6 citations · 14 across the 8 of their papers we have counts for
7 papers · 1 filter
HyDRA: A Hybrid-Driven Reasoning Architecture for Verifiable Knowledge Graphs
Adrian Kaiser, Claudiu Leoveanu-Condrei, Ryan Gold +2
The synergy between symbolic knowledge, often represented by Knowledge Graphs (KGs), and the generative capabilities of neural networks is central to advancing neurosymbolic AI. A…
Parameter Choice and Neuro-Symbolic Approaches for Deep Domain-Invariant Learning
Marius-Constantin Dinu
As artificial intelligence (AI) systems advance, we move towards broad AI: systems capable of performing well on diverse tasks, understanding context, and adapting rapidly to new s…
Large Language Models Can Self-Improve At Web Agent Tasks
Ajay Patel, Markus Hofmarcher, Claudiu Leoveanu-Condrei +3
Training models to act as agents that can effectively navigate and perform actions in a complex environment, such as a web browser, has typically been challenging due to lack of tr…
SymbolicAI: A framework for logic-based approaches combining generative models and solvers
Marius-Constantin Dinu, Claudiu Leoveanu-Condrei, Markus Holzleitner +2
We introduce SymbolicAI, a versatile and modular framework employing a logic-based approach to concept learning and flow management in generative processes. SymbolicAI enables the…
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning
Christian Steinparz, Thomas Schmied, Fabian Paischer +5
In lifelong learning, an agent learns throughout its entire life without resets, in a constantly changing environment, as we humans do. Consequently, lifelong learning comes with a…
A Dataset Perspective on Offline Reinforcement Learning
Kajetan Schweighofer, Andreas Radler, Marius-Constantin Dinu +5
The application of Reinforcement Learning (RL) in real world environments can be expensive or risky due to sub-optimal policies during training. In Offline RL, this problem is avoi…
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Vihang P. Patil, Markus Hofmarcher, Marius-Constantin Dinu +5
Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER…