All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Counterexample Explanation by Learning Small Strategies in Markov Decision Processes

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F15%3A00080918" target="_blank" >RIV/00216224:14330/15:00080918 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1007/978-3-319-21690-4_10" target="_blank" >http://dx.doi.org/10.1007/978-3-319-21690-4_10</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1007/978-3-319-21690-4_10" target="_blank" >10.1007/978-3-319-21690-4_10</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Counterexample Explanation by Learning Small Strategies in Markov Decision Processes

  • Original language description

    For deterministic systems, a counterexample to a property can simply be an error trace, whereas counterexamples in probabilistic systems are necessarily more complex. For instance, a set of erroneous traces with a sufficient cumulative probability mass can be used. Since these are too large objects to understand and manipulate, compact representations such as subchains have been considered. In the case of probabilistic systems with non-determinism, the situation is even more complex. While a subchain for a given strategy (or scheduler, resolving non-determinism) is a straightforward choice, we take a different approach. Instead, we focus on the strategy itself, and extract the most important decisions it makes, and present its succinct representation.The key tools we employ to achieve this are (1) introducing a concept of importance of a state w.r.t. the strategy, and (2) learning using decision trees. There are three main consequent advantages of our approach.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GBP202%2F12%2FG061" target="_blank" >GBP202/12/G061: Center of excellence - Institute for theoretical computer science (CE-ITI)</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2015

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Computer Aided Verification: 27th International Conference, CAV 2015

  • ISBN

    9783319216898

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    20

  • Pages from-to

    158-177

  • Publisher name

    Springer

  • Place of publication

    Cham

  • Event location

    Cham

  • Event date

    Jan 1, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article