Research Article: Performance heuristics for GR(1) synthesis and related algorithms

Date Published: December 5, 2019

Publisher: Springer Berlin Heidelberg

Author(s): Elizabeth Firman, Shahar Maoz, Jan Oliver Ringert.

http://doi.org/10.1007/s00236-019-00351-9

Abstract

Reactive synthesis for the GR(1) fragment of LTL has been implemented and studied in many works. In this work we present and evaluate a list of heuristics to potentially reduce running times for GR(1) synthesis and related algorithms. The list includes several heuristics for controlled predecessor computation and BDDs, early detection of fixed-points and unrealizability, fixed-point recycling, and several heuristics for unrealizable core computations. We have implemented the heuristics and integrated them in our synthesis environment Spectra Tools, a set of tools for writing specifications and running synthesis and related analyses. We evaluate the presented heuristics on SYNTECH15, a total of 78 specifications of 6 autonomous Lego robots, on SYNTECH17, a total of 149 specifications of 5 autonomous Lego robots, all written by 3rd year undergraduate computer science students in two project classes we have taught, as well as on benchmarks from the literature. The evaluation investigates not only the potential of the suggested heuristics to improve computation times, but also the difference between existing benchmarks and the robot’s specifications in terms of the effectiveness of the heuristics. Our evaluation shows positive results for the application of all the heuristics together, which get more significant for specifications with slower original running times. It also shows differences in effectiveness when applied to different sets of specifications. Furthermore, a comparison between Spectra, with all the presented heuristics, and two existing tools, RATSY and Slugs, over two well-known benchmarks, shows that Spectra outperforms both on most of the specifications; the larger the specification, the faster Spectra becomes relative to the two other tools.

Partial Text

Reactive synthesis is an automated procedure to obtain a correct-by-construction reactive system from its temporal logic specification [43]. Rather than manually constructing a system and using model checking to verify its compliance with its specification, synthesis offers an approach where a correct implementation of the system is automatically obtained, if such an implementation exists.

We breifly provide necessary background on LTL and synthesis, documentclass[12pt]{minimal}
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begin{document}$$mu $$end{document}μ-calculus and fixed-points, GR(1) synthesis, unrealizability and Rabin(1) game, binary decision diagrams and the computation of controlled predecessors, and delta debugging.

We are now ready to present the main contribution of our work, a list of heuristics for improving running times of GR(1) and related algorithms. We divide the presented heuristics into three lists. The first list is inspired by classic heuristics for the controlled predecessor computation, i.e., the evaluation of the operators
and
(Sect. 3.1). The second list of heuristics applies to the GR(1) and Rabin(1) fixed-point algorithms (Sect. 3.2). Finally, the third list applies to computing unrealizable cores (Sect. 3.3). For each heuristics we present a rationale including a source of the heuristics, the heuristics and how we implemented it in Algorithms 1–6, and a brief intution about its potential effect.

We divide the evaluation into three parts following the division of the performance heuristics into heuristics for controlled predecessor computation and BDDs from Sect. 3.1, heuristics for the GR(1) and the Rabin(1) algorithms from Sect. 3.2, and heuristics for computing unrealizable core from Sect. 3.3.

We discuss related works that deal with BDD variable reorder and other performance heuristics for reactive synthesis and DDMin.

We presented a list of heuristics to potentially reduce running times for GR(1) synthesis and related algorithms. The list includes efficient computations of the controlled predecessors and BDD level operations, specifically grouping of variables and their primed copies, combined conjunction and existential abstraction, and use of partitioned transition relation. It further includes fixed-point related heuristics, specifically early detection of fixed-points, early detection of unrealizability, and fixed-point recycling. Finally, it includes heuristics for accelerating unrealizable core computation.

 

Source:

http://doi.org/10.1007/s00236-019-00351-9