Abstract
In this paper, we describe the strategies used by our team, MLFC, that led us to achieve the 2\(^{nd}\) place in the 15\(^{th}\) edition of the Multi-Agent Programming Contest. The scenario used in the contest is an extension of the previous edition (14\(^{th}\)) “Agents Assemble” wherein two teams of agents move around a 2D grid and compete to assemble complex block structures. We discuss the languages and tools used during the development of our team. Then, we summarise the main strategies that were carried over from our previous participation in the 14\(^{th}\) edition and list the limitations (if any) of using these strategies in the latest contest edition. We also developed new strategies that were made specifically for the extended scenario: cartography (determining the size of the map); formal verification of the map merging protocol (to provide assurances that it works when increasing the number of agents); plan cache (efficiently scaling the number of planners); task achievement (forming groups of agents to achieve tasks); and bullies (agents that focus on stopping agents from the opposing team). Finally, we give a brief overview of our performance in the contest and discuss what we believe were our shortcomings.
Work supported by UK Research and Innovation, and EPSRC Hubs for “Robotics and AI in Hazardous Environments”: EP/R026092 (FAIR-SPACE) and EP/R026084 (RAIN). Cardoso’s work is also supported by Royal Academy of Engineering under the Chairs in Emerging Technologies scheme.
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Notes
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The source code for MLFC is available and can be downloaded from the following URL: https://github.com/autonomy-and-verification-uol/mapc2020-lfc.
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Clear events occur randomly, but agents have access to a clear action which has a reduced area of effect but otherwise functions the same. A clear event/action will remove any obstacles or blocks and disable any agents that are inside its area of effect.
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An example of a problem file that was generated dynamically during one of the matches can be found at: https://github.com/autonomy-and-verification-uol/mapc2020-lfc/blob/master/planner/example_problem.pddl.
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Failures of a movement action are tracked for the agent to have an up-to-date idea of the distance to its destination.
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When \(A_1\) and \(A_2\) first adopt the role of cartographers, they need to retain the distance between them, as this initial distance has to be added to the final sum.
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The mapped values are not semantically relevant, as long as is preserved for all mappings.
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Cardoso, R.C., Ferrando, A., Papacchini, F., Luckcuck, M., Linker, S., Payne, T.R. (2021). MLFC: From 10 to 50 Planners in the Multi-Agent Programming Contest. In: Ahlbrecht, T., Dix, J., Fiekas, N., Krausburg, T. (eds) The Multi-Agent Programming Contest 2021. MAPC 2021. Lecture Notes in Computer Science(), vol 12947. Springer, Cham. https://doi.org/10.1007/978-3-030-88549-6_4
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