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Found 8 papers, 1 papers with code
An International Consortium for Evaluations of Societal-Scale Risks from Advanced AI
Given rapid progress toward advanced AI and risks from frontier AI systems (advanced AI systems pushing the boundaries of the AI capabilities frontier), the creation and implementation of AI governance and regulatory schemes deserves prioritization and substantial investment.
On Imperfect Recall in Multi-Agent Influence Diagrams
Multi-agent influence diagrams (MAIDs) are a popular game-theoretic model based on Bayesian networks.
Reasoning about Causality in Games
Regarding question iii), we describe correspondences between causal games and other formalisms, and explain how causal games can be used to answer queries that other causal or game-theoretic models do not support.
Learning Task Automata for Reinforcement Learning using Hidden Markov Models
Second, we propose a novel method for distilling the task automaton (assumed to be a deterministic finite automaton) from the learnt product MDP.
Concentric Spherical GNN for 3D Representation Learning
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.
Equilibrium Refinements for Multi-Agent Influence Diagrams: Theory and Practice
Multi-agent influence diagrams (MAIDs) are a popular form of graphical model that, for certain classes of games, have been shown to offer key complexity and explainability advantages over traditional extensive form game (EFG) representations.
How Robust Are Graph Neural Networks to Structural Noise?
Graph neural networks (GNNs) are an emerging model for learning graph embeddings and making predictions on graph structured data.
Exploring AI Futures Through Role Play
We present an innovative methodology for studying and teaching the impacts of AI through a role play game.
