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RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies.

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Title: RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies.
Authors: Ferreira Jr., Paulo Roberto, dos Santos, Fernando, Bazzan, Ana L. C., Epstein, Daniel, Waskow, Samuel J.
Source: Autonomous Agents & Multi-Agent Systems; May2010, Vol. 20 Issue 3, p421-443, 23p, 2 Diagrams, 4 Charts, 5 Graphs
Subject Terms: MULTIAGENT systems, ALGORITHMS, INTELLIGENT agents, ALGEBRA, COMPUTER programming, COMPUTER software
Abstract: This paper addresses distributed task allocation among teams of agents in a RoboCup Rescue scenario. We are primarily concerned with testing different mechanisms that formalize issues underlying implicit coordination among teams of agents. These mechanisms are developed, implemented, and evaluated using two algorithms: Swarm-GAP and LA-DCOP. The latter bases task allocation on a comparison between an agent’s capability to perform a task and the capability demanded by this task. Swarm-GAP is a probabilistic approach in which an agent selects a task using a model inspired by task allocation among social insects. Both algorithms were also compared to another one that allocates tasks in a greedy way. Departing from previous works that tackle task allocation in the rescue scenario only among fire brigades, here we consider the various actors in the RoboCup Rescue, a step forward in the direction of realizing the concept of extreme teams. Tasks are allocated to teams of agents without explicit negotiation and using only local information. Our results show that the performance of Swarm-GAP and LA-DCOP are similar and that they outperform a greedy strategy. Also, it is possible to see that using more sophisticated mechanisms for task selection does pay off in terms of score. [ABSTRACT FROM AUTHOR]
Copyright of Autonomous Agents & Multi-Agent Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies.
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  Data: Autonomous Agents & Multi-Agent Systems; May2010, Vol. 20 Issue 3, p421-443, 23p, 2 Diagrams, 4 Charts, 5 Graphs
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  Data: <searchLink fieldCode="DE" term="%22MULTIAGENT+systems%22">MULTIAGENT systems</searchLink><br /><searchLink fieldCode="DE" term="%22ALGORITHMS%22">ALGORITHMS</searchLink><br /><searchLink fieldCode="DE" term="%22INTELLIGENT+agents%22">INTELLIGENT agents</searchLink><br /><searchLink fieldCode="DE" term="%22ALGEBRA%22">ALGEBRA</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+programming%22">COMPUTER programming</searchLink><br /><searchLink fieldCode="DE" term="%22COMPUTER+software%22">COMPUTER software</searchLink>
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  Data: This paper addresses distributed task allocation among teams of agents in a RoboCup Rescue scenario. We are primarily concerned with testing different mechanisms that formalize issues underlying implicit coordination among teams of agents. These mechanisms are developed, implemented, and evaluated using two algorithms: Swarm-GAP and LA-DCOP. The latter bases task allocation on a comparison between an agent’s capability to perform a task and the capability demanded by this task. Swarm-GAP is a probabilistic approach in which an agent selects a task using a model inspired by task allocation among social insects. Both algorithms were also compared to another one that allocates tasks in a greedy way. Departing from previous works that tackle task allocation in the rescue scenario only among fire brigades, here we consider the various actors in the RoboCup Rescue, a step forward in the direction of realizing the concept of extreme teams. Tasks are allocated to teams of agents without explicit negotiation and using only local information. Our results show that the performance of Swarm-GAP and LA-DCOP are similar and that they outperform a greedy strategy. Also, it is possible to see that using more sophisticated mechanisms for task selection does pay off in terms of score. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Autonomous Agents & Multi-Agent Systems is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: May2010
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