682 citations · 1.1k across the 20 of their papers we have counts for
22 papers
A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms
Davide Paglieri, Logan Cross, Tim Genewein +3
Multi-agent AI science ecosystems rely on agents possessing tools that allow them to communicate, coordinate, and build on each other's work. Yet this shared infrastructure can als…
Solipsistic Superintelligence is Unlikely to be Cooperative
Rakshit S Trivedi, Natasha Jaques, Logan Cross +2
AI's central challenge is shifting from capability to coexistence. The dominant paradigm in AI research focuses on developing powerful agents that treat the world as an exogenous a…
Stabilising Generative Models of Attitude Change
Jayd Matyas, William A. Cunningham, Alexander Sasha Vezhnevets +3
Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories. These accounts often…
A Theory of Appropriateness That Accounts for Norms of Rationality
Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz +10
We propose a society-first theory of normative appropriateness where individuals, modeled as pre-trained actors with cognitive architectures analogous to Large Language Models (LLM…
A Generative Model of Conspicuous Consumption and Status Signaling
Logan Cross, Jordi Grau-Moya, William A. Cunningham +2
Status signaling drives human behavior and the allocation of scarce resources such as mating opportunities, yet the generative mechanisms governing how specific goods, signals, or…
Persona Generators: Generating Diverse Synthetic Personas for Arbitrary Contexts
Davide Paglieri, Logan Cross, William A. Cunningham +2
Evaluating AI systems that interact with humans requires understanding their behavior across diverse user populations, but collecting representative human data is often expensive o…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
A Pragmatic View of AI Personhood
Joel Z. Leibo, Alexander Sasha Vezhnevets, William A. Cunningham +1
The emergence of agentic Artificial Intelligence (AI) is set to trigger a "Cambrian explosion" of new kinds of personhood. This paper proposes a pragmatic framework for navigating…
Multi-Actor Generative Artificial Intelligence as a Game Engine
Alexander Sasha Vezhnevets, Jayd Matyas, Logan Cross +6
Generative AI can be used in multi-actor environments with purposes ranging from social science modeling to interactive narrative and AI evaluation. Supporting this diversity of us…
Societal and technological progress as sewing an ever-growing, ever-changing, patchy, and polychrome quilt
Joel Z. Leibo, Alexander Sasha Vezhnevets, William A. Cunningham +3
Artificial Intelligence (AI) systems are increasingly placed in positions where their decisions have real consequences, e.g., moderating online spaces, conducting research, and adv…
A theory of appropriateness with applications to generative artificial intelligence
Joel Z. Leibo, Alexander Sasha Vezhnevets, Manfred Diaz +11
What is appropriateness? Humans navigate a multi-scale mosaic of interlocking notions of what is appropriate for different situations. We act one way with our friends, another with…
Generative agent-based modeling with actions grounded in physical, social, or digital space using Concordia
Alexander Sasha Vezhnevets, John P. Agapiou, Avia Aharon +7
Agent-based modeling has been around for decades, and applied widely across the social and natural sciences. The scope of this research method is now poised to grow dramatically as…
Diversity Through Exclusion (DTE): Niche Identification for Reinforcement Learning through Value-Decomposition
Peter Sunehag, Alexander Sasha Vezhnevets, Edgar Duéñez-Guzmán +2
Many environments contain numerous available niches of variable value, each associated with a different local optimum in the space of behaviors (policy space). In such situations i…
Melting Pot 2.0
John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán +14
Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsis…
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
Kavya Kopparapu, Edgar A. Duéñez-Guzmán, Jayd Matyas +7
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. This is particular…
Statistical discrimination in learning agents
Edgar A. Duéñez-Guzmán, Kevin R. McKee, Yiran Mao +9
Undesired bias afflicts both human and algorithmic decision making, and may be especially prevalent when information processing trade-offs incentivize the use of heuristics. One pr…
Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot
Joel Z. Leibo, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets +7
Existing evaluation suites for multi-agent reinforcement learning (MARL) do not assess generalization to novel situations as their primary objective (unlike supervised-learning ben…
A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings
Eugene Vinitsky, Raphael Köster, John P. Agapiou +3
Society is characterized by the presence of a variety of social norms: collective patterns of sanctioning that can prevent miscoordination and free-riding. Inspired by this, we aim…
Options as responses: Grounding behavioural hierarchies in multi-agent RL
Alexander Sasha Vezhnevets, Yuhuai Wu, Remi Leblond +1
This paper investigates generalisation in multi-agent games, where the generality of the agent can be evaluated by playing against opponents it hasn't seen during training. We prop…
StarCraft II: A New Challenge for Reinforcement Learning
Oriol Vinyals, Timo Ewalds, Sergey Bartunov +22
This paper introduces SC2LE (StarCraft II Learning Environment), a reinforcement learning environment based on the StarCraft II game. This domain poses a new grand challenge for re…
FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul +4
We introduce FeUdal Networks (FuNs): a novel architecture for hierarchical reinforcement learning. Our approach is inspired by the feudal reinforcement learning proposal of Dayan a…
Strategic Attentive Writer for Learning Macro-Actions
Alexander, Vezhnevets, Volodymyr Mnih +5
We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement…