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Performance heuristics for GR(1) synthesis and related algorithms.

Elizabeth Firman1, Shahar Maoz1, Jan Oliver Ringert2

  • 11Tel Aviv University, Tel Aviv, Israel.

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Summary
This summary is machine-generated.

This study introduces heuristics to speed up GR(1) synthesis, a key algorithm in reactive systems. Integrating these heuristics into Spectra Tools significantly improves performance, especially for complex specifications.

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Area of Science:

  • Formal methods in computer science
  • Automated synthesis of reactive systems

Background:

  • Reactive synthesis for the Linear Temporal Logic (LTL) GR(1) fragment is a well-researched area.
  • Existing algorithms face challenges with scalability and running times for complex specifications.

Purpose of the Study:

  • To present and evaluate a novel list of heuristics aimed at reducing computation times for GR(1) synthesis.
  • To integrate these heuristics into the Spectra Tools synthesis environment.
  • To assess the effectiveness of these heuristics on diverse benchmarks, including student-generated robot specifications.

Main Methods:

  • Implementation of heuristics for controlled predecessor computation, BDDs, early fixed-point detection, fixed-point recycling, and unrealizable core computations.
  • Integration into the Spectra Tools software.
  • Empirical evaluation on SYNTECH15, SYNTECH17 robot specifications, and literature benchmarks.
  • Comparative analysis against existing tools (RATSY, Slugs).

Main Results:

  • All implemented heuristics, when applied together, demonstrate positive results in reducing GR(1) synthesis running times.
  • Heuristic effectiveness varies across different specification sets.
  • Spectra Tools, incorporating these heuristics, outperforms RATSY and Slugs on most benchmarks, with performance gains increasing for larger specifications.

Conclusions:

  • The proposed heuristics offer a significant improvement for GR(1) synthesis and related algorithms.
  • Spectra Tools provides a competitive and scalable solution for reactive synthesis.
  • The findings highlight the importance of tailored heuristics for optimizing automated synthesis tools.