Michael Bowling

Brett Browning, Manuela M. Veloso, Michael H. Bowling, 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

Rational Learning of Mixed Equilibria in Stochastic Games

M. Veloso, Michael Bowling, 2000 .

The lemonade stand game competition: solving unsolvable games

Michael H. Bowling, Martin Zinkevich, Michael Wunder, 2011, SECO.

Boosting Expert Ensembles for Rapid Concept Recall

Achim Rettinger, Michael H. Bowling, Martin Zinkevich, 2006, AAAI.

Variance Reduction in Monte-Carlo Tree Search

Joel Veness, Michael H. Bowling, Marc Lanctot, 2011, NIPS.

Scalable Learning in Stochastic Games

Manuela Veloso, Michael Bowling, M. Veloso, 2002 .

Convergence Problems of General-Sum Multiagent Reinforcement Learning

Michael H. Bowling, Michael Bowling, 2000, ICML.

Anticipation: A Key for Collaboration in a Team of Agents

Peter Stone, Manuela Veloso, Michael Bowling, 1999 .

Monte Carlo Tree Search in Continuous Action Spaces with Execution Uncertainty

Michael H. Bowling, Viliam Lisý, Timothy Yee, 2016, IJCAI.

Convergence and No-Regret in Multiagent Learning

Michael H. Bowling, Michael Bowling, 2004, NIPS.

Solving Heads-Up Limit Texas Hold'em

Michael H. Bowling, Neil Burch, Michael Johanson, 2015, IJCAI.

Coordination and Adaptation in Impromptu Teams

Michael H. Bowling, Peter McCracken, Michael Bowling, 2005, AAAI.

Plays as Effective Multiagent Plans Enabling Opponent-Adaptive Play Selection

Brett Browning, Manuela M. Veloso, Michael H. Bowling, 2004, ICAPS.

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker pdf

Kevin Waugh, Michael H. Bowling, Viliam Lisý, 2017, ArXiv.

Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis

Susan A. Murphy, Michael H. Bowling, Daniel J. Lizotte, 2010, ICML.

Existence of Multiagent Equilibria with Limited Agents pdf

Manuela M. Veloso, Michael H. Bowling, M. Veloso, 2004, J. Artif. Intell. Res..

An Analysis of Stochastic Game Theory for Multiagent Reinforcement Learning

Manuela Veloso, Michael Bowling, M. Veloso, 2000 .

STP: Skills, tactics, and plays for multi-robot control in adversarial environments

Brett Browning, Manuela Veloso, Michael Bowling, 2005 .

Bayesian Learning of Recursively Factored Environments

Marc G. Bellemare, Joel Veness, Michael H. Bowling, 2013, ICML.

University of Alberta Using State Estimation for Dynamic Agent Modelling

Carl F Bard, Michael Bowling, Martin A. Zinkevich, 2022 .

IJCAI-07 Reviewers

Wolfram Burgard, Olivier Buffet, Christian Balkenius, 2007, IJCAI.

North Carolina Children and Youth Fitness Study

Michael Bowling, F. Ormond, K. Davis, 1994 .

The Alberta Plan for AI Research

R. Sutton, Michael Bowling, P. Pilarski, 2022, ArXiv.

Learning Curricula for Humans: An Empirical Study with Puzzles from The Witness

Levi H. S. Lelis, Michael Bowling, Nathan R Sturtevant, 2022, IJCAI.

The Alberta Plan for AI Research

R. Sutton, Michael Bowling, P. Pilarski, 2022, ArXiv.

Over-communicate no more: Situated RL agents learn concise communication protocols

Michael Bowling, P. Pilarski, Florian Strub, 2022, ArXiv.

Settling the Reward Hypothesis

John D. Martin, Michael Bowling, Will Dabney, 2022, ICML.

Multi‐Agent Planning in the Presence of Multiple Goals

Manuela Veloso, Michael Bowling, Rune Møller Jensen, 2006 .

A Formalization of Equilibria for Multiagent Planning

Manuela M. Veloso, Michael H. Bowling, Rune M. Jensen, 2003, IJCAI.

Multiagent learning in the presence of agents with limitations

Manuela Veloso, Michael Bowling, M. Veloso, 2003 .

Policy Tree: Adaptive Representation for Policy Gradient

Erik Talvitie, Michael H. Bowling, Ujjwal Das Gupta, 2015, AAAI.

Alignment based kernel learning with a continuous set of base kernels

Michael H. Bowling, Csaba Szepesvári, Arash Afkanpour, 2011, Machine Learning.

Learning Subjective Representations for Planning

Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson, 2005, IJCAI.

Optimal Estimation of Multivariate ARMA Models

Martha White, Dale Schuurmans, Michael H. Bowling, 2015, AAAI.

AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games pdf

Michael H. Bowling, Martin Schmid, Matej Moravcik, 2016, AAAI.

Eqilibrium Approximation Quality of Current No-Limit Poker Bots pdf

Michael H. Bowling, Viliam Lisý, Michael Bowling, 2016, AAAI Workshops.

Machine learning and games

Johannes Fürnkranz, Michael H. Bowling, Thore Graepel, 2006, Machine Learning.

Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games

Jonathan Schaeffer, Michael H. Bowling, Darse Billings, 2004, Computers and Games.

Dual Representations for Dynamic Programming and Reinforcement Learning

Tao Wang, D. Schuurmans, M. Bowling, 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

Ease-of-Teaching and Language Structure from Emergent Communication

Michael Bowling, Fushan Li, Michael Bowling, 2019, NeurIPS.

