39 citations · 119 across the 8 of their papers we have counts for
8 papers
A social path to human-like artificial intelligence
Edgar A. Duéñez-Guzmán, Suzanne Sadedin, Jane X. Wang +2
Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contempora…
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas
Udari Madhushani, Kevin R. McKee, John P. Agapiou +6
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others. In reinforcement learning, SVO has been…
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
Zun Li, Marc Lanctot, Kevin R. McKee +7
Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents' strategies, and exploiting this opponent model by playing a best respo…
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
Ian Gemp, Thomas Anthony, Yoram Bachrach +24
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 simul…
Subverting machines, fluctuating identities: Re-learning human categorization
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 at…
Warmth and competence in human-agent cooperation
Kevin R. McKee, Xuechunzi Bai, Susan T. Fiske
Interaction and cooperation with humans are overarching aspirations of artificial intelligence (AI) research. Recent studies demonstrate that AI agents trained with deep reinforcem…
A multi-agent reinforcement learning model of reputation and cooperation in human groups
Kevin R. McKee, Edward Hughes, Tina O. Zhu +7
Collective action demands that individuals efficiently coordinate how much, where, and when to cooperate. Laboratory experiments have extensively explored the first part of this pr…
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning
Kevin R. McKee, Joel Z. Leibo, Charlie Beattie +1
Generalization is a major challenge for multi-agent reinforcement learning. How well does an agent perform when placed in novel environments and in interactions with new co-players…