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18th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)

作者： Baert, Quentin Caron, Anne-Cecile Morge, Maxime Routier, Jean-Christophe Stathis, Kostas Univ Lille CRIStAL Ctr Rech Informat Signal & Automat Lille Cent Lille CNRSUMR 9189 Lille France Royal Holloway Univ London Dept Comp Sci London England

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Multicriteria optimization in CSPs :: Foundations and distributed solving approach

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11th International Conference on Artificial Intelligence - Methodology, Systems, and Applications

作者： Ben Jaâfar, I Khayati, N Ghédira, K UR SOIE Tunis Tunisia

ISBN: (纸本)3540229590

In Constraint Satisfaction and Optimization problems ranging from design engineering to economics, there are often multiple design criteria or cost function that govern the decision whereas, the user needs to be provided with a set of solutions which are the best for all the points of view. In this paper we define a new formalism for multicriteria optimization in constraint satisfaction problems "CSPs" and a multi-agent model solving problems in this setting. This approach separately optimizes different criteria in a distributed way by considering them as cooperative agents trying to reach all the non-dominated solutions. It exploits distributed problems solving together with nogood exchange and negotiation to enhance the overall problem-solving effort. The effectiveness of the approach is discussed on randomly generated examples.

关键词： multicriteria optimization constraint satisfaction problem distributed problem solving multi Agent systems cooperative agents nogoods exchange

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Cooperative Neighborhood Learning: Application to Robotic Inverse Model 13

Cooperative Neighborhood Learning: Application to Robotic In...

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13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Dato, Bruno Gleizes, Marie-Pierre Migeon, Frederic Univ Toulouse III Paul Sabatier Toulouse France

ISBN: (纸本)9789897584848

In this paper we present a generic multiagent learning system based on context learning applied in robotics. By applying learning with multiagent systems in robotics, we propose an endogenous self-learning strategy to improve learning performances. Inspired by constructivism, this learning mechanism encapsulates models in agents. To enhance the learning performance despite the weak amount of data, local and internal negotiation, also called cooperation, is introduced. Agents collaborate by generating artificial learning situations to improve their model. A second contribution is a new exploitation of the learnt models that allows less training. We consider highly redundant robotic arms to learn their Inverse Kinematic Model. A multiagent system learns a collective of models for a robotic arm. The exploitation of the models allows to control the end position of the robotic arm in a 2D/3D space. We show how the addition of artificial learning situations increases the performances of the learnt model and decreases the required labeled learning data. Experimentations are conducted on simulated arms with up to 30 joints in a 2D task space.

关键词： Multiagent Learning distributed problem solving Learning by Endogenous Feedback Inverse Kinematics Learning

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distributed Task Allocation in Dynamic Multi-Agent System

Distributed Task Allocation in Dynamic Multi-Agent System

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International Conference on Computing, Communication & Automation (ICCCA)

作者： Singhal, Vaishnavi Dahiya, Deepak Jaypee Univ Informat & Technol Dept CSE & IT Waknaghat HP India

ISBN: (纸本)9781479988907

from the last two decades, Software agents are playing an important role in the field of Artificial intelligence and the distributed problem solving. The properties of software agents like autonomy, reactivity, pro-activity and their social ability make them more center of focus for the real world problems. The accomplishment of any complex task is required to be done by the agents autonomously without any user intervention in order to achieve high reliability and adaptability. This paper mainly concentrates on the task allocation problem in multi-agent systems. Task Allocation is an important and challenging problem. This can be defined as the problem of allocating tasks among agents within a multi-agent system. Main objective of the task allocation problem is to maximize the number of successfully completed task and overall system utility without any conflict. To accomplish any complex task, agents negotiate, cooperate and coordinate with each other. Many researchers are working in the field of task allocation in multiagent systems. In this paper, various approaches of task allocation in multi-agent systems are discussed. The comparison of these approaches is also providing that lead to a discussion and motivation of the task allocation problem for multi-agent systems. This paper presents a hybrid approach for task allocation in dynamic multi-agent systems. The conclusion drawn from this survey is that, for dynamic multi-agent systems, the distributed task allocation is a better approach.

关键词： Agent coordination communication and negotiation Artificial Intelligence distributed problem solving Multi-Agent system System utility Task allocation

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Task Bundle Delegation for Reducing the Flowtime 13th

Task Bundle Delegation for Reducing the Flowtime

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13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Beauprez, Ellie Caron, Anne Cecile Morge, Maxime Routier, Jean-Christophe Univ Lille Cent Lille CNRS UMR 9189 CRIStAL F-59000 Lille France

ISBN: (纸本)9783031101618;9783031101601

In this paper, we study the problem of task reallocation for load-balancing in distributed data processing models that tackle vast amount of data. We propose a strategy based on cooperative agents used to optimize the rescheduling of tasks in multiple jobs which must be executed as soon as possible. It allows agents to determine locally the next tasks to process, to delegate, possibly to swap according to their knowledge, their own belief base and their peer modelling. The novelty lies in the ability of agents to identify opportunities and bottleneck agents, and afterwards to reassign some bundles of tasks thanks to concurrent bilateral negotiations. The strategy adopted by the agents allows to warrant a continuous improvement of the flowtime. Our experimentation reveals that our strategy reaches a flowtime which is better than the one reached by a DCOP resolution, close to the one reached by the classical heuristic approach, and significantly reduces the rescheduling time.

