Finite-trace and generalized-reactivity specifications in temporal synthesis

Linear Temporal Logic (LTL) synthesis aims at automatically synthesizing a program that complies with desired properties expressed in LTL. Unfortunately it has been proved to be too difficult computationally to perform full LTL synthesis. There have been two success stories with LTL synthesis, both having to do with the form of the specification. The first is the GR(1) approach: use safety conditions to determine the possible transitions in a game between the environment and the agent, plus one powerful notion of fairness, Generalized Reactivity(1), or GR(1). The second, inspired by AI planning, is focusing on finite-trace temporal synthesis, with LTLf (LTL on finite traces) as the specification language. In this paper we take these two lines of work and bring them together. We first study the case in which we have an LTLf agent goal and a GR(1) assumption. We then add to the framework safety conditions for both the environment and the agent, obtaining a highly expressive yet still scalable form of LTL synthesis.

[1]  Lydia E. Kavraki,et al.  Efficient Symbolic Reactive Synthesis for Finite-Horizon Tasks , 2019, 2019 International Conference on Robotics and Automation (ICRA).

[2]  Giuseppe De Giacomo,et al.  Synthesis for LTL and LDL on Finite Traces , 2015, IJCAI.

[3]  Erwin Engeler,et al.  Logic of Programs , 1981, Lecture Notes in Computer Science.

[4]  Vasumathi Raman,et al.  Slugs: Extensible GR(1) Synthesis , 2016, CAV.

[5]  Hadas Kress-Gazit,et al.  Temporal-Logic-Based Reactive Mission and Motion Planning , 2009, IEEE Transactions on Robotics.

[6]  Stéphane Lafortune,et al.  Supervisory control and reactive synthesis: a comparative introduction , 2017, Discret. Event Dyn. Syst..

[7]  Orna Kupferman,et al.  Recent Challenges and Ideas in Temporal Synthesis , 2012, SOFSEM.

[8]  Giuseppe De Giacomo,et al.  Automata-Theoretic Foundations of FOND Planning for LTLf and LDLf Goals , 2018, IJCAI.

[9]  Stefan Edelkamp,et al.  Automated Planning: Theory and Practice , 2007, Künstliche Intell..

[10]  Giuseppe De Giacomo,et al.  Planning and Synthesis Under Assumptions , 2018, ArXiv.

[11]  Michael Luttenberger,et al.  Strix: Explicit Reactive Synthesis Strikes Back! , 2018, CAV.

[12]  Giuseppe De Giacomo,et al.  Two-Stage Technique for LTLf Synthesis Under LTL Assumptions , 2020, KR.

[13]  Bernd Finkbeiner,et al.  Synthesis of Reactive Systems , 2016, Dependable Software Systems Engineering.

[14]  Amir Pnueli,et al.  On the synthesis of a reactive module , 1989, POPL '89.

[15]  Fred Kröger,et al.  Temporal Logic of Programs , 1987, EATCS Monographs on Theoretical Computer Science.

[16]  Alberto Camacho,et al.  Non-Deterministic Planning with Temporally Extended Goals: LTL over Finite and Infinite Traces , 2017, AAAI.

[17]  James U. Korein,et al.  Robotics , 2018, IBM Syst. J..

[18]  Giuseppe De Giacomo,et al.  Pure-Past Linear Temporal and Dynamic Logic on Finite Traces , 2020, IJCAI.

[19]  Paulo Tabuada,et al.  Dynamics-Based Reactive Synthesis and Automated Revisions for High-Level Robot Control , 2014, ArXiv.

[20]  Giuseppe De Giacomo,et al.  Planning under LTL Environment Specifications , 2019, ICAPS.

[21]  Giuseppe De Giacomo,et al.  Linear Temporal Logic and Linear Dynamic Logic on Finite Traces , 2013, IJCAI.