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pro vyhledávání: '"Joel Z"'
Cultural accumulation drives the open-ended and diverse progress in capabilities spanning human history. It builds an expanding body of knowledge and skills by combining individual exploration with inter-generational information transmission. Despite
Externí odkaz:
http://arxiv.org/abs/2406.00392
Traditionally, cognitive and computer scientists have viewed intelligence solipsistically, as a property of unitary agents devoid of social context. Given the success of contemporary learning algorithms, we argue that the bottleneck in artificial int
Externí odkaz:
http://arxiv.org/abs/2405.15815
Normal-form games with two players, each with two strategies, are the most studied class of games. These so-called 2x2 games are used to model a variety of strategic interactions. They appear in game theory, economics, and artificial intelligence res
Externí odkaz:
http://arxiv.org/abs/2402.16985
We study computationally efficient methods for finding equilibria in n-player general-sum games, specifically ones that afford complex visuomotor skills. We show how existing methods would struggle in this setting, either computationally or in theory
Externí odkaz:
http://arxiv.org/abs/2401.05133
Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous disciplines, including game theory, economics, social sciences, and evolutionary biology. Research in this area aims to understand both how agents can coordinate eff
Externí odkaz:
http://arxiv.org/abs/2312.05162
Autor:
Vezhnevets, Alexander Sasha, Agapiou, John P., Aharon, Avia, Ziv, Ron, Matyas, Jayd, Duéñez-Guzmán, Edgar A., Cunningham, William A., Osindero, Simon, Karmon, Danny, Leibo, Joel Z.
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 it absorbs the new affordances provided by Large Language Models (LLM
Externí odkaz:
http://arxiv.org/abs/2312.03664
Autor:
Brinkmann, Levin, Baumann, Fabian, Bonnefon, Jean-François, Derex, Maxime, Müller, Thomas F., Nussberger, Anne-Marie, Czaplicka, Agnieszka, Acerbi, Alberto, Griffiths, Thomas L., Henrich, Joseph, Leibo, Joel Z., McElreath, Richard, Oudeyer, Pierre-Yves, Stray, Jonathan, Rahwan, Iyad
Publikováno v:
Nat Hum Behav 7, 1855-1868 (2023)
The ability of humans to create and disseminate culture is often credited as the single most important factor of our success as a species. In this Perspective, we explore the notion of machine culture, culture mediated or generated by machines. We ar
Externí odkaz:
http://arxiv.org/abs/2311.11388
Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In resp
Externí odkaz:
http://arxiv.org/abs/2310.12928
Today, using Large-scale generative Language Models (LLMs) it is possible to simulate free responses to interview questions like those traditionally analyzed using qualitative research methods. Qualitative methodology encompasses a broad family of te
Externí odkaz:
http://arxiv.org/abs/2309.06364
Autor:
Mao, Yiran, Reinecke, Madeline G., Kunesch, Markus, Duéñez-Guzmán, Edgar A., Comanescu, Ramona, Haas, Julia, Leibo, Joel Z.
Is it possible to evaluate the moral cognition of complex artificial agents? In this work, we take a look at one aspect of morality: `doing the right thing for the right reasons.' We propose a behavior-based analysis of artificial moral cognition whi
Externí odkaz:
http://arxiv.org/abs/2305.18269