Abstract
Collaborative task execution is an important area of research in multi-agent systems. In some situations, the agents are spatially distributed, have limited information about the environment, and update their knowledge via exchanging messages. Distributed approaches for task execution in such situations have been suggested in the literature. In these approaches, skills of robots and skills required for task execution are represented as p-dimensional binary skill vectors. However, in real-world applications, it would be more desirable to consider real-valued skills. In this paper, we develop a distributed framework that consider quantitative representation of skills. We have performed extensive experiments on a RoboCupRescue simulation environment. The experimental results show the efficacy of the approach.
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Acknowledgments
The authors thank the anonymous reviewers of ICCSA 2021 for their valuable suggestions. The second author was in part supported by a research grant from Google.
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Nath, A., Niyogi, R. (2021). Distributed Framework for Task Execution with Quantitative Skills. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_29
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DOI: https://doi.org/10.1007/978-3-030-87007-2_29
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