Michael Buro's Publication List

Journal Publications & Book Chapters

• M. Buro and H. Kleine Büning, Report on a SAT Competition, Bulletin of the EATCS 49 (1993)
  
  
• M. Buro, On the Maximum Length of Huffman Codes, Information Processing Letters 45(1993), 219-223
  
  
• M. Buro, ProbCut: An Effective Selective Extension of the Alpha-Beta Algorithm, ICCA Journal 18(2) 1995, 71-76
  
  
• M. Buro, Statistical Feature Combination for the Evaluation of Game Positions , JAIR 3(1995), 373-382
  
  
• M. Buro and H. Kleine Büning, On Resolution with Short Clauses, Annals of Mathematics and Artificial Intelligence 18 (1996) 2-4, 243-260
  
  
• M. Buro, The Othello Match of the Year: Takeshi Murakami vs. Logistello , ICCA Journal 20(3) 1997, 189-193
  
  
• M. Buro, Toward Opening Book Learning, ICCA Journal 22(2) 1999, 98-102, reprinted in: Games in AI Research, H.J. van den Herik, H. Iida (ed.), ISBN: 90-621-6416-1, 2000, and in: Machines That Learn to Play Games, J. Fürnkranz and M. Kubat (ed.), ISBN: 1-59033-021-8, 2001
  
  
• M. Buro, Efficient Approximation of Backgammon Race Equities, ICCA Journal 22(3) 1999, 133-142, reprinted in: Advances in Computer Games 9, H.J. van den Herik, B. Monien (ed.), ISBN 90-6216-566-4, 2001
  
  
• M. Buro, How Machines have Learned to Play Othello, IEEE Intelligent Systems J. 14(6) 1999, 12-14
  
  
• M. Buro, Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello , Games in AI Research, H.J. van den Herik, H. Iida (ed.), ISBN: 90-621-6416-1, 2000
  
  
• M. Buro, Improving Heuristic Mini-Max Search by Supervised Learning , Artificial Intelligence, Vol. 134 (1-2) (2002) pp. 85-99
  
  
• M. Buro, Report On the IWEC-2002 Man-Machine Othello Match , ICGA Journal, Vol. 25, No.2 (June 2002), pp.113-114
  
  
• M. Buro, The Evolution of Strong Othello Programs, in: Entertainment Computing - Technology and Applications, R. Nakatsu and J. Hoshino (ed.), Kluwer 2003, pp. 81-88
  
  
• D. Gomboc, M. Buro, and T. Marsland, Tuning evaluation functions by maximizing concordance, Theoretical Computer Science, Volume 349, Issue 2, pp. 202-229, 2005
  
  
• P.I. Cowling, M. Buro, M. Bida, A. Botea, B. Bouzy, M.V. Butz, P. Hingston, H. Muoz-Avila, D. Nau, and M. Sipper, Search in Real-Time Video Games. In: Artificial and Computational Intelligence in Games, a follow-up to Dagstuhl Seminar 12191, Eds: S.M. Lucas, M. Mateas, M. Preuss, P. Spronck, J. Togelius, pp. 1-19, 2013
  
  
• A. Botea, B. Bouzy, M. Buro, C .Bauckhage, and D. Nau. Pathfinding in Games. In: Artificial and Computational Intelligence in Games, a follow-up to Dagstuhl Seminar 12191, Eds: S.M. Lucas, M. Mateas, M. Preuss, P. Spronck, J. Togelius, pp. 21-31, 2013
  
  
• N. Barriga, M. Stanescu, and M. Buro, Combining Scripted Behavior with Game Tree Search for Stronger, More Robust Game AI (preprint). In: Game AI Pro 3 - Steve Rabin (ed.), ISBN-10: 1498742580, 2017
  
  
• M. Stanescu, N. Barriga, and M. Buro, Combat Outcome Prediction for RTS Games (preprint). In: Game AI Pro 3 - Steve Rabin (ed.), ISBN-10: 1498742580, 2017
  
  
• D. Churchill and M. Buro, Hierarchical Portfolio Search in Prismata (preprint). In: Game AI Pro 3 - Steve Rabin (ed.), ISBN-10: 1498742580, 2017
  
  
• N. Barriga, M. Stanescu, and M. Buro, Game Tree Search Based on Non-Deterministic Action Scripts in Real-Time Strategy Games, IEEE TCIAIG (DOI:10.1109/TCIAIG.2017.2717902), 2017
  
