All posts by Google DeepMind Blog

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AI for the board game Diplomacy

Successful communication and cooperation have been crucial for helping societies advance throughout history. The closed environments of board games can serve as a sandbox for modelling and investigating interaction and communication – and we can learn a lot from playing them. In our recent paper, published today in Nature Communications, we show how artificial agents can use communication to better cooperate in the board game Diplomacy, a vibrant domain in artificial intelligence (AI) research, known for its focus on alliance building.

AI for the board game Diplomacy

Successful communication and cooperation have been crucial for helping societies advance throughout history. The closed environments of board games can serve as a sandbox for modelling and investigating interaction and communication – and we can learn a lot from playing them. In our recent paper, published today in Nature Communications, we show how artificial agents can use communication to better cooperate in the board game Diplomacy, a vibrant domain in artificial intelligence (AI) research, known for its focus on alliance building.

DeepMind’s latest research at NeurIPS 2022

NeurIPS is the world’s largest conference in artificial intelligence (AI) and machine learning (ML), and we’re proud to support the event as Diamond sponsors, helping foster the exchange of research advances in the AI and ML community. Teams from across DeepMind are presenting 47 papers, including 35 external collaborations in virtual panels and poster sessions.

DeepMind’s latest research at NeurIPS 2022

NeurIPS is the world’s largest conference in artificial intelligence (AI) and machine learning (ML), and we’re proud to support the event as Diamond sponsors, helping foster the exchange of research advances in the AI and ML community. Teams from across DeepMind are presenting 47 papers, including 35 external collaborations in virtual panels and poster sessions.

Building interactive agents in video game worlds

Most artificial intelligence (AI) researchers now believe that writing computer code which can capture the nuances of situated interactions is impossible. Alternatively, modern machine learning (ML) researchers have focused on learning about these types of interactions from data. To explore these learning-based approaches and quickly build agents that can make sense of human instructions and safely perform actions in open-ended conditions, we created a research framework within a video game environment.Today, we’re publishing a paper [INSERT LINK] and collection of videos, showing our early steps in building video game AIs that can understand fuzzy human concepts – and therefore, can begin to interact with people on their own terms.

Building interactive agents in video game worlds

Most artificial intelligence (AI) researchers now believe that writing computer code which can capture the nuances of situated interactions is impossible. Alternatively, modern machine learning (ML) researchers have focused on learning about these types of interactions from data. To explore these learning-based approaches and quickly build agents that can make sense of human instructions and safely perform actions in open-ended conditions, we created a research framework within a video game environment.Today, we’re publishing a paper [INSERT LINK] and collection of videos, showing our early steps in building video game AIs that can understand fuzzy human concepts – and therefore, can begin to interact with people on their own terms.
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