812 citations · 855 across the 10 of their papers we have counts for
10 papers
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…
Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning
Michael Bradley Johanson, Edward Hughes, Finbarr Timbers +1
Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. Thi…
The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents
Patrick M. Pilarski, Andrew Butcher, Elnaz Davoodi +7
Learned communication between agents is a powerful tool when approaching decision-making problems that are hard to overcome by any single agent in isolation. However, continual coo…
Pavlovian Signalling with General Value Functions in Agent-Agent Temporal Decision Making
Andrew Butcher, Michael Bradley Johanson, Elnaz Davoodi +6
In this paper, we contribute a multi-faceted study into Pavlovian signalling -- a process by which learned, temporally extended predictions made by one agent inform decision-making…
Assessing Human Interaction in Virtual Reality With Continually Learning Prediction Agents Based on Reinforcement Learning Algorithms: A Pilot Study
Dylan J. A. Brenneis, Adam S. Parker, Michael Bradley Johanson +7
Artificial intelligence systems increasingly involve continual learning to enable flexibility in general situations that are not encountered during system training. Human interacti…
Negotiating Team Formation Using Deep Reinforcement Learning
Yoram Bachrach, Richard Everett, Edward Hughes +6
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a bin…
Learned human-agent decision-making, communication and joint action in a virtual reality environment
Patrick M. Pilarski, Andrew Butcher, Michael Johanson +3
Humans make decisions and act alongside other humans to pursue both short-term and long-term goals. As a result of ongoing progress in areas such as computing science and automatio…
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Matej Moravčík, Martin Schmid, Neil Burch +7
Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect informa…
Measuring the Size of Large No-Limit Poker Games
Michael Johanson
In the field of computational game theory, games are often compared in terms of their size. This can be measured in several ways, including the number of unique game states, the nu…
Solving Imperfect Information Games Using Decomposition
Neil Burch, Michael Johanson, Michael Bowling
Decomposition, i.e. independently analyzing possible subgames, has proven to be an essential principle for effective decision-making in perfect information games. However, in imper…