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DMAPP: A Distributed Multi-agent Path Planning Algorithm

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Book cover AI 2015: Advances in Artificial Intelligence (AI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9457))

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Abstract

Multi-agent path planning is a very challenging problem that has several applications. It has received a lot of attention in the last decade. Multi-agent optimal path planning is computationally intractable. Some algorithms have been suggested that may not return optimal plans but are useful in practice. These works mostly use centralized algorithms to compute plans. However in a multi-agent setting it would be more appropriate for the agents, with limited information, to compute the plans. In this paper, we suggest a distributed multi-agent path planning algorithm DMAPP, where all the phases are distributed. We have implemented DMAPP and have compared its performance with some existing algorithms. The results show the effectiveness of our approach.

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Acknowledgements

The authors thank the anonymous reviewers of AI-2015 for their valuable comments and suggestions for improving the paper.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Satyendra Singh Chouhan & Rajdeep Niyogi

Authors
  1. Satyendra Singh Chouhan
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  2. Rajdeep Niyogi
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Corresponding author

Correspondence to Satyendra Singh Chouhan .

Editor information

Editors and Affiliations

  1. The University of Waikato, Hamilton, New Zealand

    Bernhard Pfahringer

  2. The Australian National University, Canberra, Aust Capital Terr, Australia

    Jochen Renz

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Chouhan, S.S., Niyogi, R. (2015). DMAPP: A Distributed Multi-agent Path Planning Algorithm. In: Pfahringer, B., Renz, J. (eds) AI 2015: Advances in Artificial Intelligence. AI 2015. Lecture Notes in Computer Science(), vol 9457. Springer, Cham. https://doi.org/10.1007/978-3-319-26350-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-26350-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-26349-6

  • Online ISBN: 978-3-319-26350-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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