Stochastic Shortest Path with Energy Constraints in POMDPs:
(Extended Abstract)

D 2016

Stochastic Shortest Path with Energy Constraints in POMDPs: (Extended Abstract)

BRÁZDIL, Tomáš, Petr NOVOTNÝ, Krishnendu CHATTERJEE, Martin CHMELÍK, Anchit GUPTA et. al.

Základní údaje

Originální název

Stochastic Shortest Path with Energy Constraints in POMDPs: (Extended Abstract)

Autoři

BRÁZDIL, Tomáš (203 Česká republika, garant, domácí), Petr NOVOTNÝ (203 Česká republika), Krishnendu CHATTERJEE (356 Indie), Martin CHMELÍK (203 Česká republika) a Anchit GUPTA (356 Indie)

Vydání

Singapur, Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, od s. 1465-1466, 2 s. 2016

Nakladatel

ACM

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Kód RIV

RIV/00216224:14330/16:00088501

Organizační jednotka

Fakulta informatiky

ISBN

978-1-4503-4239-1

UT WoS

000465199800247

Klíčová slova anglicky

POMDP; planning; energy constraints; decision trees

Štítky

formela-conference

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2020 19:34, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

We extend the traditional framework of POMDPs to model resource consumption inducing a hard constraint on the behaviour of the model. Resource levels increase and decrease with transitions, and the hard constraint requires that the level remains positive in all steps. We present an algorithm for solving POMDPs with resource levels, developing on existing POMDP solvers. Our second contribution is related to policy representation. For larger POMDPs the policies computed by existing solvers are too large to be understandable, an issue particularly pronounced in POMDPs with resource levels. We present a procedure based on machine learning techniques that extracts important decisions of a policy and outputs its readable representation.

Návaznosti

GBP202/12/G061, projekt VaV

Název: Centrum excelence - Institut teoretické informatiky (CE-ITI) (Akronym: CE-ITI)

Investor: Grantová agentura ČR, Centrum excelence - Institut teoretické informatiky

Citovat

BRÁZDIL, Tomáš, Petr NOVOTNÝ, Krishnendu CHATTERJEE, Martin CHMELÍK a Anchit GUPTA. Stochastic Shortest Path with Energy Constraints in POMDPs: (Extended Abstract). In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems. Singapur: ACM, 2016, s. 1465-1466. ISBN 978-1-4503-4239-1.

@inproceedings{1366476,
   author = {Brázdil, Tomáš and Novotný, Petr and Chatterjee, Krishnendu and Chmelík, Martin and Gupta, Anchit},
   address = {Singapur},
   booktitle = {Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems},
   keywords = {POMDP; planning; energy constraints; decision trees},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Singapur},
   isbn = {978-1-4503-4239-1},
   pages = {1465-1466},
   publisher = {ACM},
   title = {Stochastic Shortest Path with Energy Constraints in POMDPs: (Extended Abstract)},
   year = {2016}
}

TY  - JOUR
ID  - 1366476
AU  - Brázdil, Tomáš - Novotný, Petr - Chatterjee, Krishnendu - Chmelík, Martin - Gupta, Anchit
PY  - 2016
TI  - Stochastic Shortest Path with Energy Constraints in POMDPs: (Extended Abstract)
PB  - ACM
CY  - Singapur
SN  - 9781450342391
KW  - POMDP
KW  - planning
KW  - energy constraints
KW  - decision trees
N2  - We extend the traditional framework of POMDPs to model resource consumption inducing a hard constraint on the behaviour of the model. Resource levels increase and decrease with transitions, and the hard constraint requires that the level remains positive in all steps. We present an algorithm for solving POMDPs with resource levels, developing on existing POMDP solvers. Our second contribution is related to policy representation. For larger POMDPs the policies computed by existing solvers are too large to be understandable, an issue particularly pronounced in POMDPs with resource levels. We present a procedure based on machine learning techniques that extracts important decisions of a policy and outputs its readable representation.
ER  -

BRÁZDIL, Tomáš, Petr NOVOTNÝ, Krishnendu CHATTERJEE, Martin CHMELÍK a Anchit GUPTA. Stochastic Shortest Path with Energy Constraints in POMDPs: (Extended Abstract). In \textit{Proceedings of the 2016 International Conference on Autonomous Agents \&{} Multiagent Systems}. Singapur: ACM, 2016, s.~1465-1466. ISBN~978-1-4503-4239-1.

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