Jan Křetínský — accepted AI-conference papers

2026

Semantically Labelled Automata for Multi-Task Reinforcement Learning with LTL Instructions

Alessandro Abate, Giuseppe De Giacomo, Mathias Jackermeier, Jan Křetínský, Maximilian Prokop, Christoph Weinhuber

IJCAI 2026

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 consider tasks specified as linear temporal logic (LTL) formulae, which are commonly used in formal methods to specify proper

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2022

Planning via model checking with decision-tree controllers

Jonis Kiesbye, Kush Grover, Pranav Ashok, Jan Křetínský

ICRA 2022poster

Planning problems can be solved not only by planners, but also by model checkers. While the former yield a plan that requires replanning as soon as any fault occurs, the latter provide a “universal” plan (a.k.a. strategy, policy, or controller) able to make decisions under all circumstances. One of…

Cited by 8 Source ↗Scholar ↗

2021

LTL-Constrained Steady-State Policy Synthesis

Jan Křetínský

IJCAI 2021poster

Decision-making policies for agents are often synthesized with the constraint that a formal specification of behaviour is satisfied. Here we focus on infinite-horizon properties. On the one hand, Linear Temporal Logic (LTL) is a popular example of a formalism for qualitative specifications. On the…

Cited by 8 Source ↗PDF ↗Scholar ↗