@inproceedings{DBLP:conf/aiia/DellAnna16,
  author    = {Davide Dell'Anna},
  title     = {Numerical and Temporal Planning for a Multi-agent Team Acting in the
               Real World},
  booktitle = {Proceedings of the 3rd Italian Workshop on Artificial Intelligence
               and Robotics, AIRO@AI*IA 2016},
  series    = {{CEUR} Workshop Proceedings},
  volume    = {1834},
  pages     = {10--14},
  year      = {2016},
  url       = {http://ceur-ws.org/Vol-1834/paper2.pdf},
  url_Slides= {2016_AIRO/AIRO16_DellAnna_Slides.pdf},
  keywords  = {Planning, Numerical, Temporal, MAS, Multi-Agent, UAV, Drones, SmatF2},
  timestamp = {Wed, 12 Feb 2020 16:44:29 +0100},
  biburl    = {https://dblp.org/rec/conf/aiia/DellAnna16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org},
  abstract = {This work addresses the problem of automatic planning for real world problems involving (possibly heterogeneous) teams of UAVs.
              Such problems are mainly characterized by cooperation, consumable resources and continuous numeric change, as well as concurrency, time and temporal constraints.
              These features make problems nontrivial for state-of-art planners. Difficulties are mainly due to temporal constraints, especially between different agents.
              This work reports experimental results concerning both synthetic problems and real-world multi-UAV multi-target planning scenarios.
              It shows that action-based approaches to planning, after a complex encoding process, can be successfully employed (with results comparable to other state-of-art approaches) to solve real-world problems.}
}

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