Learning Explainable and Better Performing Representations of POMDP Strategies
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F24%3A00139094" target="_blank" >RIV/00216224:14330/24:00139094 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-031-57249-4_15" target="_blank" >http://dx.doi.org/10.1007/978-3-031-57249-4_15</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-57249-4_15" target="_blank" >10.1007/978-3-031-57249-4_15</a>
Alternative languages
Result language
angličtina
Original language name
Learning Explainable and Better Performing Representations of POMDP Strategies
Original language description
Strategies for partially observable Markov decision processes (POMDP) typically require memory. One way to represent this memory is via automata. We present a method to learn an automaton representation of a strategy using a modification of the L∗-algorithm. Compared to the tabular representation of a strategy, the resulting automaton is dramatically smaller and thus also more explainable. Moreover, in the learning process, our heuristics may even improve the strategy’s performance. We compare our approach to an existing approach that synthesizes an automaton directly from the POMDP, thereby solving it. Our experiments show that our approach can lead to significant improvements in the size and quality of the resulting strategy representations.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
TACAS 2024, 30th International Conference on Tools and Algorithms for the Construction and Analysis of Systems
ISBN
9783031572487
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
21
Pages from-to
299-319
Publisher name
Springer
Place of publication
Luxembourg City, Luxembourg
Event location
Luxembourg City, Luxembourg
Event date
Jan 1, 2024
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
001284179800015