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Towards Fully Probabilistic Cooperative Decision Making

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Book cover Multi-Agent Systems (EUMAS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11450))

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Abstract

Modern prescriptive decision theories try to support the dynamic decision making (DM) in incompletely-known, stochastic, and complex environments. Distributed solutions single out as the only universal and scalable way to cope with DM complexity and with limited DM resources. They require a solid cooperation scheme, which harmonises disparate aims and abilities of involved agents (human decision makers, DM realising devices and their mixed groups). The paper outlines a distributed fully probabilistic DM. Its flat structuring enables a fully-scalable cooperative DM of adaptive and wise selfish agents. The paper elaborates the cooperation based on sharing and processing agents’ aims in the way, which negligibly increases agents’ deliberation effort, while preserving advantages of distributed DM. Simulation results indicate the strength of the approach and confirm the possibility of using an agent-specific feedback for controlling its cooperation.

Supported by GAČR, grant 16-09848S. This work was strongly influenced by the feedback provided to us by Dr. Tatiana V. Guy. We appreciate her insight and help.

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Notes

  1. 1.

    The axiomatisation [19] also shows that any Bayesian DM formulation can be approximated by an FPD formulation to an arbitrary precision.

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Authors and Affiliations

  1. The Czech Academy of Sciences, Institute of Information Theory and Automation, POB 18, 182 08, Prague 8, Czech Republic

    Miroslav Kárný & Zohreh Alizadeh

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  1. Miroslav Kárný
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  2. Zohreh Alizadeh
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Correspondence to Miroslav Kárný .

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  1. University of Bergen, Bergen, Norway

    Marija Slavkovik

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Kárný, M., Alizadeh, Z. (2019). Towards Fully Probabilistic Cooperative Decision Making. In: Slavkovik, M. (eds) Multi-Agent Systems. EUMAS 2018. Lecture Notes in Computer Science(), vol 11450. Springer, Cham. https://doi.org/10.1007/978-3-030-14174-5_11

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  • DOI: https://doi.org/10.1007/978-3-030-14174-5_11

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