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  • aamas20

    Rights statement: © ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020 https://dl.acm.org/doi/abs/10.5555/3398761.3398880

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    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

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Real-time Learning and Planning in Environments with Swarms: A Hierarchical and a Parameter-based Simulation Approach

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Abstract

Swarms can be applied in many relevant domains, such as patrolling or rescue. They usually follow simple local rules, leading to complex emergent behavior. Given their wide applicability, an agent may need to take decisions in an environment containing a swarm that is not under its control, and that may even be an antagonist. Predicting the behavior of each swarm member is a great challenge, and must be done under real time constraints, since they usually move constantly following quick reactive algorithms. We propose the first two solutions for this novel problem, showing integrated on-line learning and planning for decision-making with unknown swarms: (i) we learn an ellipse abstraction of the swarm based on statistical models, and predict its future parameters using time-series; (ii) we learn algorithm parameters followed by each swarm member, in order to directly simulate them. We find in our
experiments that we are significantly faster to reach an objective than local repulsive forces, at the cost of success rate in some situations. Additionally, we show that this is a challenging problem for reinforcement learning.

Bibliographic note

© ACM, 2020. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems, 2020 https://dl.acm.org/doi/abs/10.5555/3398761.3398880