Search Results for author: Kate Larson

Found 19 papers, 3 papers with code

Liquid Democracy for Low-Cost Ensemble Pruning

no code implementations30 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.

Ensemble Learning Ensemble Pruning

Evaluating Agents using Social Choice Theory

1 code implementation5 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.

Towards a Better Understanding of Learning with Multiagent Teams

no code implementations28 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.

Revealed Multi-Objective Utility Aggregation in Human Driving

no code implementations13 Mar 2023 Atrisha Sarkar, Kate Larson, Krzysztof Czarnecki

A central design problem in game theoretic analysis is the estimation of the players' utilities.

Decision Making

Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning

1 code implementation26 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.

Multi-agent Reinforcement Learning Q-Learning +2

Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments

no code implementations22 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.

reinforcement-learning Reinforcement Learning (RL)

Exploring the Benefits of Teams in Multiagent Learning

no code implementations4 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.

Reinforcement Learning (RL)

The Importance of Credo in Multiagent Learning

no code implementations15 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).

reinforcement-learning Reinforcement Learning (RL)

Multi-Agent Advisor Q-Learning

1 code implementation26 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.

Decision Making Multi-agent Reinforcement Learning +3

A taxonomy of strategic human interactions in traffic conflicts

no code implementations27 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.

Autonomous Vehicles Navigate

Generalized dynamic cognitive hierarchy models for strategic driving behavior

no code implementations20 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.

Autonomous Driving

Open Problems in Cooperative AI

no code implementations15 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.

Scheduling

Improving Welfare in One-sided Matching using Simple Threshold Queries

no code implementations27 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.

Object

Algorithmic Stability in Fair Allocation of Indivisible Goods Among Two Agents

no code implementations30 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.

Fairness Vocal Bursts Valence Prediction

Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes

no code implementations1 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.

Random Serial Dictatorship versus Probabilistic Serial Rule: A Tale of Two Random Mechanisms

no code implementations4 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).

Inducing Honest Reporting Without Observing Outcomes: An Application to the Peer-Review Process

no code implementations12 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.

Matching Demand with Supply in the Smart Grid using Agent-Based Multiunit Auction

no code implementations22 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.

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