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20132022
most citedDeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

812 citations · 840 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.AI20228 cited

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…

cs.AI20222 cited

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…

cs.AI20221 cited

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…

cs.AI20194 cited

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…

cs.AI2017812 cited

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…