Policy synthesis for collective dynamics

Paul Piho, Jane Hillston

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

In this paper we consider the problem of policy synthesis for systems of large numbers of simple interacting agents where dynamics of the system change through information spread via broadcast communication. By modifying the existing modelling language CARMA and giving it a semantics in terms of continuous time Markov decision processes we introduce a natural way of formulating policy synthesis problems for such systems. However, solving policy synthesis problems is difficult since all non-trivial models result in very large state spaces. To combat this we propose an approach exploiting the results on fluid approximations of continuous time Markov chains to obtain estimates of optimal policies.
Original languageEnglish
Title of host publication15th International Conference on Quantitative Evaluation of SysTems (QEST 2018)
EditorsAnnabelle McIver, Andras Horvath
Place of PublicationBeijing, China
Number of pages17
ISBN (Electronic)978-3-319-99154-2
ISBN (Print)978-3-319-99153-5
Publication statusPublished - 15 Aug 2018
Event15th International Conference on Quantitative Evaluation of SysTems - Beijing, China
Duration: 4 Sept 20187 Sept 2018

Publication series

NameLecture Notes in Computer Science
PublisherSpringer, Cham
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Quantitative Evaluation of SysTems
Abbreviated titleQEST 2018
Internet address


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