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2020 – today
- 2023
[j7]Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas Anthony, Julien Pérolat:
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. Trans. Mach. Learn. Res. 2023 (2023)- [c35]Vojtech Kovarík, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý:
Rethinking Formal Models of Partially Observable Multiagent Decision Making (Extended Abstract). IJCAI 2023: 6920-6924 - [i19]Marc Lanctot, John Schultz, Neil Burch, Max Olan Smith, Daniel Hennes, Thomas W. Anthony, Julien Pérolat:
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning. CoRR abs/2303.03196 (2023) - [i18]Ondrej Kubícek, Neil Burch, Viliam Lisý:
Look-ahead Search on Top of Policy Networks in Imperfect Information Games. CoRR abs/2312.15220 (2023) - 2022
- [j6]Vojtech Kovarík
, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý:
Rethinking formal models of partially observable multiagent decision making. Artif. Intell. 303: 103645 (2022) - [c34]Finbarr Timbers, Nolan Bard, Edward Lockhart, Marc Lanctot, Martin Schmid, Neil Burch, Julian Schrittwieser, Thomas Hubert, Michael Bowling:
Approximate Exploitability: Learning a Best Response. IJCAI 2022: 3487-3493 - [i17]Julien Pérolat, Bart De Vylder, Daniel Hennes, Eugene Tarassov, Florian Strub, Vincent de Boer, Paul Muller, Jerome T. Connor, Neil Burch, Thomas W. Anthony, Stephen McAleer, Romuald Elie, Sarah H. Cen, Zhe Wang, Audrunas Gruslys, Aleksandra Malysheva, Mina Khan, Sherjil Ozair, Finbarr Timbers, Toby Pohlen, Tom Eccles, Mark Rowland, Marc Lanctot, Jean-Baptiste Lespiau, Bilal Piot, Shayegan Omidshafiei, Edward Lockhart, Laurent Sifre, Nathalie Beauguerlange, Rémi Munos, David Silver, Satinder Singh, Demis Hassabis, Karl Tuyls:
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning. CoRR abs/2206.15378 (2022) - 2021
- [c33]Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot:
Solving Common-Payoff Games with Approximate Policy Iteration. AAAI 2021: 9695-9703 - [c32]Michal Sustr, Martin Schmid, Matej Moravcík, Neil Burch, Marc Lanctot, Michael Bowling:
Sound Algorithms in Imperfect Information Games. AAMAS 2021: 1674-1676 - [c31]Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls:
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. ICML 2021: 8525-8535 - [i16]Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot:
Solving Common-Payoff Games with Approximate Policy Iteration. CoRR abs/2101.04237 (2021) - [i15]Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Joshua Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, G. Zacharias Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling:
Player of Games. CoRR abs/2112.03178 (2021) - 2020
- [j5]Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling:
The Hanabi challenge: A new frontier for AI research. Artif. Intell. 280: 103216 (2020) - [i14]Julien Pérolat, Rémi Munos, Jean-Baptiste Lespiau, Shayegan Omidshafiei, Mark Rowland, Pedro A. Ortega, Neil Burch, Thomas W. Anthony, David Balduzzi, Bart De Vylder, Georgios Piliouras, Marc Lanctot, Karl Tuyls:
From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization. CoRR abs/2002.08456 (2020) - [i13]Michal Sustr, Martin Schmid, Matej Moravcík, Neil Burch, Marc Lanctot, Michael Bowling:
Sound Search in Imperfect Information Games. CoRR abs/2006.08740 (2020) - [i12]Edward Lockhart, Neil Burch, Nolan Bard, Sebastian Borgeaud, Tom Eccles, Lucas Smaira, Ray Smith:
Human-Agent Cooperation in Bridge Bidding. CoRR abs/2011.14124 (2020)
2010 – 2019
- 2019
- [j4]Neil Burch, Matej Moravcik, Martin Schmid:
Revisiting CFR+ and Alternating Updates. J. Artif. Intell. Res. 64: 429-443 (2019) - [c30]Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling:
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games Using Baselines. AAAI 2019: 2157-2164 - [c29]Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling:
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. ICML 2019: 1942-1951 - [i11]Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling:
The Hanabi Challenge: A New Frontier for AI Research. CoRR abs/1902.00506 (2019) - [i10]Vojtech Kovarík, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý:
Rethinking Formal Models of Partially Observable Multiagent Decision Making. CoRR abs/1906.11110 (2019) - 2018
- [c28]Neil Burch, Martin Schmid, Matej Moravcik, Dustin Morrill, Michael Bowling:
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games. AAAI 2018: 949-956 - [i9]Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling:
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines. CoRR abs/1809.03057 (2018) - [i8]Neil Burch, Matej Moravcik, Martin Schmid:
Revisiting CFR+ and Alternating Updates. CoRR abs/1810.11542 (2018) - [i7]Jakob N. Foerster, H. Francis Song, Edward Hughes, Neil Burch, Iain Dunning, Shimon Whiteson, Matthew M. Botvinick, Michael Bowling:
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning. CoRR abs/1811.01458 (2018) - 2017
- [j3]Michael Bowling, Neil Burch, Michael Johanson, Oskari Tammelin:
Heads-up limit hold'em poker is solved. Commun. ACM 60(11): 81-88 (2017) - [c27]Neil Burch, Martin Schmid, Matej Moravcik, Michael Bowling:
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games. AAAI Workshops 2017 - [i6]Matej Moravcík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael H. Bowling:
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker. CoRR abs/1701.01724 (2017) - 2016
- [i5]Neil Burch, Martin Schmid, Matej Moravcik, Michael H. Bowling:
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games. CoRR abs/1612.06915 (2016) - 2015
- [c26]Oskari Tammelin, Neil Burch, Michael Johanson, Michael Bowling:
Solving Heads-Up Limit Texas Hold'em. IJCAI 2015: 645-652 - 2014
- [j2]Robert C. Holte, Neil Burch:
Automatic move pruning for single-agent search. AI Commun. 27(4): 363-383 (2014) - [c25]Neil Burch, Michael Johanson, Michael Bowling:
Solving Imperfect Information Games Using Decomposition. AAAI 2014: 602-608 - [c24]Trevor Davis, Neil Burch, Michael Bowling:
Using Response Functions to Measure Strategy Strength. AAAI 2014: 630-636 - [i4]Uzi Zahavi, Ariel Felner, Neil Burch, Robert C. Holte:
Predicting the Performance of IDA* using Conditional Distributions. CoRR abs/1401.3493 (2014) - 2013
- [c23]Nolan Bard, Michael Johanson, Neil Burch, Michael Bowling:
Online implicit agent modelling. AAMAS 2013: 255-262 - [c22]Michael Johanson, Neil Burch, Richard Anthony Valenzano, Michael Bowling:
Evaluating state-space abstractions in extensive-form games. AAMAS 2013: 271-278 - [c21]Christopher Archibald, Neil Burch, Michael Bowling, Matthew J. Rutherford:
Rating players in games with real-valued outcomes. AAMAS 2013: 1307-1308 - [i3]Neil Burch, Michael Bowling:
CFR-D: Solving Imperfect Information Games Using Decomposition. CoRR abs/1303.4441 (2013) - 2012
- [c20]Richard G. Gibson, Marc Lanctot, Neil Burch, Duane Szafron, Michael Bowling:
Generalized Sampling and Variance in Counterfactual Regret Minimization. AAAI 2012: 1355-1361 - [c19]Michael Johanson, Nolan Bard, Neil Burch, Michael Bowling:
Finding Optimal Abstract Strategies in Extensive-Form Games. AAAI 2012: 1371-1379 - [c18]Marc Lanctot, Richard G. Gibson, Neil Burch, Michael Bowling:
