activity
20192026
most citedLearning Deterministic Finite Automata Decompositions from Examples and Demonstrations

1 citations · 1 across the 7 of their papers we have counts for

collaborators

8 papers

cs.LO2026

Scenario-Based Compositional Statistical Model Checking for Safety Specifications

Abhinav Pomalapally, Arya Raeesi, Kevin Kai-Chun Chang +2

In safety-critical domains such as autonomous driving, systems must be evaluated across a large number of environment conditions, often represented as composite scenarios built fro…

cs.LG2026

Controllability in preference-conditioned multi-objective reinforcement learning

Pau de las Heras Molins, Beyazit Yalcinkaya, Lasse Peters +2

Multi-objective reinforcement learning (MORL) allows a user to express preference over outcomes in terms of the relative importance of the objectives, but standard metrics cannot c…

cs.MA2025

Automata-Conditioned Cooperative Multi-Agent Reinforcement Learning

Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte, Ameesh Shah +2

We study learning multi-task, multi-agent policies for cooperative, temporal objectives, under centralized training, decentralized execution. In this setting, using automata to rep…

cs.LG2025

Provably Correct Automata Embeddings for Optimal Automata-Conditioned Reinforcement Learning

Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte +1

Automata-conditioned reinforcement learning (RL) has given promising results for learning multi-task policies capable of performing temporally extended objectives given at runtime,…

cs.LG2024

Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning

Beyazit Yalcinkaya, Niklas Lauffer, Marcell Vazquez-Chanlatte +1

Goal-conditioned reinforcement learning is a powerful way to control an AI agent's behavior at runtime. That said, popular goal representations, e.g., target states or natural lang…

cs.LG2023

Specification-Guided Data Aggregation for Semantically Aware Imitation Learning

Ameesh Shah, Jonathan DeCastro, John Gideon +3

Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios,…

cs.FL2022★ 1 cited

Learning Deterministic Finite Automata Decompositions from Examples and Demonstrations

Niklas Lauffer, Beyazit Yalcinkaya, Marcell Vazquez-Chanlatte +2

The identification of a deterministic finite automaton (DFA) from labeled examples is a well-studied problem in the literature; however, prior work focuses on the identification of…

cs.FL2019

ATAC: A Tool for Automating Timed Automata Construction

Beyazit Yalcinkaya, Ebru Aydin Gol

In this paper, we focus on the design and verification of timed automata (TA). We introduce a new method for assisting construction and verification of TA models along with a tool…