Generative Agents as Characters in a Simulated World

Matthew Berman introduces a Stanford and Google research architecture for language-model characters that interact in a sandbox.

MBMatthew BermanIntervenant · Forward Future
Chaîne
Matthew Berman
Publié
26 mai 2023
Vidéo
0:59
Lecture
1 min
Original
anglais
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RÉSUMÉ

En 30 secondes

Matthew Berman briefly introduces the 2023 paper “Generative Agents: Interactive Simulacra of Human Behavior.” He describes how researchers seed simulated characters with short identity descriptions and imagines how such agents could influence future game worlds.

  1. 1AI and game worldsBerman introduces a Stanford and Google research project and considers its potential for game characters.
  2. 2Generative agentsThe architecture supports characters that act and interact with relatively little user input.
  3. 3Starting with an identityResearchers provide each agent with a short natural-language description as a starting point.

À retenir

  1. The research explores simulated characters that interact with their environment and each other.
  2. Each agent begins with roughly a paragraph of identity information.
  3. Berman sees potential applications in games, though the project is a research prototype.
TEXTE INTÉGRAL

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Transcription seule
01

A possible future for game characters

Berman introduces the project and its promise for simulated characters. He presents it as a possible direction for future games.

I am excited about a project from Google and Stanford bringing artificial intelligence and video games together. It suggests a possible future in which non-player characters have lives of their own inside a simulated world.

02

The generative-agent architecture

The research uses language-model agents that can interact in a virtual environment. Each starts from a short description of its identity.

The paper “Generative Agents: Interactive Simulacra of Human Behavior” describes an architecture for characters that can interact with their environment and with one another with relatively little user input. Berman points to language-model technology behind the research, rather than a finished commercial game.

The researchers give each generative agent roughly a paragraph describing who it is. That short identity description serves as a starting point for its behavior in the simulation.

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