5 papers
Infinite Trace Objectives with Finite Trace Techniques: Translating LTL to LTLf+
Christoph Weinhuber, Maximilian Prokop, Giuseppe De Giacomo +1
Linear Temporal Logic (LTL) is one of the most widely adopted languages for specifying temporal extended objectives in AI, with applications ranging from reactive synthesis to stoc…
SemML 2.0: Synthesizing Controllers for LTL
Jan Křetínský, Tobias Meggendorfer, Maximilian Prokop
Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-critical systems design. These sy…
Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions
Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier +3
We study multi-task reinforcement learning (RL), a setting in which an agent learns a single, universal policy capable of generalising to arbitrary, possibly unseen tasks. We consi…
SemML: Enhancing Automata-Theoretic LTL Synthesis with Machine Learning
Jan Kretinsky, Tobias Meggendorfer, Maximilian Prokop +1
Synthesizing a reactive system from specifications given in linear temporal logic (LTL) is a classical problem, finding its applications in safety-critical systems design. We prese…
Guessing Winning Policies in LTL Synthesis by Semantic Learning
Jan Kretinsky, Tobias Meggendorfer, Maximilian Prokop +1
We provide a learning-based technique for guessing a winning strategy in a parity game originating from an LTL synthesis problem. A cheaply obtained guess can be useful in several…