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Revisiting Game Representations: The Hidden Costs of Efficiency in Sequential Decision-making Algorithms
Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker.
(When) Is Truth-telling Favored in AI Debate?
For some problems, humans may not be able to accurately judge the goodness of AI-proposed solutions.
Rethinking Formal Models of Partially Observable Multiagent Decision Making
A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.
Value Functions for Depth-Limited Solving in Zero-Sum Imperfect-Information Games
We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games.
Approximation capability of neural networks on sets of probability measures and tree-structured data
This paper extends the proof of density of neural networks in the space of continuous (or even measurable) functions on Euclidean spaces to functions on compact sets of probability measures.
