MASCARET

2011 ◽  
pp. 1137-1156
Author(s):  
Cédric Buche ◽  
Ronan Querrec ◽  
Pierre De Loor ◽  
Pierre Chevaillier
Keyword(s):  
Social Environment ◽  
Intelligent Tutoring System ◽  
Pedagogical Agents ◽  
Multi Agent Systems ◽  
Training Environment ◽  
Pedagogical Strategies ◽  
Operational Conditions ◽  
The Social ◽  
Social Abilities ◽  
Multi Agent

This study concerns virtual environments for training in operational conditions. The principal developed idea is that these environments are heterogeneous and open multi-agent systems. The MASCARET model is proposed to organize the interactions between agents and to provide them reactive, cognitive and social abilities to simulate the physical and social environment. The physical environment represents, in a realistic way, the phenomena that learners and teachers have to take into account. The social environment is simulated by agents executing collaborative and adaptive tasks. These agents realize, in team, procedures that they have to adapt to the environment. The users participate to the training environment through their avatar. In this article, we explain how we integrated, in MASCARET, models necessary to the creation of Intelligent Tutoring System. We notably incorporate pedagogical strategies and pedagogical actions. We present pedagogical agents. To validate our model, the SÉCURÉVI application for fire fighters’ training is developed.

Sociable Behaviors in Virtual Worlds

2011 ◽  
pp. 123-139
Author(s):  
Francisco Grimaldo ◽  
Miguel Lozano ◽  
Fernando Barber ◽  
Juan M. Orduña
Keyword(s):  
Virtual Worlds ◽  
Social Impact ◽  
Social Model ◽  
Virtual Agents ◽  
Multi Agent Systems ◽  
Artificial Society ◽  
Simulated Environment ◽  
The Social ◽  
Multi Agent ◽  
Virtual Actors

When simulating three-dimensional environments populated by virtual humanoids, immersion requires the simulation of consistent social behaviors to keep the attention of the user/s while displaying realistic scenes. However, intelligent virtual actors still lack a kind of collective or social intelligence necessary to reinforce the roles they are playing in the simulated environment (e.g. a waiter, a guide, etc). Decision making for virtual agents has been traditionally modeled under self interested assumptions, which are not suitable for social multi-agent domains. Instead, artificial society models should be introduced to provide virtual actors with socially acceptable decisions, which are needed to cover the user expectations about the roles played in the simulated scenes. This chapter reviews the sociability models oriented to simulate the ability of the agents that are part of an artificial society and, thus, interact among its members. Furthemore, it also includes a full description of a social model for multi-agent systems that allows the actors to evaluate the social impact of their actions, and then to decide how to act in accordance with the simulated society. Finally, the authors show the social outcomes obtained from the simulation of a particular 3D social scenario.

Analysis and synthesis: multi-agent systems in the social sciences

2012 ◽  
Vol 27 (2) ◽  
pp. 123-136 ◽  
Author(s):  
Robert E. Marks
Keyword(s):  
Social Sciences ◽  
Computer Science ◽  
Common Source ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
The Social ◽  
Analysis And Synthesis ◽  
Multi Agent ◽  
Computational Systems

AbstractAlthough they flow from a common source, the uses of multi-agent systems (or ‘agent-based computational systems’––ACE) vary between the social sciences and computer science. The distinction can be broadly summarized as analysis versus synthesis, or explanation versus design. I compare and contrast these uses, and discuss sufficiency and necessity in simulations in general and in multi-agent systems in particular, with a computer science audience in mind.

scholarly journals Resource-bounded Norm Monitoring In Multi-agent Systems

2018 ◽  
Vol 62 ◽  
pp. 153-192 ◽  
Author(s):  
Natalia Criado
Keyword(s):  
Incomplete Information ◽  
Multi Agent Systems ◽  
Sociotechnical System ◽  
Agent Systems ◽  
Norm Violations ◽  
Norm Compliance ◽  
The Social ◽  
The World ◽  
Multi Agent ◽  
Bounded Norm

Norms allow system designers to specify the desired behaviour of a sociotechnical system. In this way, norms regulate what the social and technical agents in a sociotechnical system should (not) do. In this context, a vitally important question is the development of mechanisms for monitoring whether these agents comply with norms. Proposals on norm monitoring often assume that monitoring has no costs and/or that monitors have unlimited resources to observe the environment and the actions performed by agents. In this paper, we challenge this assumption and propose the first practical resource-bounded norm monitor. Our monitor is capable of selecting the resources to be deployed and use them to check norm compliance with incomplete information about the actions performed and the state of the world. We formally demonstrate the correctness and soundness of our norm monitor and study its complexity. We also demonstrate in randomised simulations and benchmark experiments that our monitor can select monitored resources effectively and efficiently, detecting more norm violations and fulfilments than other tractable optimization approaches and obtaining slightly worse results than intractable optimal approaches.

