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Hierarchical LTL-Task MDPs for Multi-Agent Coordination through Auctioning and Learning
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control. Bosch Center for Artificial Intelligence.ORCID iD: 0000-0002-9547-9784
Bosch Center for Artificial Intelligence.
KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.ORCID iD: 0000-0001-7309-8086
2019 (English)In: The international journal of robotics research, ISSN 0278-3649, E-ISSN 1741-3176Article in journal (Refereed) Submitted
Abstract [en]

Given a temporal behavior specification and a team of agents available for execution in a stochastic environment, it is still an open problem how to efficiently decompose and allocate the specification to the agents while coordinating their actions accordingly and while considering long-term performance under uncertain external events. Our proposed framework works towards this goal by constructing a so-called hierachical LTL-Task MDP automatically by formally decomposing a given temporal logic goal specification into a set of smaller MDP planning problems. In order to efficiently find a multi-agent policy in this generated LTL-Task MDP, we combine methods from planning under uncertainty and auction-based task allocation with techniques from reinforcement learning. A particular challenge is to consider uncertainty in the environment, which might require significant additional effort to satisfy the given specification. This is addressed here by a formalism that allows the agents to consider preparation of such possible future reactions instead of updating the set of tasks only after observing an event.

Place, publisher, year, edition, pages
2019.
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-246196OAI: oai:DiVA.org:kth-246196DiVA, id: diva2:1296473
Funder
EU, Horizon 2020, 731869
Note

QC 20190319

Available from: 2019-03-15 Created: 2019-03-15 Last updated: 2019-05-10Bibliographically approved

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