16 citations · 18 across the 6 of their papers we have counts for
6 papers
Temporal-Causal Inference for Reinforcement Learning via Automata Learning
Jan Corazza, Daniil Kaminskyi, Simon Lutz +4
We consider reinforcement learning in environments with dynamics that undergo an irreversible phase transition governed by a hidden temporal pattern. The agent observes the base st…
Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
Alfio Ventura, Tim Katzke, Jan Corazza +1
Trust calibration -- aligning user trust judgment with model capability -- is crucial for safe deployment of explainable AI (XAI), yet is often evaluated via global trust ratings d…
Expediting Reinforcement Learning by Incorporating Knowledge About Temporal Causality in the Environment
Jan Corazza, Hadi Partovi Aria, Daniel Neider +1
Reinforcement learning (RL) algorithms struggle with learning optimal policies for tasks where reward feedback is sparse and depends on a complex sequence of events in the environm…
Reinforcement Learning with Stochastic Reward Machines
Jan Corazza, Ivan Gavran, Daniel Neider
Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing a…
Decentralizing Multi-Agent Reinforcement Learning with Temporal Causal Information
Jan Corazza, Hadi Partovi Aria, Hyohun Kim +2
Reinforcement learning (RL) algorithms can find an optimal policy for a single agent to accomplish a particular task. However, many real-world problems require multiple agents to c…
Accessible Smart Contracts Verification: Synthesizing Formal Models with Tamed LLMs
Jan Corazza, Ivan Gavran, Gabriela Moreira +1
When blockchain systems are said to be trustless, what this really means is that all the trust is put into software. Thus, there are strong incentives to ensure blockchain software…