Translating a Continuous-Time Temporal Logic into Timed Automata

2003 ◽  
pp. 322-338 ◽  
Author(s):  
Guangyuan Li ◽  
Zhisong Tang
Keyword(s):  
Temporal Logic ◽  
Continuous Time ◽  
Timed Automata

scholarly journals Continuous time temporal logic with counting

2012 ◽  
Vol 214 ◽  
pp. 1-9 ◽  
Author(s):  
Yoram Hirshfeld ◽  
Alexander Rabinovich
Keyword(s):  
Temporal Logic ◽  
Continuous Time

scholarly journals Robustness of temporal logic specifications for continuous-time signals

2009 ◽  
Vol 410 (42) ◽  
pp. 4262-4291 ◽  
Author(s):  
Georgios E. Fainekos ◽  
George J. Pappas
Keyword(s):  
Temporal Logic ◽  
Continuous Time ◽  
Time Signals

scholarly journals Transfer of Temporal Logic Formulas in Reinforcement Learning

2019 ◽  
Author(s):  
Zhe Xu ◽  
Ufuk Topcu
Keyword(s):  
Reinforcement Learning ◽  
Temporal Logic ◽  
Timed Automata ◽  
Disjunctive Normal Form ◽  
Transfer Of Knowledge ◽  
Extended State ◽  
Target Task ◽  
Order Of Magnitude ◽  
High Level ◽  
Timed Automaton

Transferring high-level knowledge from a source task to a target task is an effective way to expedite reinforcement learning (RL). For example, propositional logic and first-order logic have been used as representations of such knowledge. We study the transfer of knowledge between tasks in which the timing of the events matters. We call such tasks temporal tasks. We concretize similarity between temporal tasks through a notion of logical transferability, and develop a transfer learning approach between different yet similar temporal tasks. We first propose an inference technique to extract metric interval temporal logic (MITL) formulas in sequential disjunctive normal form from labeled trajectories collected in RL of the two tasks. If logical transferability is identified through this inference, we construct a timed automaton for each sequential conjunctive subformula of the inferred MITL formulas from both tasks. We perform RL on the extended state which includes the locations and clock valuations of the timed automata for the source task. We then establish mappings between the corresponding components (clocks, locations, etc.) of the timed automata from the two tasks, and transfer the extended Q-functions based on the established mappings. Finally, we perform RL on the extended state for the target task, starting with the transferred extended Q-functions. Our implementation results show, depending on how similar the source task and the target task are, that the sampling efficiency for the target task can be improved by up to one order of magnitude by performing RL in the extended state space, and further improved by up to another order of magnitude using the transferred extended Q-functions.

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