By Topic

Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004. Proceedings of the Third International Joint Conference on

Date 23-23 July 2004

Filter Results

Displaying Results 1 - 25 of 300
  • The 3rd International Joint Conference on Autonomous Agents and MultiAgent Systems 2004

    Page(s): 0_1
    Save to Project icon | PDF file iconPDF (833 KB)  
    Freely Available from IEEE
  • Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems AAMAS 2004 - Cover

    Page(s): 0_2
    Save to Project icon | PDF file iconPDF (235 KB)  
    Freely Available from IEEE
  • This page intentionally left blank

    Page(s): 0_3
    Save to Project icon | PDF file iconPDF (234 KB)  
    Freely Available from IEEE
  • Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems AAMAS 2004 - Title

    Page(s): 0_4
    Save to Project icon | PDF file iconPDF (240 KB)  
    Freely Available from IEEE
  • Copyright

    Page(s): 0_5
    Save to Project icon | PDF file iconPDF (255 KB)  
    Freely Available from IEEE
  • Table of contents

    Page(s): 0_6 - 0_24
    Save to Project icon | PDF file iconPDF (319 KB)  
    Freely Available from IEEE
  • Preface

    Page(s): 0_25
    Save to Project icon | Request Permissions | PDF file iconPDF (245 KB)  
    Freely Available from IEEE
  • Acknowledgements

    Page(s): 1_1
    Save to Project icon | PDF file iconPDF (250 KB)  
    Freely Available from IEEE
  • Conference Officials

    Page(s): 1_2
    Save to Project icon | PDF file iconPDF (254 KB)  
    Freely Available from IEEE
  • Senior Program Committee Members

    Page(s): 1_3
    Save to Project icon | PDF file iconPDF (244 KB)  
    Freely Available from IEEE
  • Program Committee Members

    Page(s): 1_4 - 1_5
    Save to Project icon | PDF file iconPDF (250 KB)  
    Freely Available from IEEE
  • Auxiliary reviewers

    Page(s): 1_6
    Save to Project icon | PDF file iconPDF (246 KB)  
    Freely Available from IEEE
  • Award finalists

    Page(s): 1_8
    Save to Project icon | PDF file iconPDF (250 KB)  
    Freely Available from IEEE
  • Multi-agent planning in complex uncertain environments

    Page(s): 2
    Save to Project icon | PDF file iconPDF (268 KB)  
    Freely Available from IEEE
  • Protocol/mechanism design for cooperation/competition

    Page(s): 3 - 7
    Save to Project icon | PDF file iconPDF (326 KB)  
    Freely Available from IEEE
  • Brain meets brawn:why grid and agents need each other

    Page(s): 8 - 15
    Save to Project icon | PDF file iconPDF (331 KB)  
    Freely Available from IEEE
  • Knowledge, rationality and action

    Page(s): 16 - 23
    Save to Project icon | PDF file iconPDF (380 KB)  
    Freely Available from IEEE
  • Full text access may be available. Click article title to sign in or learn about subscription options.
  • A framework to control emergent survivability of multi agent systems

    Page(s): 28 - 35
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (482 KB) |  | HTML iconHTML  

    As the science of multi-agent systems matures, many developers are looking to deploy mission critical applications on distributed multi-agent systems (DMAS). Due to their distributed nature, designing survivable resource constrained DMAS is a serious challenge. Fortunately, the intrinsic flexibility of DMAS allows them to shift resources at runtime between dimensions of functionality such as security, robustness, and the primary application. In this paper we present an algebra for computing overall survivability from these dimensions of success, and a control infrastructure that leverages these degrees of freedom to make run-time adaptations at multiple hierarchical levels to maximize overall system survivability. We have implemented this survivability control infrastructure on the Cougaar agent architecture, and built a military logistics application that can survive in chaotic environments. Finally, we present results from assessing the performance of this application, and discuss the implications for future deployed DMAS. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A pheromone-based utility model for collaborative foraging

    Page(s): 36 - 43
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (503 KB) |  | HTML iconHTML  