  
• N. Barriga, M. Stanescu, F. Basoain, M. Buro, Improving RTS Game AI by Supervised Policy Learning, Tactical Search, and Deep Reinforcement Learning , IEEE Computational Intelligence Magazine, 14(3):11, 2019

Workshop & Conference Contributions

• M. Buro, Logistello: A Strong Learning Othello Program , 19th Annual Conference Gesellschaft für Klassifikation e.V. (1995), Basel
  
  
• M. Buro, Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello , Workshop on game-tree search, NECI, August 1997
  
  
• M. Buro, Toward Opening Book Learning , IJCAI-97 workshop on computer games.
  
  
• M. Buro, From Simple Features to Sophisticated Evaluation Functions , The First International Conference on Computers and Games (CG'98), Tsukuba, Japan. Published in LNCS volume 1558 (© Springer-Verlag)
  
  
• M. Buro, Efficient Approximation of Backgammon Race Equities , Advances in Computer Games 9 (1999), Paderborn Germany
  
  
• M. Buro, Simple Amazons Endgames and their Connection to Hamilton Circuits in Cubic Subgrid Graphs , The Second International Conference on Computers and Games (CG2000), Hamamatsu Japan, pp.250-261
  
  
• M. Buro and I. Durdanovic, An Overview of NECI's Generic Game Server , Proceedings of The 6th Computer Games Olympiad Workshop (2001), Maastricht, The Netherlands, J.W.H.M. Uiterwijk (editor) pp.34-39 (HTML version), and GPW-2001, Hakone (Japan), 2001
  
  
• M. Buro, The Evolution of Strong Othello Programs, Proceedings of the IWEC-2002 Workshop on Entertainment Computing (2002), Makuhari, Japan
  
  
• M. Buro, ORTS: A Hack-Free RTS Game Environment, Proceedings of the International Computers and Games Conference 2002, Edmonton, Canada. p.280-291
  
  
• M. Buro, Real-Time Strategy Games: A new AI Research Challenge, Proceedings of the International Joint Conference on AI 2003, Acapulco, Mexico, pp.1534--1535
  
  
• D. Gomboc, T.A. Marsland, and M. Buro, Evaluation Function Tuning via Ordinal Correlation, Proceedings of the Advances in Computer Games Conference 10, Graz 2003, pp.1-18
  
  
• M. Buro, Solving the Oshi-Zumo Game, Proceedings of the Advances in Computer Games Conference 10, Graz 2003, pp.361-366
  
  
• A.X. Jiang and M. Buro, First Experimental Results of ProbCut Applied to Chess, Proceedings of the Advances in Computer Games Conference 10, Graz 2003, pp.19-32
  
  
• M. Buro and T. Furtak, RTS Games and Real-Time AI Research, Proceedings of the Behavior Representation in Modeling and Simulation Conference (BRIMS), Arlington VA 2004, pp.34-41
  
  
• T. Hauk, M. Buro, and J. Schaeffer, Rediscovering *-MiniMax Search, Computers and Games Conference, Ramat-Gan 2004, pp.35-50
  
  
• T. Hauk, M. Buro, and J. Schaeffer, *-MiniMax Performance in Backgammon, Computers and Games Conference, Ramat-Gan 2004, pp.61-66
  
  
• M. Buro, Call for AI Research in RTS Games, Proceedings of the AAAI-04 workshop on AI in games, San Jose 2004, pp.139-142
  
  
• M. Chung, M. Buro, and J. Schaeffer, Monte Carlo Planning in RTS Games, CIG 2005, Colchester, UK 2005
  
  
• A. Kovarsky and M. Buro, Heuristic Search Applied to Abstract Combat Games, Proceedings of the The Eighteenth Canadian Conference on Artificial Intelligence, Victoria 2005
  
  
• T. Furtak, M. Kiyomi, T. Uno, and M. Buro, Generalized Amazons is PSPACE-Complete, IJCAI, Edinburgh, 2005, pp.132-137
  
  
• N.R. Sturtevant and M. Buro, Partial Pathfinding Using Map Abstraction and Refinement, AAAI, Pittsburgh, 2005, pp.1392-1397
  
  
• M. Buro and T. Furtak, On the Development of a Free RTS Game Engine, GameOn'NA Conference, Montreal, 2005, pp.23-27
  
