scholarly journals Compositional Algorithms for LTL Synthesis

Author(s):  
Emmanuel Filiot ◽  
Nayiong Jin ◽  
Jean-François Raskin
Keyword(s):  
2021 ◽  
Author(s):  
Giuseppe De Giacomo ◽  
Antonio Di Stasio ◽  
Giuseppe Perelli ◽  
Shufang Zhu

We study the impact of the need for the agent to obligatorily instruct the action stop in her strategies. More specifically we consider synthesis (i.e., planning) for LTLf goals under LTL environment specifications in the case the agent must mandatorily stop at a certain point. We show that this obligation makes it impossible to exploit the liveness part of the LTL environment specifications to achieve her goal, effectively reducing the environment specifications to their safety part only. This has a deep impact on the efficiency of solving the synthesis, which can sidestep handling Buchi determinization associated to LTL synthesis, in favor of finite-state automata manipulation as in LTLf synthesis. Next, we add to the agent goal, expressed in LTLf, a safety goal, expressed in LTL. Safety goals must hold forever, even when the agent stops, since the environment can still continue its evolution. Hence the agent, before stopping, must ensure that her safety goal will be maintained even after she stops. To do synthesis in this case, we devise an effective approach that mixes a synthesis technique based on finite-state automata (as in the case of LTLf goals) and model-checking of nondeterministic Buchi automata. In this way, again, we sidestep Buchi automata determinization, hence getting a synthesis technique that is intrinsically simpler than standard LTL synthesis.


Author(s):  
Nathanaël Fijalkow ◽  
Bastien Maubert ◽  
Aniello Murano ◽  
Moshe Vardi

Prompt-LTL extends Linear Temporal Logic with a bounded version of the ``eventually'' operator to express temporal requirements such as bounding waiting times. We study assume-guarantee synthesis for prompt-LTL: the goal is to construct a system such that for all environments satisfying a first prompt-LTL formula (the assumption) the system composed with this environment satisfies a second prompt-LTL formula (the guarantee). This problem has been open for a decade. We construct an algorithm for solving it and show that, like classical LTL synthesis, it is 2-EXPTIME-complete.


2020 ◽  
Vol 34 (03) ◽  
pp. 3088-3095
Author(s):  
Shufang Zhu ◽  
Giuseppe De Giacomo ◽  
Geguang Pu ◽  
Moshe Y. Vardi

In synthesis, assumptions are constraints on the environment that rule out certain environment behaviors. A key observation here is that even if we consider systems with LTLƒ goals on finite traces, environment assumptions need to be expressed over infinite traces, since accomplishing the agent goals may require an unbounded number of environment action. To solve synthesis with respect to finite-trace LTLƒ goals under infinite-trace assumptions, we could reduce the problem to LTL synthesis. Unfortunately, while synthesis in LTLƒ and in LTL have the same worst-case complexity (both 2EXPTIME-complete), the algorithms available for LTL synthesis are much more difficult in practice than those for LTLƒ synthesis. In this work we show that in interesting cases we can avoid such a detour to LTL synthesis and keep the simplicity of LTLƒ synthesis. Specifically, we develop a BDD-based fixpoint-based technique for handling basic forms of fairness and of stability assumptions. We show, empirically, that this technique performs much better than standard LTL synthesis.


Author(s):  
Giuseppe De Giacomo ◽  
Antonio Di Stasio ◽  
Lucas M. Tabajara ◽  
Moshe Vardi ◽  
Shufang Zhu

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.


Author(s):  
Benjamin Aminof ◽  
Giuseppe De Giacomo ◽  
Sasha Rubin

We study best-effort synthesis under environment assumptions specified in LTL, and show that this problem has exactly the same computational complexity of standard LTL synthesis: 2EXPTIME-complete. We provide optimal algorithms for computing best-effort strategies, both in the case of LTL over infinite traces and LTL over finite traces (i.e., LTLf). The latter are particularly well suited for implementation.


2011 ◽  
Vol 39 (3) ◽  
pp. 261-296 ◽  
Author(s):  
Emmanuel Filiot ◽  
Naiyong Jin ◽  
Jean-François Raskin
Keyword(s):  

Author(s):  
Aaron Bohy ◽  
Véronique Bruyère ◽  
Emmanuel Filiot ◽  
Naiyong Jin ◽  
Jean-François Raskin
Keyword(s):  

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