2 citations · 4 across the 10 of their papers we have counts for
6 papers · 1 filter
G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models
Timo Bertram, Sidhant Bhavnani, Richard Freinschlag +3
In this work, we focus on SE-RRMs, a symbol-equivariant instantiation of RRMs that exhibits improved extrapolation to larger problem sizes. We propose a neuro-symbolic approach, ``…
UrzaGPT: LoRA-Tuned Large Language Models for Card Selection in Collectible Card Games
Timo Bertram
Collectible card games (CCGs) are a difficult genre for AI due to their partial observability, long-term decision-making, and evolving card sets. Due to this, current AI models per…
Contrastive Learning of Preferences with a Contextual InfoNCE Loss
Timo Bertram, Johannes Fürnkranz, Martin Müller
A common problem in contextual preference ranking is that a single preferred action is compared against several choices, thereby blowing up the complexity and skewing the preferenc…
Learning With Generalised Card Representations for "Magic: The Gathering"
Timo Bertram, Johannes Fürnkranz, Martin Müller
A defining feature of collectable card games is the deck building process prior to actual gameplay, in which players form their decks according to some restrictions. Learning to bu…
Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets
Timo Bertram, Johannes Fürnkranz, Martin Müller
In imperfect information games, the evaluation of a game state not only depends on the observable world but also relies on hidden parts of the environment. As accessing the obstruc…
Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess
Timo Bertram, Johannes Fürnkranz, Martin Müller
In imperfect information games, the game state is generally not fully observable to players. Therefore, good gameplay requires policies that deal with the different information tha…