10.4230/LIPICS.FSTTCS.2011.411
Bertrand, Nathalie
Nathalie
Bertrand
Genest, Blaise
Blaise
Genest
Minimal Disclosure in Partially Observable Markov Decision Processes
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
2011
Article
Partially Observable Markov Decision Processes
Stochastic Games
Model-Checking
Worst-Case/Average-Case Analysis
Chakraborty, Supratik
Supratik
Chakraborty
Kumar, Amit
Amit
Kumar
2011
2011-12-01
2011-12-01
2011-12-01
en
urn:nbn:de:0030-drops-33286
10.4230/LIPIcs.FSTTCS.2011
978-3-939897-34-7
1868-8969
10.4230/LIPIcs.FSTTCS.2011
LIPIcs, Volume 13, FSTTCS 2011
IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2011)
2013
13
39
411
422
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
Chakraborty, Supratik
Supratik
Chakraborty
Kumar, Amit
Amit
Kumar
1868-8969
Leibniz International Proceedings in Informatics (LIPIcs)
2011
13
Schloss Dagstuhl – Leibniz-Zentrum für Informatik
12 pages
460947 bytes
application/pdf
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license
info:eu-repo/semantics/openAccess
For security and efficiency reasons, most systems do not give the
users a full access to their information. One key specification
formalism for these systems are the so called Partially Observable Markov Decision Processes (POMDP for short), which have been extensively studied in several research communities, among which AI and model-checking. In this paper we tackle the problem of the minimal information a user needs at runtime to achieve a simple goal, modeled as reaching an objective with probability one. More precisely, to achieve her goal, the user can at each step either choose to use the partial information, or pay a fixed cost and receive the full information. The natural question is then to minimize the cost the user needs to fulfill her objective. This optimization question gives rise to two different problems, whether we consider to minimize the worst case cost, or the average cost. On
the one hand, concerning the worst case cost, we show that efficient
techniques from the model checking community can be adapted to compute the optimal worst case cost and give optimal strategies for the users. On the other hand, we show that the optimal average price (a question typically considered in the AI community) cannot be computed in general, nor can it be approximated in polynomial time even up to a large approximation factor.
LIPIcs, Vol. 13, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2011), pages 411-422