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<article-title>Exploiting Factored Representations for Decentralized Execution in Multi-agent Teams</article-title>
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<author><a href="mailto:mroth@andrew.cmu.edu"><name>Maayan Roth</name></a></author>
<aff>Robotics Institute Carnegie ,Mellon University<br/> 5000 Forbes Avenue Pittsburgh, PA, USA</aff>

<author><a href="mailto:reids@cs.cmu.edu"><name>Reid Simmons</name></a></author>
<aff>Robotics Institute Carnegie, Mellon University <br/>5000 Forbes Avenue Pittsburgh, PA, USA</aff>

<author><a href="mailto:veloso@cs.cmu.edu"><name>Manuela Veloso</name></a></author>
<aff>Computer Science Department ,Carnegie Mellon University <br/>5000 Forbes Avenue Pittsburgh, PA, USA</aff>
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<title>ABSTRACT</title>
<p>In many cooperative multi-agent domains, there exist some
states in which the agents can act independently and others in which they need to coordinate with their teammates.
In this paper, we explore how factored representations of
state can be used to generate factored policies that can,
with minimal communication, be executed distributedly by
a multi-agent team. The factored policies indicate those
portions of the state where no coordination is necessary, automatically alert the agents when they reach a state in which
they do need to coordinate, and determine what the agents
should communicate in order to achieve this coordination.
We evaluate the success of our approach experimentally by
comparing the amount of communication needed by a team
executing a factored policy to a team that needs to communicate in every timestep.</p>
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