关键词： Multi-agents systems distributed problem solving Agent-based negotiation

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Crowdsourcing as a model for problem solving: An introduction and cases

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Convergence 2008年第1期14卷 75-90页

作者： Brabham, Daren C. University of Utah United States Department of Communication University of Utah Salt Lake City UT 84112 255 South Central Campus Dr. United States

Crowdsourcing is an online, distributed problem-solving and production model that has emerged in recent years. Notable examples of the model include Threadless, iStockphoto, InnoCentive, the Goldcorp Challenge, and user-generated advertising contests. This article provides an introduction to crowdsourcing, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production. This article also explores the possibilities for the model, its potential to exploit a crowd of innovators, and its potential for use beyond forprofit sectors. Finally, this article proposes an agenda for research into crowdsourcing. Copyright © 2008 Sage Publications.

关键词： Collective intelligence Crowdsourcing distributed problem solving Goldcorp challenge InnoCentive iStockphoto Open source Threadless Wisdom of crowds

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Ant-Cam network: Tracking across cameras using SVT parameters 2017

Ant-Cam network: Tracking across cameras using SVT parameter...

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11th International Conference on distributed Smart Cameras (ICDSC)

作者： Ben Khelifa, Lobna Quinton, Jean-Charles Berry, Francois Clermont Univ Pascal Inst CNRS Clermont Ferrand France Univ Grenoble Alpes CNRS Lab Jean Kuntzmann Grenoble France

ISBN: (纸本)9781450354875

Over the last years, the rapid growth of distributed smart cameras has triggered the search for new approaches of smartness of cameras to have better results. As communication among camera entities is becoming more and more complex and new ways of modeling communication have been proposed. These new ways have been taking inspiration from different fields such as socio-economic approach or game theory. Moreover, one of the major problems of the camera network is re-identification. However, in most cases, the interaction between cameras presupposes that the latter are able to perform perfect arid unambiguous detections, thus limiting the decision tasks to the Markovian model. Within this paper, we present a new approach of interaction between cameras based on a non-Markovian model. To resolve this issue, we can exploit other types of information rather than visual information to improve re-identification. This information is Spatial, Visual and Temporal (SVT). Temporal information holds the time needed to go from one camera to another, while spatial information contains the path followed by the target which is a key point for the decision-making process. This offers the possibility for the network to learn regularities and then reach a steady state.

关键词： Visual sensor network smart network smart camera distributed problem solving

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Information and knowledge exchange in a multi-agent system for stock trading

Information and knowledge exchange in a multi-agent system f...

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Enterprise Networking, Applications and Services Conference

作者： Luo, Y Davis, DN Liu, KC Staffordshire Univ Sch Comp Stafford ST18 0DG England

ISBN: (纸本)0780373421

A distributed problem solving system can be characterised as a group of individual agents running and co-operating with other agents to solve a problem. As dynamic domains such as stock trading are continuing to grow in complexity, it becomes more difficult to control the behaviour of agents in the domains where unexpected events can occur. This paper presented an information and knowledge exchange framework to support the distributed problem solving in the stock trading domain. It addresses two important issues: (1) How individual agents should be interconnected so that their capacities are efficiently used and their goals are accomplished effectively and efficiently;(2) How the information and knowledge transfer should take place among agents to allow them to respond successfully to user requests and unexpected situations in the outside world. The focus of this paper is dynamic knowledge exchange among MASST agents. The co-ordinator agent together with a decision enabling warehouse acting as a dynamic blackboard plus direct intercommunication among the agents enable facts, commands, and rules to be transferred between MASST agents. Knowledge can be exchanged among the agents by using a combination of facts, rules and commands transfers.

关键词： distributed problem solving multi-agent system knowledge exchange stock trading

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A Multi-Agent Negotiation Strategy for Reducing the Flowtime 13

A Multi-Agent Negotiation Strategy for Reducing the Flowtime

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13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Beauprez, Ellie Caron, Anne-Cecile Morge, Maxime Routier, Jean-Christophe Univ Lille CNRS Cent Lille UMR 9189 CRIStAL F-59000 Lille France

ISBN: (纸本)9789897584848

In this paper, we study the problem of task reallocation for load-balancing in distributed data processing models that tackle vast amount of data. In this context, we propose a novel strategy based on cooperative agents used to optimize the rescheduling of tasks for multiple jobs submitted by users in order to be executed as soon as possible. It allows an agent to determine locally the next task to process and the next task to delegate according to its knowledge, its own belief base and its peer modelling. The novelty of our strategy lies in the ability of agents to identify opportunities and bottleneck agents, and afterwards to reallocate some of the tasks. Our contribution is that, thanks to concurrent bilateral negotiations, tasks are continuously reallocated according to the local perception and the peer modelling of agents. In order to evaluate the responsiveness of our approach, we implement a prototype testbed and our experimentation reveals that our strategy reaches a flowtime which is close to the one reached by the classical heuristic approach and significantly reduces the rescheduling time.

关键词： Multi-Agent Systems distributed problem solving Negotiation Interaction Protocols

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distributed Search for Pure Nash Equilibria in Graphical Games 15

Distributed Search for Pure Nash Equilibria in Graphical Gam...

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15th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

作者： Litov, Omer Meisels, Amnon Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel

ISBN: (纸本)9781450342391

Graphical games introduce a compact representation, where agents' outcomes depend only on their neighbors. A distributed search algorithm for pure Nash equilibria of graphical games is presented. The algorithm uses the analogy of graphical games with asymmetric distributed constraints optimization problems (ADCOPs). The proposed algorithm includes three components - an admissible pruning heuristic;a back-checking mechanism;and a pseudo tree representation of the game. An experimental evaluation of the components of the proposed search algorithm is presented for randomly generated networks of multiple agents. The major speedup over a naive search algorithm is shown to arise from the use of a pseudo tree representation. A simple assessment method of the privacy loss due to back-checking is presented and is shown to result in a tradeoff between the performance of the complete algorithm and its privacy loss.

关键词： distributed problem solving DCOP Nash equilibrium Privacy

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