Coordinating Agent Interactions Under Open Environments

2011 ◽  
pp. 1430-1443
Author(s):  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Multi Agent Systems ◽  
Social Ability ◽  
Agent Interactions ◽  
Interaction Protocols ◽  
The Social ◽  
Open Environments ◽  
Agent Interaction ◽  
Coloured Petri Net ◽  
Multi Agent ◽  
Rich Interaction

An intelligent agent is a reactive, proactive, autonomous, and social entity. The social ability of an agent is exercised in a multi-agent system (MAS), which constitutes a collection of such agents. Current multi-agent systems mostly work in complex, open, and dynamic environments. In an open environment, many facts, such as domain constraints, agent number, and agent relationships, are not fixed. That brings a lot of difficulties to coordinate agents’ interactions and cooperation. One major problem that impedes agent interaction is that most current agent interaction protocols are not very suitable for open environments. In this chapter, we introduce an approach to ameliorate agent interactions from two perspectives. First, the approach can enable agents to form knowledge “rich” interaction protocols by using ontologies. Second, we use coloured Petri net (CPN) based methods to enable agents to form interaction protocols dynamically, which are more suitable for agent interaction under open environments.

Coordinating Agent Interactions Under Open Environments

2006 ◽  
pp. 52-67 ◽  
Author(s):  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Multi Agent Systems ◽  
Social Ability ◽  
Agent Interactions ◽  
Interaction Protocols ◽  
The Social ◽  
Open Environments ◽  
Agent Interaction ◽  
Coloured Petri Net ◽  
Multi Agent ◽  
Rich Interaction

An intelligent agent is a reactive, proactive, autonomous, and social entity. The social ability of an agent is exercised in a multi-agent system (MAS), which constitutes a collection of such agents. Current multi-agent systems mostly work in complex, open, and dynamic environments. In an open environment, many facts, such as domain constraints, agent number, and agent relationships, are not fixed. That brings a lot of difficulties to coordinate agents’ interactions and cooperation. One major problem that impedes agent interaction is that most current agent interaction protocols are not very suitable for open environments. In this chapter, we introduce an approach to ameliorate agent interactions from two perspectives. First, the approach can enable agents to form knowledge “rich” interaction protocols by using ontologies. Second, we use coloured Petri net (CPN) based methods to enable agents to form interaction protocols dynamically, which are more suitable for agent interaction under open environments.

EVOLUTIONARY GAMES IN MULTI-AGENT SYSTEMS OF WEIGHTED SOCIAL NETWORKS

2009 ◽  
Vol 20 (05) ◽  
pp. 701-710 ◽  
Author(s):  
WEN-BO DU ◽  
XIAN-BIN CAO ◽  
HAO-RAN ZHENG ◽  
HONG ZHOU ◽  
MAO-BIN HU
Keyword(s):  
Cooperative Behavior ◽  
Social Systems ◽  
Evolutionary Game ◽  
Evolutionary Games ◽  
Evolution Of Cooperation ◽  
Multi Agent Systems ◽  
Snowdrift Game ◽  
Cooperation Level ◽  
The Social ◽  
Multi Agent

Much empirical evidence has shown realistic networks are weighted. Compared with those on unweighted networks, the dynamics on weighted network often exhibit distinctly different phenomena. In this paper, we investigate the evolutionary game dynamics (prisoner's dilemma game and snowdrift game) on a weighted social network consisted of rational agents and focus on the evolution of cooperation in the system. Simulation results show that the cooperation level is strongly affected by the weighted nature of the network. Moreover, the variation of time series has also been investigated. Our work may be helpful in understanding the cooperative behavior in the social systems.

Interoperability Support for E-Business Applications through Standards, Services, and Multi-Agent Systems

2012 ◽  
pp. 129-153 ◽  
Author(s):  
Rainer Unland
Keyword(s):  
Intelligent Agents ◽  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Main Research ◽  
Active Behavior ◽  
Agent Systems ◽  
Business Applications ◽  
Social Abilities ◽  
Service Oriented ◽  
Multi Agent

Intelligent agents can be regarded as autonomous, problem-solving computational entities with social abilities that are capable of effective pro-active behavior in open and dynamic environments. If the term entity is replaced by service the substantial overlap in interests between both communities can easily be imagined. Nevertheless, right now the main research focus of each community seems to be different. The service-oriented computing community concentrates mainly on developing service engineering methodologies. Active topics in the multi-agent systems community are collaboration, self-organization, adaptability, flexibility, proactiveness, and interoperability. The overlap between those two communities and the fact that they concentrate on different research topics can definitely be seen as a huge chance since it means that each community may be able to benefit from the research efforts of the other.

The Study on the Key Technologies in Multiple Agent-Based Intelligent Tutoring System

2013 ◽  
Vol 846-847 ◽  
pp. 1889-1892
Author(s):  
Yan Yu ◽  
Jian Hua Wang
Keyword(s):  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Multi Agent Systems ◽  
Teaching System ◽  
Network Teaching ◽  
Agent Based ◽  
Agent Systems ◽  
Multiple Agent ◽  
Key Technologies ◽  
Multi Agent

Network teaching system has changed the way of teaching, with the artificial intelligence technology, Currently, many traditional network teaching system has been unable to meet the needs of the public.After studying the defects in current teaching systems and combining features of multi-agent systems and their application theories in intelligent teaching systems, this paper, integrating the concept of multi-agent systems, discusses the key technologies in multiple agent-based intelligent tutoring systems based on multiple agent-based intelligent tutoring system models. To achieve true intelligent teaching system, simulate real teaching environments, offer customized teaching services, and really realize students' independent learning and collaborative learning.

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