'interaction jusqu'aux outils de developpement). Les modeles d'architecture peuvent etre separes en deux grandes categories : les modeles centralises et les modeles multi-agents ou distribues. Nous proposons le modele multi-agents amf (agent multi-facettes) dont l'objectif est de fournir au concepteur une structure logicielle : qui accorde la meme importance a tous les aspects du dialogue homme-machine (l'interaction, les presentations multiples de l'information, la gestion des contraintes, l'assistance a l'utilisateur, la gestion des erreurs et l'observation des comportements) ; qui apporte une decomposition plus fine des agents interactifs ; qui definit des mecanismes de communication entre les composants du logiciel interactif ; qui facilite l'elaboration des li par l'automatisation au moins partielle du processus de developpement et la prise en compte des standards ; et qui facilite la construction des outils de developpement des li. En se basant sur le modele amf, nous proposons en complement une infrastructure logicielle d'un environnement de construction et de prototypage d'interfaces multi-agents amf. L'objectif de cette infrastructure est de constituer une reference pour la construction d'environnements specialises dans differents domaines d'application. L'infrastructure proposee integre un ensemble d'outils permettant l'elaboration, l'execution et l'evaluation des interfaces amf. Comme application de nos propositions, nous avons concu et realise e4 (environnement d'etudes ergonomiques d'ecrans), un environnement specialise dans l'elaboration et les mesures ergonomiques d'interfaces utilisateur (conducteur) dans le poste de conduite automobile. Les logiciels interactifs integres dans le poste de conduite s'appuient sur le modele amf
A user interface architecture model provides a structure for interactive software in order to ease the design of friendly user interfaces, their automatic production and to take in to account standard components. These objectives haven't been satisfied yet despite the numerous models proposed. In this thesis we have studied the main aspects related to the interactive software production and its architecture. They concern both interaction models, styles and development tools. We distinguish two main classes of architecture models : the centralized ones and the multi-agents (distributed) ones. We propose the multi-agents model called AMF (Agent Multi-Facettes) in order to provide for the developer a software structure which : give the same importance to all aspects of dialog (interaction, multiple presentation, constraints handling, help and errors handling, capturing user behavior) ; bring more detailed decomposition of interactive agents ; define concepts and methods for communication between the interactive software components in order to ease an extension of the agents by new functions or services ; ease the elaboration of interactive software by automatisation ; and ease the elaboration of development tools. Concerning development tools, we consider those specialized environments are ease-to-use by designers and users of the application domain. So, we propose a software framework which constitutes a reference model for specialized environments in different domains. This framework integrates a set of basic and generic tools allowing the elaboration, the execution and the evaluation of the AMF user interface. Its architecture is also based on AMF model and tools integration is made by using a base of specializable AMF agents. Finally in order to apply our propositions, we conceveid and implemented an environment called E4 (in french Environment d'Etudes Ergonomiques d'Ecrans) specialized in development of in-car interfaces
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Marzougui, Borhen. "Contribution à la modélisation et à la vérification des systèmes multi agents." Thesis, Paris, CNAM, 2014. http://www.theses.fr/2014CNAM0918/document.

