Some Classes of Imperfect Information Finite State-Space Stochastic Games with Finite-Dimensional Solutions

Some Classes of Imperfect Information Finite State-Space Stochastic Games with Finite-Dimensional... Stochastic games under imperfect information are typically computationally intractable even in the discrete-time/discrete-state case considered here. We consider a problem where one player has perfect information. A function of a conditional probability distribution is proposed as an information state. In the problem form here, the payoff is only a function of the terminal state of the system, and the initial information state is either linear or a sum of max-plus delta functions. When the initial information state belongs to these classes, its propagation is finite-dimensional. The state feedback value function is also finite-dimensional, and obtained via dynamic programming, but has a nonstandard form due to the necessity of an expanded state variable. Under a saddle point assumption, Certainty Equivalence is obtained and the proposed function is indeed an information state. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics and Optimization Springer Journals

Some Classes of Imperfect Information Finite State-Space Stochastic Games with Finite-Dimensional Solutions

Loading next page...
 
/lp/springer_journal/some-classes-of-imperfect-information-finite-state-space-stochastic-ZripHFHrWf
Publisher
Springer-Verlag
Copyright
Copyright © 2004 by Springer-Verlag
Subject
Philosophy
ISSN
0095-4616
eISSN
1432-0606
D.O.I.
10.1007/s00245-004-0793-y
Publisher site
See Article on Publisher Site

Abstract

Stochastic games under imperfect information are typically computationally intractable even in the discrete-time/discrete-state case considered here. We consider a problem where one player has perfect information. A function of a conditional probability distribution is proposed as an information state. In the problem form here, the payoff is only a function of the terminal state of the system, and the initial information state is either linear or a sum of max-plus delta functions. When the initial information state belongs to these classes, its propagation is finite-dimensional. The state feedback value function is also finite-dimensional, and obtained via dynamic programming, but has a nonstandard form due to the necessity of an expanded state variable. Under a saddle point assumption, Certainty Equivalence is obtained and the proposed function is indeed an information state.

Journal

Applied Mathematics and OptimizationSpringer Journals

Published: Aug 1, 2004

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off