Detailed Information on Publication Record
2018
Strategy Representation by Decision Trees in Reactive Synthesis
BRÁZDIL, Tomáš, Krishnendu CHATTERJEE, Jan KŘETÍNSKÝ and Viktor TOMANBasic information
Original name
Strategy Representation by Decision Trees in Reactive Synthesis
Authors
BRÁZDIL, Tomáš (203 Czech Republic, guarantor, belonging to the institution), Krishnendu CHATTERJEE (356 India), Jan KŘETÍNSKÝ (203 Czech Republic) and Viktor TOMAN (703 Slovakia)
Edition
Cham, 24th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS 2018), p. 385-407, 23 pp. 2018
Publisher
Springer
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Switzerland
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
Impact factor
Impact factor: 0.402 in 2005
RIV identification code
RIV/00216224:14330/18:00118582
Organization unit
Faculty of Informatics
ISBN
978-3-319-89959-6
ISSN
UT WoS
000546326300021
Keywords in English
INFINITE GAMES; AUTOMATA
Tags
International impact, Reviewed
Změněno: 5/11/2021 15:01, RNDr. Pavel Šmerk, Ph.D.
Abstract
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.