No-Regret Learning in Extensive-Form Games with Imperfect Recall. ICML 2012 - [c17]Richard G. Gibson, Neil Burch, Marc Lanctot, Duane Szafron:
Efficient Monte Carlo Counterfactual Regret Minimization in Games with Many Player Actions. NIPS 2012: 1889-1897 - [c16]Neil Burch, Robert C. Holte:
Automatic Move Pruning Revisited. SOCS 2012: 18-24 - [i2]Marc Lanctot, Richard G. Gibson, Neil Burch, Martin Zinkevich, Michael H. Bowling:
No-Regret Learning in Extensive-Form Games with Imperfect Recall. CoRR abs/1205.0622 (2012) - [i1]Finnegan Southey, Michael Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, D. Chris Rayner:
Bayes' Bluff: Opponent Modelling in Poker. CoRR abs/1207.1411 (2012) - 2011
- [c15]Peter Yap, Neil Burch, Robert C. Holte, Jonathan Schaeffer:
Block A*: Database-Driven Search with Applications in Any-Angle Path-Planning. AAAI 2011: 120-125 - [c14]Peter Kai Yue Yap, Neil Burch, Robert C. Holte, Jonathan Schaeffer:
Any-Angle Path Planning for Computer Games. AIIDE 2011 - [c13]Neil Burch, Robert C. Holte:
Automatic Move Pruning in General Single-Player Games. SOCS 2011: 31-38 - [c12]Peter Kai Yue Yap, Neil Burch, Robert C. Holte, Jonathan Schaeffer:
Abstract: Block A* and Any-Angle Path-Planning. SOCS 2011: 211-212 - 2010
- [j1]Uzi Zahavi, Ariel Felner, Neil Burch, Robert C. Holte:
Predicting the Performance of IDA* using Conditional Distributions. J. Artif. Intell. Res. 37: 41-83 (2010) - [c11]Neil Burch, Robert C. Holte, Martin Müller, David O'Connell, Jonathan Schaeffer:
Automating Layouts of Sewers in Subdivisions. ECAI 2010: 655-660
2000 – 2009
- 2009
- [c10]Michael H. Bowling, Nicholas Abou Risk, Nolan Bard, Darse Billings, Neil Burch, Joshua Davidson, John Alexander Hawkin, Robert Holte, Michael Johanson, Morgan Kan, Bryce Paradis, Jonathan Schaeffer, David Schnizlein, Duane Szafron, Kevin Waugh, Martin Zinkevich:
A demonstration of the Polaris poker system. AAMAS (2) 2009: 1391-1392 - [c9]Nathan R. Sturtevant, Ariel Felner, Max Barer, Jonathan Schaeffer, Neil Burch:
Memory-Based Heuristics for Explicit State Spaces. IJCAI 2009: 609-614 - 2008
- [c8]Uzi Zahavi, Ariel Felner, Neil Burch, Robert C. Holte:
Predicting the Performance of IDA* with Conditional Distributions. AAAI 2008: 381-386 - [c7]Michael H. Bowling, Michael Johanson, Neil Burch, Duane Szafron:
Strategy evaluation in extensive games with importance sampling. ICML 2008: 72-79 - 2007
- [c6]Martin Zinkevich, Michael H. Bowling, Neil Burch:
A New Algorithm for Generating Equilibria in Massive Zero-Sum Games. AAAI 2007: 788-794 - 2005
- [c5]Jonathan Schaeffer, Yngvi Björnsson, Neil Burch, Akihiro Kishimoto, Martin Müller, Robert Lake, Paul Lu, Steve Sutphen:
Solving Checkers. IJCAI 2005: 292-297 - [c4]Finnegan Southey, Michael H. Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, D. Chris Rayner:
Bayes? Bluff: Opponent Modelling in Poker. UAI 2005: 550-558 - 2004
- [c3]Darse Billings, Aaron Davidson, Terence Schauenberg, Neil Burch, Michael H. Bowling, Robert C. Holte, Jonathan Schaeffer, Duane Szafron:
Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games. Computers and Games 2004: 21-34 - 2003
- [c2]Jonathan Schaeffer, Yngvi Björnsson, Neil Burch, Robert Lake, Paul Lu, Steve Sutphen:
Building the Checkers 10-piece Endgame Databases. ACG 2003: 193-210 - [c1]Darse Billings, Neil Burch, Aaron Davidson, Robert C. Holte, Jonathan Schaeffer, Terence Schauenberg, Duane Szafron:
Approximating Game-Theoretic Optimal Strategies for Full-scale Poker. IJCAI 2003: 661-668
Coauthor Index
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