Strategy Representation by Decision Trees with Linear Classifiers

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F19%3A00108295" target="_blank" >RIV/00216224:14330/19:00108295 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-30281-8_7" target="_blank" >http://dx.doi.org/10.1007/978-3-030-30281-8_7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-30281-8_7" target="_blank" >10.1007/978-3-030-30281-8_7</a>

Result language
angličtina
Original language name
Strategy Representation by Decision Trees with Linear Classifiers
Original language description
Graph games and Markov decision processes (MDPs) are standard models in reactive synthesis and verification of probabilistic systems with nondeterminism. The class of omega-regular winning conditions; e.g., safety, reachability, liveness, parity conditions; provides a robust and expressive specification formalism for properties that arise in analysis of reactive systems. The resolutions of nondeterminism in games and MDPs are represented as strategies, and we consider succinct representation of such strategies. The decision-tree data structure from machine learning retains the flavor of decisions of strategies and allows entropy-based minimization to obtain succinct trees. However, in contrast to traditional machine-learning problems where small errors are allowed, for winning strategies in graph games and MDPs no error is allowed, and the decision tree must represent the entire strategy. In this work we propose decision trees with linear classifiers for representation of strategies in graph games and MDPs. We have implemented strategy representation using this data structure and we present experimental results for problems on graph games and MDPs, which show that this new data structure presents a much more efficient strategy representation as compared to standard decision trees.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
<a href="/en/project/GA18-11193S" target="_blank" >GA18-11193S: Algorithms for Infinite-State Discrete Systems and Games</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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