27 citations · 64 across the 44 of their papers we have counts for
6 papers · 2 filters
Do Not Marginalize Mechanisms, Rather Consolidate!
Moritz Willig, Matej Zečević, Devendra Singh Dhami +1
Structural causal models (SCMs) are a powerful tool for understanding the complex causal relationships that underlie many real-world systems. As these systems grow in size, the num…
Causal Parrots: Large Language Models May Talk Causality But Are Not Causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami +1
Some argue scale is all what is needed to achieve AI, covering even causal models. We make it clear that large language models (LLMs) cannot be causal and give reason onto why some…
Scalable Neural-Probabilistic Answer Set Programming
Arseny Skryagin, Daniel Ochs, Devendra Singh Dhami +1
The goal of combining the robustness of neural networks and the expressiveness of symbolic methods has rekindled the interest in Neuro-Symbolic AI. Deep Probabilistic Programming L…
V-LoL: A Diagnostic Dataset for Visual Logical Learning
Lukas Helff, Wolfgang Stammer, Hikaru Shindo +2
Despite the successes of recent developments in visual AI, different shortcomings still exist; from missing exact logical reasoning, to abstract generalization abilities, to unders…
Know your Enemy: Investigating Monte-Carlo Tree Search with Opponent Models in Pommerman
Jannis Weil, Johannes Czech, Tobias Meuser +1
In combination with Reinforcement Learning, Monte-Carlo Tree Search has shown to outperform human grandmasters in games such as Chess, Shogi and Go with little to no prior domain k…
Representation Matters for Mastering Chess: Improved Feature Representation in AlphaZero Outperforms Switching to Transformers
Johannes Czech, Jannis Blüml, Kristian Kersting +1
While transformers have gained recognition as a versatile tool for artificial intelligence (AI), an unexplored challenge arises in the context of chess - a classical AI benchmark.…