    Multi-agent research often borrows from biology, where remarkable examples of collective intelligence may be found. One interesting example is ant colonies?? use of pheromones as a joint communication mechanism. In this paper we propose two pheromone-based algorithms for artificial agent foraging, trail-creation, and other tasks. Whereas practically all previous work in this area has focused on biologically-plausible but ad-hoc single pheromone models, we have developed a formalism which uses multiple pheromones to guide cooperative tasks. This model bears some similarity to reinforcement learning. However, our model takes advantage of symmetries common to foraging environments which enables it to achieve much faster reward propagation than reinforcement learning does. Using this approach we demonstrate cooperative behaviors well beyond the previous ant-foraging work, including the ability to create optimal foraging paths in the presence of obstacles, to cope with dynamic environments, and to follow tours with multiple waypoints.We believe that this model may be used for more complex problems still. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Modelling, control and validation of multi-agent plans in dynamic context

    Page(s): 44 - 51
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (412 KB) |  | HTML iconHTML  

    This paper proposes a multi-agent planning framework. It focuses on two fundamental aspects in dynamic context: the multi-agent plan validation and the control that takes into account the flexibility of agents behaviour. This framework is based on a dynamic management of control sets of the multi-agent plans. These control sets are pertinent constraints that enable to control and validate the multi-agent plans execution. We present the mechanisms of control and the validation criteria in multi-agent planning applied to our context of tactical aircraft simulation. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Multi-agent simulation of collaborative strategies in a supply chain

    Page(s): 52 - 59
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (486 KB) |  | HTML iconHTML  

    The bullwhip effect is the amplification of the order variability in a supply chain. This phenomenon causes important financial cost due to higher inventory levels and agility reduction. In this paper, we study, for each company in a supply chain, the individual incentive to collaborate to reduce this problem. To achieve this, we simulate a supply chain inspired by the Quebec forest industry, in which each company is an agent that uses one of three ordering schemes. Each ordering scheme represents a level of collaboration. One run of the simulation is done with fifty (50) weeks for each of the 36 = 729 combinations of these 3 ordering schemes among the 6 companies of the simulation. In each run, we evaluate each company??s inventory holding and backorder costs. These outcomes are used to build a game in the normal form, which is next analyzed using Game Theory. In particular, we find two Nash equilibria incurring the minimum cost of the supply chain. We also note that there are no Nash equilibria in which some companies do not collaborate: collaborating companies have no incentive to stop collaboration. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Towards truly agent-based traffic and mobility simulations

    Page(s): 60 - 67
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (496 KB) |  | HTML iconHTML  

    Traveling is necessary and desirable; yet, it imposes external costs on other people. Quantitative methods help finding a balance. Multi-agent simulations seem an obvious possibility here. A real world traffic simulation consists of many modules, all requiring different expertise. The paper discusses how such modules can be coupled to a complete simulation system, how such a system can be made fast enough to deal with real-world sizes (several millions of travelers), and how agent memory can be introduced. A real-world case study is presented, which says that multi-agent methods for traffic are mature enough to be used alongside existing methods. Finally, some outlook into the near future is given. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Using adaptive multi-agent systems to simulate economic models

    Page(s): 68 - 75
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (478 KB) |  | HTML iconHTML  

    Economic markets are complex systems. They are characterized by a large and dynamic population of firms. To deal with this complexity, we propose an adaptive multiagent system which models a set of firms in competition with each other within a shared market. The firms are represented by agents; each firm is represented by an adaptive agent. We show the advantages of adaptive agents to represent firms. Moreover, we underline the limits of the economic models which account for the firms only and ignore the organizational forms. We propose a new adaptive multiagent model that includes the organizational forms into the economic models. We simulate this model and discuss its advantages. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Towards a formal approach to overhearing: algorithms for conversation identification

    Page(s): 78 - 85
    Save to Project icon | Click to expandQuick Abstract | PDF file iconPDF (382 KB) |  | HTML iconHTML  

    Overhearing is gaining attention as a generic method for cooperative monitoring of distributed, open, multiagent systems. It involves monitoring the routine conversations of agents??who know they are being overheard??to assist the agents, assess their progress, or suggest advice. While there have been several investigations of applications and methods of overhearing, no formal model of overhearing exists. This paper takes steps towards such a model. It first formalizes a conversation system??the set of conversations in a multi-agent system. It then defines a key step in overhearing??conversation recognition?? identifying the conversations that took place within a system, given a set of overheard messages. We provide a skeleton algorithm for conversation recognition, and provide instantiations of it for settings involving no message loss, random message loss, and systematic message loss (such as always losing one side of the conversation). We analyze the complexity of these algorithms, and show that the systematic message loss algorithm, which is unique to overhearing, is significantly more efficient then the random loss algorithm (which is intractable). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.