no code implementations • 11 Apr 2024 • Marvin Pafla, Kate Larson, Mark Hancock
The field of eXplainable artificial intelligence (XAI) has produced a plethora of methods (e. g., saliency-maps) to gain insight into artificial intelligence (AI) models, and has exploded with the rise of deep learning (DL).
Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1
no code implementations • 30 Jan 2024 • Ben Armstrong, Kate Larson
We argue that there is a strong connection between ensemble learning and a delegative voting paradigm -- liquid democracy -- that can be leveraged to reduce ensemble training costs.
1 code implementation • 5 Dec 2023 • Marc Lanctot, Kate Larson, Yoram Bachrach, Luke Marris, Zun Li, Avishkar Bhoopchand, Thomas Anthony, Brian Tanner, Anna Koop
We argue that many general evaluation problems can be viewed through the lens of voting theory.
no code implementations • 28 Jun 2023 • David Radke, Kate Larson, Tim Brecht, Kyle Tilbury
While it has long been recognized that a team of individual learning agents can be greater than the sum of its parts, recent work has shown that larger teams are not necessarily more effective than smaller ones.
no code implementations • 13 Mar 2023 • Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki
A central design problem in game theoretic analysis is the estimation of the players' utilities.
no code implementations • 1 Feb 2023 • Zun Li, Marc Lanctot, Kevin R. McKee, Luke Marris, Ian Gemp, Daniel Hennes, Paul Muller, Kate Larson, Yoram Bachrach, Michael P. Wellman
Multiagent reinforcement learning (MARL) has benefited significantly from population-based and game-theoretic training regimes.
1 code implementation • 26 Jan 2023 • Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning.
no code implementations • 22 Sep 2022 • Ian Gemp, Thomas Anthony, Yoram Bachrach, Avishkar Bhoopchand, Kalesha Bullard, Jerome Connor, Vibhavari Dasagi, Bart De Vylder, Edgar Duenez-Guzman, Romuald Elie, Richard Everett, Daniel Hennes, Edward Hughes, Mina Khan, Marc Lanctot, Kate Larson, Guy Lever, SiQi Liu, Luke Marris, Kevin R. McKee, Paul Muller, Julien Perolat, Florian Strub, Andrea Tacchetti, Eugene Tarassov, Zhe Wang, Karl Tuyls
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
no code implementations • 4 May 2022 • David Radke, Kate Larson, Tim Brecht
For problems requiring cooperation, many multiagent systems implement solutions among either individual agents or across an entire population towards a common goal.
no code implementations • 15 Apr 2022 • David Radke, Kate Larson, Tim Brecht
We propose a model for multi-objective optimization, a credo, for agents in a system that are configured into multiple groups (i. e., teams).
1 code implementation • 26 Oct 2021 • Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson, Mark Crowley
In the last decade, there have been significant advances in multi-agent reinforcement learning (MARL) but there are still numerous challenges, such as high sample complexity and slow convergence to stable policies, that need to be overcome before wide-spread deployment is possible.
no code implementations • 27 Sep 2021 • Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki
In order to enable autonomous vehicles (AV) to navigate busy traffic situations, in recent years there has been a focus on game-theoretic models for strategic behavior planning in AVs.
no code implementations • 20 Sep 2021 • Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki
While there has been an increasing focus on the use of game theoretic models for autonomous driving, empirical evidence shows that there are still open questions around dealing with the challenges of common knowledge assumptions as well as modeling bounded rationality.
no code implementations • 15 Dec 2020 • Allan Dafoe, Edward Hughes, Yoram Bachrach, Tantum Collins, Kevin R. McKee, Joel Z. Leibo, Kate Larson, Thore Graepel
We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.
no code implementations • 27 Nov 2020 • Thomas Ma, Vijay Menon, Kate Larson
Most work on such problems assume that the agents only have ordinal preferences and usually the goal in them is to compute a matching that satisfies some notion of economic efficiency.
no code implementations • 30 Jul 2020 • Vijay Menon, Kate Larson
To address this, we introduce a notion of algorithmic stability and study it in the context of fair and efficient allocations of indivisible goods among two agents.
no code implementations • 1 Mar 2017 • Hadi Hosseini, Kate Larson, Robin Cohen
One-sided matching mechanisms are fundamental for assigning a set of indivisible objects to a set of self-interested agents when monetary transfers are not allowed.
no code implementations • 4 Mar 2015 • Hadi Hosseini, Kate Larson, Robin Cohen
For assignment problems where agents, specifying ordinal preferences, are allocated indivisible objects, two widely studied randomized mechanisms are the Random Serial Dictatorship (RSD) and Probabilistic Serial Rule (PS).
no code implementations • 12 Sep 2013 • Arthur Carvalho, Stanko Dimitrov, Kate Larson
Experimental results show that encouraging honest reporting through the proposed scoring method creates more accurate reviews than the traditional peer-review process.
no code implementations • 22 Aug 2013 • Tri Kurniawan Wijaya, Kate Larson, Karl Aberer
Recent work has suggested reducing electricity generation cost by cutting the peak to average ratio (PAR) without reducing the total amount of the loads.