Linear fitted-Q iteration with multiple reward functions

Susan A. Murphy, Michael H. Bowling, Daniel J. Lizotte, 2013, J. Mach. Learn. Res..

Variance Reduction via Antithetic Markov Chains

Dale Schuurmans, Michael H. Bowling, James Neufeld, 2015, AISTATS.

The Alberta Plan for AI Research

R. Sutton, Michael Bowling, P. Pilarski, 2022, ArXiv.

Search in Imperfect Information Games Using Online Monte Carlo Counterfactual Regret Minimization

Michael Bowling, Marc Lanctot, Viliam Lisy, 2014, AAAI 2014.

Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games

Michael H. Bowling, Viliam Lisý, Marc Lanctot, 2015, AAMAS.

Multi-robot team response to a multi-robot opponent team

Brett Browning, Manuela M. Veloso, Michael H. Bowling, 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

Low-Variance and Zero-Variance Baselines for Extensive-Form Games

Martin Schmid, Michael Bowling, Trevor Davis, 2019, ICML.

Abstraction pathologies in extensive games

Kevin Waugh, Duane Szafron, Michael H. Bowling, 2009, AAMAS.

Action Selection for Hammer Shots in Curling

Michael H. Bowling, Robert C. Holte, Zaheen Farraz Ahmad, 2016, IJCAI.

Compact, Convex Upper Bound Iteration for Approximate POMDP Planning

Tao Wang, Dale Schuurmans, Pascal Poupart, 2006, AAAI.

Do pokers players know how good they are? Accuracy of poker skill estimation in online and offline players

Michael Bowling, Nolan Bard, David C. Hodgins, 2014, Comput. Hum. Behav..

Solving Games with Functional Regret Estimation

Kevin Waugh, Michael H. Bowling, J. Andrew Bagnell, 2014, AAAI Workshop: Computer Poker and Imperfect Information.

Using Response Functions to Measure Strategy Strength

Michael H. Bowling, Neil Burch, Trevor Davis, 2014, AAAI.

Subset Selection of Search Heuristics

Nathan R. Sturtevant, Michael H. Bowling, D. Chris Rayner, 2013, IJCAI.

The Forget-me-not Process

Joel Veness, Michael H. Bowling, Demis Hassabis, 2016, NIPS.

Generalized Sampling and Variance in Counterfactual Regret Minimization

Duane Szafron, Michael H. Bowling, Neil Burch, 2012, AAAI.

Bounding the Suboptimality of Reusing Subproblem

Manuela M. Veloso, Michael H. Bowling, M. Veloso, 1999, IJCAI.

Automating Collusion Detection in Sequential Games

Christopher Archibald, Michael H. Bowling, Parisa Mazrooei, 2013, AAAI.

Finding Optimal Abstract Strategies in Extensive-Form Games

Michael H. Bowling, Neil Burch, Nolan Bard, 2012, AAAI.

Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization

Michael H. Bowling, Marc Lanctot, Nolan Bard, 2012, AAMAS.

Strategy Grafting in Extensive Games

Kevin Waugh, Michael H. Bowling, Nolan Bard, 2009, NIPS.

Learning a Value Analysis Tool for Agent Evaluation

Martha White, Michael H. Bowling, Michael Bowling, 2009, IJCAI.

Strategy evaluation in extensive games with importance sampling

Duane Szafron, Michael H. Bowling, Neil Burch, 2008, ICML '08.

Learning Purposeful Behaviour in the Absence of Rewards pdf

Marlos C. Machado, Michael H. Bowling, Michael Bowling, 2016, ArXiv.

Stable Dual Dynamic Programming

Tao Wang, Dale Schuurmans, Michael H. Bowling, 2007, NIPS.

Tractable Objectives for Robust Policy Optimization

Michael H. Bowling, Katherine Chen, Michael Bowling, 2012, NIPS.

Online implicit agent modelling

Michael H. Bowling, Neil Burch, Nolan Bard, 2013, AAMAS.

Robust game play against unknown opponents

Nathan R. Sturtevant, Michael H. Bowling, Michael Bowling, 2006, AAMAS '06.

Approximate Linear Programming for Constrained Partially Observable Markov Decision Processes

Kee-Eung Kim, Pascal Poupart, Michael H. Bowling, 2015, AAAI.

The CMUnited-98 champion small-robot team

Manuela M. Veloso, Peter Stone, Michael H. Bowling, 1998, Adv. Robotics.

Settling the Reward Hypothesis

John D. Martin, Michael Bowling, Will Dabney, 2022, ICML.

Autonomous geocaching: navigation and goal finding in outdoor domains

Michael H. Bowling, Jason Roberts, Adam Milstein, 2008, AAMAS.

Incremental Least-Squares Temporal Difference Learning

Alborz Geramifard, Michael H. Bowling, Richard S. Sutton, 2006, AAAI.

Online Discovery and Learning of Predictive State Representations

Michael H. Bowling, Peter McCracken, Michael Bowling, 2005, NIPS.

Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents (Extended Abstract)

Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, 2018, IJCAI.

Evaluating state-space abstractions in extensive-form games

Michael H. Bowling, Richard Anthony Valenzano, Neil Burch, 2013, AAMAS.

DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

Michael Bradley Johanson, Michael Bowling, Nolan Bard, 2017, ArXiv.

Bayesian Calibration for Monte Carlo Localization

Michael H. Bowling, Petr Musílek, Armita Kaboli, 2006, AAAI.

Subjective Localization with Action Respecting Embedding

Michael H. Bowling, Ali Ghodsi, Dana F. Wilkinson, 2005, ISRR.