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Les Réseaux de Petri (RdP) sont actuellement les approches les plus prometteuses pour modéliser et vérifier les systèmes complexes tels que les Systèmes Multi Agents (SMA). De nombreuses solutions ont été proposées pour remédier aux problèmes de communication, de coordination et d’interaction entre les Agents. Cependant, il n’existe aucune en mesure de traiter, à la fois les aspects structurels et comportementaux, du moins à notre connaissance. La thèse s'intéresse à la problématique de modélisation formelle et de vérification automatique et semi-automatique de propriétés pour les Systèmes Multi Agents. Plus précisément, l'objectif consiste à proposer un nouveau modèle formel original basé sur les réseaux de Petri, les Réseaux de Petri à Agents (RdPA), qui permettent d’exprimer de manière consistante et plus précise les systèmes Multi Agents. Il s’intéresse de plus à l’extension de ce modèle aux fins de modéliser la migration des agents dans le cadre des systèmes à agents mobiles. Cette classe de modèle permet de s’intéresser à la vérification formelle de propriétés classiques comme notamment la vivacité ou l’absence d’interblocage dans le cadre des Systèmes Multi-Agent
Petri nets (PN) are currently the most promising approaches to model and to verify complex systems such as Multi Agent Systems (MAS). Several solutions have been proposed to solve the problems of communication, coordination and interaction among Agents. However, to best of our knowledge, none of this solution has able to handle both aspects: structural and behavioral. The thesis focuses on the problem of formal modeling and automatic and semi-automatic verification of properties in Multi Agent Systems. More specifically, the objective is to propose a new original formal model based on Petri nets, Agents Petri nets (APN), which express consistently more accurate a Multi Agent Systems. There is growing interest in the extension of this model for modeling the migration of Agents within the mobile Agent systems. This class of model allows focusing on the formal verification of classical properties such as alertness or absence of deadlock in the context of Multi Agent Systems
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CARVALHO, GUSTAVO ROBICHEZ DE. "G-FRAMEWORKS: AN APPROACH TO PROMOTE THE REUSE OF INTERACTION LAWS IN OPEN MULTI-AGENTS SYSTEMS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2007. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=10169@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
Um dos desafios de desenvolvimento de software é produzir aplicativos que são projetados para evoluir reduzindo esforços de manutenção. Diversas técnicas desenvolvidas para a governança de leis de interação em sistemas multiagentes abertos foram propostas, no entanto a flexibilidade e a reutilização de leis não ocorrem de forma sistemática com estas técnicas. A tecnologia de gframeworks visa orientar o projeto e a implementação de leis de interação em sistemas multiagentes abertos, com o objetivo de produzir mecanismos de governança de leis de interação. A flexibilidade em g- frameworks é obtida através da introdução de incrementos específicos que as instâncias em desenvolvimento requerem, de modo a completar e adaptar as funcionalidades originais do g-framework. A reutilização em g-frameworks vem justamente do re-aproveitamento de um mesmo projeto e código de lei de interação em instâncias geradas a partir do g-framework. Os benefícios obtidos por tal abordagem podem impactar positivamente o desenvolvimento de software em termos do custo e tempo total de construção de uma família de mecanismos de governança de sistemas multiagentes. Para isto, são apresentadas técnicas de governança de sistemas multiagentes abertos e técnicas de reutilização de leis de interação. Um método de orientação é proposto para guiar o desenvolvimento de g-frameworks. Experimentos foram desenvolvidos e são descritos neste documento.
One of the challenges of software development is to produce applications that are designed to evolve, reducing maintenance efforts. Many techniques developed to govern the interaction laws in open multi- agent systems were proposed, but the flexibility and reuse concerns of interaction laws were not systemically fulfilled by them. The technology of g- frameworks intends to guide the design and the implementation of interaction laws in open multi-agent systems, aiming to facilitate the production of interaction law governance mechanisms. The flexibility in g-frameworks is achieved by specific increments that the instances under development require, to complete and adapt the original functionalities of the g-framework. The reuse in g- frameworks is related to a common design and codification of that interaction laws that are shared by instances developed with the g-framework. The benefits of this approach might positively impact the development of software considering the costs and the necessary time to construct the family of governance mechanisms of multiagent systems. In this thesis, some techniques to promote reuse of interaction laws were propose to fulfill this goal. One method to orient the development of g-frameworks is proposed. Experiments were developed and they are described in this thesis.
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Goracci, Laura. "Interaction of surfactants with DNA : a multi-technical approach to the study of DNA transfection agents." Bordeaux 1, 2004. http://www.theses.fr/2004BOR12783.

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Les interactions entre l'ADN et une série de molécules amphiphiles ont étés étudiés en combinant des techniques spectroscopiques anisotropes (absorption fluorescence) et polarisées (dichroi͏̈sme circulaire, dépolarisation de fluorescence). Les résultats obtenus ont été ensuite analysés en utilisant un modele de docking moléculaire et indiquent que l'utilisation de la sonde fluorescente bromure d'éthidium pour étudier les interactions entre ADN et amphiphiles est bien plus complexe que ce postulé actuellement dans la littérature. L'utilisation d'une nouvelle sonde, le composé Hoechst 33258, est proposé.
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Persson, Christian. "Strategies for enhancing consumer interaction in electronic retailing." Doctoral thesis, KTH, Numerical Analysis and Computer Science, NADA, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3267.

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Books on the topic "Interaction multi-agents"

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1960-, Sun Ron, ed. Cognition and multi-agent interaction: From cognitive modeling to social simulation. Cambridge: Cambridge University Press, 2006.

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Khosla, Rajiv. Intelligent Multimedia Multi-Agent Systems: A Human-Centered Approach. Boston, MA: Springer US, 2000.

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Sun, Ron. Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2008.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2009.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2005.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.

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Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2005.

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Intelligent Multimedia Multi-Agent Systems: A Human-Centered Approach (The Springer International Series in Engineering and Computer Science). Springer, 2000.

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Smart, Paul R. Mandevillian Intelligence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198801764.003.0013.

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Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive shortcomings, limitations, and biases play a positive functional role in yielding various forms of collective cognitive success. When this idea is transposed to the epistemological domain, mandevillian intelligence emerges as the idea that individual forms of intellectual vice may, on occasion, support the epistemic performance of some form of multi-agent ensemble, such as a socio-epistemic system, a collective doxastic agent, or an epistemic group agent. As a specific form of collective intelligence, mandevillian intelligence is relevant to a number of debates in social epistemology, especially those that seek to understand how group (or collective) knowledge arises from the interactions between a collection of individual epistemic agents.
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Book chapters on the topic "Interaction multi-agents"

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Hassan, Mohd Fadzil, and Dave Robertson. "Addressing the Brittleness of Agent Interaction." In Intelligent Agents and Multi-Agent Systems, 214–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89674-6_24.

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McGinnis, Jarred, and David Robertson. "Dynamic and Distributed Interaction Protocols." In Adaptive Agents and Multi-Agent Systems II, 167–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32274-0_11.

