Strategy Representation by Decision Trees in Reactive Synthesis

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F18%3A00118582" target="_blank" >RIV/00216224:14330/18:00118582 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-319-89960-2_21" target="_blank" >http://dx.doi.org/10.1007/978-3-319-89960-2_21</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-89960-2_21" target="_blank" >10.1007/978-3-319-89960-2_21</a>

Result language

angličtina

Original language name

Strategy Representation by Decision Trees in Reactive Synthesis

Original language description

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.

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

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Article name in the collection

24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018)

ISBN

9783319899596

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

23

Pages from-to

385-407

Publisher name

Springer

Place of publication

Cham

Event location

Cham

Event date

Jan 1, 2018

Type of event by nationality

CST - Celostátní akce

UT code for WoS article

000546326300021