A multi-paradigm language for reactive synthesis

Abstract

This paper proposes a language for describing reactive synthesis problems that integrates imperative and declarative elements. The semantics is defined in terms of two-player turn-based infinite games with full information. Currently, synthesis tools accept linear temporal logic (LTL) as input, but this description is less structured and does not facilitate the expression of sequential constraints. This motivates the use of a structured programming language to specify synthesis problems. Transition systems and guarded commands serve as imperative constructs, expressed in a syntax based on that of the modeling language PROMELA. The syntax allows defining which player controls data and control flow, and separating a program into assumptions and guarantees. These notions are necessary for input to game solvers. The integration of imperative and declarative paradigms allows using the paradigm that is most appropriate for expressing each requirement. The declarative part is expressed in the LTL fragment of generalized reactivity(1), which admits efficient synthesis algorithms, extended with past LTL. The implementation translates PROMELA to input for the SLUGS synthesizer and is written in PYTHON. The AMBA AHB bus case study is revisited and synthesized efficiently, identifying the need to reorder binary decision diagrams during strategy construction, in order to prevent the exponential blowup observed in previous work.

Parallelizing Synthesis from Temporal Logic Specifications by Identifying Equicontrollable States

“…The computations were performed on a 32 core AMD Opteron machine at 2.4GHz with 96 GB of RAM, and and we see considerable gains in computation time for the parallelized approach. The implementation is written in Python using the packages dd, omega [15], and tulip [14]. 1 A possible explanation for the large variance is the cost of reordering the BDDs, which can show large variance depending on the structure of the formula for which the reordering is being done.…”

Performance Heuristics for GR(1) Synthesis and Related Algorithms

“…Slugs turns off reordering during strategy construction. Filippidis et al [9] reported better performance with reordering during strategy construction. We are not aware of any GR(1) specific heuristics for (dynamic) BDD variable ordering.…”

Layering Assume-Guarantee Contracts for Hierarchical System Design

“…For this purpose, we implemented the exact symbolic minimization method for the lattice of integer orthotopes (hypperrectangles aligned to axes). The implementation is available as part of the Python package omega [149]. Briefly, the problem of finding a minimal DNF formula of the form we described can be expressed as in Fig.…”