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Oluyomi, Ayodele, and Leon Sterling. "A Dedicated Approach for Developing Agent Interaction Protocols." In Intelligent Agents and Multi-Agent Systems, 162–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32128-6_13.

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Ribeiro, Richardson, Douglas M. Guisi, Marcelo Teixeira, Eden R. Dosciatti, Andre P. Borges, and Fabrício Enembreck. "Combination of Interaction Models for Multi-Agents Systems." In Enterprise Information Systems, 107–21. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62386-3_5.

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Barbuceanu, Mihai, and Wai-Kau Lo. "Integrating Conversational Interaction and Constraint Based Reasoning in an Agent Building Shell." In Infrastructure for Agents, Multi-Agent Systems, and Scalable Multi-Agent Systems, 144–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-47772-1_13.

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Bakar, Najwa Abu, and Ali Selamat. "Assessing Agents Interaction Quality via Multi-agent Runtime Verification." In Computational Collective Intelligence. Technologies and Applications, 175–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40495-5_18.

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Soic, Renato, Pavle Skocir, and Gordan Jezic. "Agent-Based System for Context-Aware Human-Computer Interaction." In Agents and Multi-Agent Systems: Technologies and Applications 2018, 34–43. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92031-3_4.

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Mathieu, Philippe, and Sébastien Picault. "The Galaxian Project: A 3D Interaction-Based Animation Engine." In Advances on Practical Applications of Agents and Multi-Agent Systems, 312–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38073-0_35.

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Lee, Wonki, and DaeEun Kim. "Local Interaction of Agents for Division of Labor in Multi-agent Systems." In From Animals to Animats 14, 46–54. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43488-9_5.

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Xia, Xinhai, and Lunhui Xu. "Coordination of Urban Intersection Agents Based on Multi-interaction History Learning Method." In Lecture Notes in Computer Science, 383–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13498-2_50.

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Conference papers on the topic "Interaction multi-agents"

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Karatas, Nihan, Shintaro Tamura, Momoko Fushiki, and Michio Okada. "Multi-party Conversation of Driving Agents." In HAI '18: 6th International Conference on Human-Agent Interaction. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3284432.3284466.

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Fang, Shu, Qipeng Liu, and Xiaofan Wang. "Swarming of multi-agents with topological-based random interaction." In 2013 Chinese Automation Congress (CAC). IEEE, 2013. http://dx.doi.org/10.1109/cac.2013.6775795.

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Shi Guodong and Hong Yiguang. "Multi-agent coordination with switching interaction structures and heterogeneous agents." In 2008 Chinese Control Conference (CCC). IEEE, 2008. http://dx.doi.org/10.1109/chicc.2008.4605540.

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Maeedi, Ali, Muhammad Umer Khan, and Bulent Irfanoglu. "Reciprocal Altruism-based Path Planning Optimization for Multi-Agents." In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 2022. http://dx.doi.org/10.1109/hora55278.2022.9799828.

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Liu, Lian, and Zimeng Wang. "Multi-Agents Interaction Approach based on Graph Network and Reinforcement Learning." In 2022 9th International Conference on Dependable Systems and Their Applications (DSA). IEEE, 2022. http://dx.doi.org/10.1109/dsa56465.2022.00101.

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Shen, Yi-Zhen, and Yong-Sheng Ding. "An Intelligent Multi-Agents Method for Study on Membrane Protein Interaction Network." In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5514946.

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Yang, Yu, Weiyong Yang, Zixin Guo, and Jiang Zhu. "Research on consensus algorithm of multi energy interaction agents based on PBFT." In 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA). IEEE, 2020. http://dx.doi.org/10.1109/iciba50161.2020.9277089.

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Mohseni-Kabir, Anahita, David Isele, and Kikuo Fujimura. "Interaction-Aware Multi-Agent Reinforcement Learning for Mobile Agents with Individual Goals." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8793721.

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Riedl, Mark, C. J. Saretto, and R. Michael Young. "Managing interaction between users and agents in a multi-agent storytelling environment." In the second international joint conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/860575.860694.

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Tian, Yu, Xingliang Huang, Ruigang Niu, Hongfeng Yu, Peijin Wang, and Xian Sun. "Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/189.

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Forecasting the future trajectories of multiple agents is a core technology for human-robot interaction systems. To predict multi-agent trajectories more accurately, it is inevitable that models need to improve interpretability and reduce redundancy. However, many methods adopt implicit weight calculation or black-box networks to learn the semantic interaction of agents, which obviously lack enough interpretation. In addition, most of the existing works model the relation among all agents in a one-to-one manner, which might lead to irrational trajectory predictions due to its redundancy and noise. To address the above issues, we present Hypertron, a human-understandable and lightweight hypergraph-based multi-agent forecasting framework, to explicitly estimate the motions of multiple agents and generate reasonable trajectories. The framework explicitly interacts among multiple agents and learns their latent intentions by our coarse-to-fine hypergraph convolution interaction module. Our experiments on several challenging real-world trajectory forecasting datasets show that Hypertron outperforms a wide array of state-of-the-art methods while saving over 60% parameters and reducing 30% inference time.
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