Strategy Representation by Decision Trees in Reactive Synthesis

BRÁZDIL, Tomáš, Krishnendu CHATTERJEE, Jan KŘETÍNSKÝ a Viktor TOMAN. Strategy Representation by Decision Trees in Reactive Synthesis. In 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018). Cham: Springer, 2018, s. 385-407. ISBN 978-3-319-89959-6. Dostupné z: https://dx.doi.org/10.1007/978-3-319-89960-2_21.

Základní údaje

Originální název

Strategy Representation by Decision Trees in Reactive Synthesis

Autoři

BRÁZDIL, Tomáš (203 Česká republika, garant, domácí), Krishnendu CHATTERJEE (356 Indie), Jan KŘETÍNSKÝ (203 Česká republika) a Viktor TOMAN (703 Slovensko)

Vydání

Cham, 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018), od s. 385-407, 23 s. 2018

Nakladatel

Springer

---

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/18:00118582

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-89959-6

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-89960-2_21

UT WoS

000546326300021

Klíčová slova anglicky

INFINITE GAMES; AUTOMATA

Štítky

core_A, firank_A

Příznaky

Mezinárodní význam, Recenzováno

---

Anotace

V originále

Graph games played by two players over finite-state graphs are central in many problems in computer science. In particular, graph games with w-regular winning conditions, specified as parity objectives, which can express properties such as safety, liveness, fairness, are the basic framework for verification and synthesis of reactive systems. The decisions for a player at various states of the graph game are represented as strategies. While the algorithmic problem for solving graph games with parity objectives has been widely studied, the most prominent data-structure for strategy representation in graph games has been binary decision diagrams (BDDs). However, due to the bit-level representation, BDDs do not retain the inherent flavor of the decisions of strategies, and are notoriously hard to minimize to obtain succinct representation. In this work we propose decision trees for strategy representation in graph games. Decision trees retain the flavor of decisions of strategies and allow entropy-based minimization to obtain succinct trees. However, decision trees work in settings (e.g., probabilistic models) where errors are allowed, and overfitting of data is typically avoided. In contrast, for strategies in graph games no error is allowed, and the decision tree must represent the entire strategy. We develop new techniques to extend decision trees to overcome the above obstacles, while retaining the entropy-based techniques to obtain succinct trees. We have implemented our techniques to extend the existing decision tree solvers. We present experimental results for problems in reactive synthesis to show that decision trees provide a much more efficient data-structure for strategy representation as compared to BDDs.