Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Tacchetti, Andrea"'
From social networks to traffic routing, artificial learning agents are playing a central role in modern institutions. We must therefore understand how to leverage these systems to foster outcomes and behaviors that align with our own values and aspi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3bd0592c0370dad5eccf7fb01ca9dc4
http://arxiv.org/abs/2202.10135
http://arxiv.org/abs/2202.10135
Autor:
Koster, Raphael, Balaguer, Jan, Tacchetti, Andrea, Weinstein, Ari, Zhu, Tina, Hauser, Oliver, Williams, Duncan, Campbell-Gillingham, Lucy, Thacker, Phoebe, Botvinick, Matthew, Summerfield, Christopher
Building artificial intelligence (AI) that aligns with human values is an unsolved problem. Here, we developed a human-in-the-loop research pipeline called Democratic AI, in which reinforcement learning is used to design a social mechanism that human
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::54dff9098ba04e24bac5d0f086e8b9f8
http://arxiv.org/abs/2201.11441
http://arxiv.org/abs/2201.11441
Autor:
Balaguer, Jan, Koster, Raphael, Weinstein, Ari, Campbell-Gillingham, Lucy, Summerfield, Christopher, Botvinick, Matthew, Tacchetti, Andrea
Artificial learning agents are mediating a larger and larger number of interactions among humans, firms, and organizations, and the intersection between mechanism design and machine learning has been heavily investigated in recent years. However, mec
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b1a5e12b2671c70dd421e4476b9287cc
Solution concepts such as Nash Equilibria, Correlated Equilibria, and Coarse Correlated Equilibria are useful components for many multiagent machine learning algorithms. Unfortunately, solving a normal-form game could take prohibitive or non-determin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f7e1f7e7e2bdcbf75a4458c1fd8255c
Autor:
Anthony, Thomas, Eccles, Tom, Tacchetti, Andrea, Kram��r, J��nos, Gemp, Ian, Hudson, Thomas C., Porcel, Nicolas, Lanctot, Marc, P��rolat, Julien, Everett, Richard, Werpachowski, Roman, Singh, Satinder, Graepel, Thore, Bachrach, Yoram
Recent advances in deep reinforcement learning (RL) have led to considerable progress in many 2-player zero-sum games, such as Go, Poker and Starcraft. The purely adversarial nature of such games allows for conceptually simple and principled applicat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7983d880dc1c37fc2831db60f261156b
http://arxiv.org/abs/2006.04635
http://arxiv.org/abs/2006.04635
Autor:
Gemp, Ian, McKee, Kevin R., Everett, Richard, Duéñez-Guzmán, Edgar A., Bachrach, Yoram, Balduzzi, David, Tacchetti, Andrea
In multiagent systems, the complex interaction of fixed incentives can lead agents to outcomes that are poor (inefficient) not only for the group, but also for each individual. Price of anarchy is a technical, game-theoretic definition that quantifie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6daa844720d113c6ef05407e2d8d07c
One of the fundamental challenges of governance is deciding when and how to intervene in multi-agent systems in order to impact group-wide metrics of success. This is particularly challenging when proposed interventions are novel and expensive. For e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ef181915b768f8f5ae3be87435215a49
From social networks to supply chains, more and more aspects of how humans, firms and organizations interact is mediated by artificial learning agents. As the influence of machine learning systems grows, it is paramount that we study how to imbue our
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::829e10859255468cf64a681dee0d98da
Autor:
Tacchetti, Andrea, Song, H. Francis, Mediano, Pedro A. M., Zambaldi, Vinicius, Rabinowitz, Neil C., Graepel, Thore, Botvinick, Matthew, Battaglia, Peter W.
The behavioral dynamics of multi-agent systems have a rich and orderly structure, which can be leveraged to understand these systems, and to improve how artificial agents learn to operate in them. Here we introduce Relational Forward Models (RFM) for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56ded66bc09793f201d08b283eb50f90
http://arxiv.org/abs/1809.11044
http://arxiv.org/abs/1809.11044