1 code implementation • 26 Mar 2020 • Rose E. Wang, Sarah A. Wu, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, Max Kleiman-Weiner
Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act.
1 code implementation • NeurIPS 2023 • Zhijing Jin, Yuen Chen, Felix Leeb, Luigi Gresele, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez Adauto, Max Kleiman-Weiner, Mrinmaya Sachan, Bernhard Schölkopf
Much of the existing work in natural language processing (NLP) focuses on evaluating commonsense causal reasoning in LLMs, thus failing to assess whether a model can perform causal inference in accordance with a set of well-defined formal rules.
1 code implementation • NeurIPS 2019 • Jack Serrino, Max Kleiman-Weiner, David C. Parkes, Joshua B. Tenenbaum
Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game.
1 code implementation • NeurIPS 2018 • DJ Strouse, Max Kleiman-Weiner, Josh Tenenbaum, Matt Botvinick, David Schwab
We show how to optimize these regularizers in a way that is easy to integrate with policy gradient reinforcement learning.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 19 Mar 2018 • Edmond Awad, Sydney Levine, Max Kleiman-Weiner, Sohan Dsouza, Joshua B. Tenenbaum, Azim Shariff, Jean-François Bonnefon, Iyad Rahwan
However, when both drivers make errors in cases of shared control between a human and a machine, the blame and responsibility attributed to the machine is reduced.
no code implementations • 12 Jan 2018 • Richard Kim, Max Kleiman-Weiner, Andres Abeliuk, Edmond Awad, Sohan Dsouza, Josh Tenenbaum, Iyad Rahwan
We introduce a new computational model of moral decision making, drawing on a recent theory of commonsense moral learning via social dynamics.
no code implementations • 13 Oct 2018 • Joseph Y. Halpern, Max Kleiman-Weiner
We provide formal definitions of degree of blameworthiness and intention relative to an epistemic state (a probability over causal models and a utility function on outcomes).
no code implementations • 18 Jan 2019 • Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum
This representation is grounded in the formalism of stochastic games and multi-agent reinforcement learning.
no code implementations • 3 Jun 2021 • Stephanie Stacy, Chenfei Li, Minglu Zhao, Yiling Yun, Qingyi Zhao, Max Kleiman-Weiner, Tao Gao
We propose a computational account of overloaded signaling from a shared agency perspective which we call the Imagined We for Communication.
no code implementations • 29 Sep 2021 • Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Nicolaus Foerster
An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations.
no code implementations • 19 Jan 2022 • Edmond Awad, Sydney Levine, Andrea Loreggia, Nicholas Mattei, Iyad Rahwan, Francesca Rossi, Kartik Talamadupula, Joshua Tenenbaum, Max Kleiman-Weiner
We can invent novel rules on the fly.
no code implementations • 29 Jan 2022 • Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Foerster
An unaddressed challenge in multi-agent coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations.
no code implementations • 25 Apr 2024 • Giorgio Piatti, Zhijing Jin, Max Kleiman-Weiner, Bernhard Schölkopf, Mrinmaya Sachan, Rada Mihalcea
Through this simulation environment, we explore the dynamics of resource sharing among AI agents, highlighting the importance of ethical considerations, strategic planning, and negotiation skills.