Search Results for author: Kevin R. McKee

Found 17 papers, 2 papers with code

How FaR Are Large Language Models From Agents with Theory-of-Mind?

no code implementations4 Oct 2023 Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju, Aditya Gupta, Kevin R. McKee, Ari Holtzman, Jay Pujara, Xiang Ren, Swaroop Mishra, Aida Nematzadeh, Shyam Upadhyay, Manaal Faruqui

We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D), which requires models to connect inferences about others' mental states to actions in social scenarios.

Question Answering

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

no code implementations22 Sep 2022 Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls

The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.

reinforcement-learning Reinforcement Learning (RL)

Subverting machines, fluctuating identities: Re-learning human categorization

no code implementations27 May 2022 Christina Lu, Jackie Kay, Kevin R. McKee

Most machine learning systems that interact with humans construct some notion of a person's "identity," yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static.

BIG-bench Machine Learning Fairness +1

Warmth and competence in human-agent cooperation

no code implementations31 Jan 2022 Kevin R. McKee, Xuechunzi Bai, Susan T. Fiske

Participants' perceptions of warmth and competence predict their stated preferences for different agents, above and beyond objective performance metrics.

reinforcement-learning Reinforcement Learning (RL)

Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria

no code implementations5 Jan 2022 Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas, Alexander Sasha Vezhnevets, John P. Agapiou, Kevin R. McKee, Richard Everett, Janusz Marecki, Joel Z. Leibo, Thore Graepel

A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate.

Statistical discrimination in learning agents

no code implementations21 Oct 2021 Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao, Ben Coppin, Silvia Chiappa, Alexander Sasha Vezhnevets, Michiel A. Bakker, Yoram Bachrach, Suzanne Sadedin, William Isaac, Karl Tuyls, Joel Z. Leibo

Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics.

Decision Making Multi-agent Reinforcement Learning

Collaborating with Humans without Human Data

1 code implementation NeurIPS 2021 DJ Strouse, Kevin R. McKee, Matt Botvinick, Edward Hughes, Richard Everett

Here, we study the problem of how to train agents that collaborate well with human partners without using human data.

Multi-agent Reinforcement Learning

Open Problems in Cooperative AI

no code implementations15 Dec 2020 Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel

We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.


Social diversity and social preferences in mixed-motive reinforcement learning

no code implementations6 Feb 2020 Kevin R. McKee, Ian Gemp, Brian McWilliams, Edgar A. Duéñez-Guzmán, Edward Hughes, Joel Z. Leibo

Recent research on reinforcement learning in pure-conflict and pure-common interest games has emphasized the importance of population heterogeneity.

reinforcement-learning Reinforcement Learning (RL)

Relational inductive bias for physical construction in humans and machines

no code implementations4 Jun 2018 Jessica B. Hamrick, Kelsey R. Allen, Victor Bapst, Tina Zhu, Kevin R. McKee, Joshua B. Tenenbaum, Peter W. Battaglia

While current deep learning systems excel at tasks such as object classification, language processing, and gameplay, few can construct or modify a complex system such as a tower of blocks.

Inductive Bias Test

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