Automaton Model

Automaton models are increasingly used to represent and analyze complex systems, particularly in scenarios involving sequential data or decision-making under uncertainty. Current research focuses on developing more expressive automaton architectures, such as those incorporating registers or handling context-free grammars, and on improving learning algorithms for efficient and explainable model construction from data. These advancements enhance the applicability of automaton models in diverse fields, including complex event recognition, reinforcement learning, and anomaly detection in cyber-physical systems, by enabling more accurate modeling, improved performance, and better interpretability of results. The resulting models offer a powerful tool for understanding and controlling complex systems.

Papers

July 3, 2024

Complex Event Recognition with Symbolic Register Transducers: Extended Technical Report
Elias Alevizos, Alexander Artikis, Georgios Paliouras
Technical Report Product Automaton Finite State Transducer Event Understanding Automaton Model

January 15, 2024

Learning Explainable and Better Performing Representations of POMDP Strategies
Alexander Bork, Debraj Chakraborty, Kush Grover, Jan Kretinsky, Stefanie Mohr
Markov Decision Process Partially Observable Markov Decision Process Better Representation Product Automaton Automaton Model Star Algorithm

December 18, 2023

Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure
Tristan Bester, Benjamin Rosman, Steven James, Geraud Nangue Tasse
Reward Function Sample Efficient Reinforcement Learning Real World eXploitation Finite State Machine Adaptive Machine Automaton Model

August 25, 2023

Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs
Tom Westermann, Milapji Singh Gill, Alexander Fay
Knowledge Graph Cyber Physical Anomaly Data Alternating Time Temporal Logic Automaton Model

June 29, 2023

Learning Environment Models with Continuous Stochastic Dynamics
Martin Tappler, Edi Muškardin, Bernhard K. Aichernig, Bettina Könighofer
Deep Reinforcement Learning Stochastic Dynamical System Learning Automaton Automaton Model

February 15, 2023

VDHLA: Variable Depth Hybrid Learning Automaton and Its Application to Defense Against the Selfish Mining Attack in Bitcoin
Ali Nikhalat-Jahromi, Ali Mohammad Saghiri, Mohammad Reza Meybodi
Product Automaton Bitcoin User Learning Automaton Automaton Model

June 15, 2022

Robust Attack Graph Generation
Dennis Mouwen, Sicco Verwer, Azqa Nadeem
Learned Model Product Automaton Attack Graph Distilled Model Pseudo Victim AttackER Automaton Model

Automaton Model

Papers

Complex Event Recognition with Symbolic Register Transducers: Extended Technical Report

Learning Explainable and Better Performing Representations of POMDP Strategies

Counting Reward Automata: Sample Efficient Reinforcement Learning Through the Exploitation of Reward Function Structure

Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs

Learning Environment Models with Continuous Stochastic Dynamics

VDHLA: Variable Depth Hybrid Learning Automaton and Its Application to Defense Against the Selfish Mining Attack in Bitcoin

Robust Attack Graph Generation