Search Results for author: Max Kleiman-Weiner

Found 11 papers, 2 papers with code

Learning to Coordinate with Humans using Action Features

no code implementations29 Jan 2022 Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner, Jakob Foerster

An unaddressed challenge in human-AI coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations.

Inductive Bias

Zero-Shot Coordination via Semantic Relationships Between Actions and Observations

no code implementations29 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.

Inductive Bias

Modeling Communication to Coordinate Perspectives in Cooperation

no code implementations3 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.

Too many cooks: Bayesian inference for coordinating multi-agent collaboration

no code implementations26 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.

Bayesian Inference

Finding Friend and Foe in Multi-Agent Games

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.

Multi-agent Reinforcement Learning

Towards Formal Definitions of Blameworthiness, Intention, and Moral Responsibility

no code implementations13 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).

Blaming humans in autonomous vehicle accidents: Shared responsibility across levels of automation

no code implementations19 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.

A Computational Model of Commonsense Moral Decision Making

no code implementations12 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.

Autonomous Vehicles Decision Making

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