  
• D. Demyen and M. Buro, Efficienta Triangulation-Based Pathfinding. Proceedings of the AAAI conference, Boston 2006, pp.942-947
  
  
• N.R. Sturtevant and M. Buro, Improving Collaborative Pathfinding Using Map Abstraction. Proceedings of the AIIDE conference, Marina del Rey 2006, pp.45-50
  
  
• M. Buro, J. Bergsma, D. Deutscher, T. Furtak, F. Sailer, D. Tom, and N. Wiebe, AI System Designs for the First RTS-Game AI Competition, Proceedings of the GameOn Conference, Braunschweig Germany, 2006, pp.13-17
  
  
• A. Kovarsky and M. Buro, A First Look at Build-Order Optimization in Real-Time Strategy Games, Proceedings of the GameOn Conference, Braunschweig Germany, 2006, pp.18-22
  
  
• F. Sailer, M. Buro, and M. Lanctot, Adversarial Planning Through Strategy Simulation, CIG, Hawaii USA, 2007
  
  
• M.R. Jansen and M. Buro, HPA* Enhancements, AIIDE, Stanford USA, 2007
  
  
• M. Buro and A. Kovarsky, Concurrent Action Execution with Shared Fluents, AAAI, Vancouver Canada, 2007
  
  
• T. Furtak and M. Buro, Minimum Proof Graphs and Fastest-Cut-First Search Heuristics, IJCAI, Pasadena USA, 2009, pp. 492-498
  
  
• M. Buro, J.R. Long, T. Furtak, and N.R. Sturtevant, Improving State Evaluation, Inference, and Search in Trick-Based Card Games, IJCAI, Pasadena USA, 2009, pp. 1407-1413
  
  
• J.R. Long, N.R. Sturtevant, M. Buro, and T. Furtak, Understanding the Success of Perfect Information Monte Carlo Sampling in Game Tree Search, AAAI, 2010
  
  
• T. Furtak and M. Buro, On the Complexity of Two-Player Attrition Games Played on Graphs, AIIDE, Stanford USA, 2010
  
  
• J.R. Long and M. Buro, Real-Time Opponent Modelling in Trick-Taking Card Games, IJCAI, Barcelona, 2011, 6 pages
  
  
• T. Furtak and M. Buro, Using Payoff-Similarity to Speed Up Search, IJCAI, Barcelona, 2011, 6 pages
  
  
• D. Churchill and M. Buro, Build Order Optimization in StarCraft, AIIDE, Stanford, 2011, 6 pages
  
  
• A. Saffidine, H. Finnsson, and M. Buro, Alpha-Beta Pruning for Games with Simultaneous Moves, AAAI, Toronto, 2012, pp. 556-562
  
  
• D. Churchill, Abdallah Saffidine, and M. Buro, Fast Heuristic Search for RTS Game Combat Scenarios, AIIDE, Stanford, 2012, pp. 112-117
  
  
• D. Churchill and M. Buro, Incorporating Search Algorithms into RTS Game Agents, AIIDE Workshop on Artificial Intelligence in Adversarial Real-Time Games, Stanford, 2012, 6 pages
  
  
• T. Furtak and M. Buro, Recursive Monte Carlo Search for Imperfect Information Games, CIG 2013, Niagara Falls, Canada, 8 pages
  
  
• D. Churchill and M. Buro, Portfolio Greedy Search and Simulation for Large-Scale Combat in Starcraft, CIG 2013, Niagara Falls, Canada, 8 pages
  
  
• M. Stanescu, S. Poo Hernandez, G. Erickson, R. Greiner and M. Buro, Predicting Army Combat Outcomes in StarCraft, AIIDE 2013, 7 pages.
  
  
• N. Barriga, M. Stanescu, and M. Buro, Parallel UCT Search on GPUs, IEEE Conference on Computational Intelligence and Games (CIG), 2014
  
  
• M. Stanescu, N. Barriga, and M. Buro. Introducing Hierarchical Adversarial Search, a Scalable Search Procedure for Real-Time Strategy Games (poster), European Conference on AI (ECAI), 2014
  
  
• M. Stanescu, N. Barriga, and M. Buro, Hierarchical Adversarial Search Applied to Real-Time Strategy Games. Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2014
  
  
• G. Erickson and M. Buro, Global State Evaluation in StarCraft. Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2014
  
  
• N. Barriga, M. Stanescu, and M. Buro, Building Placement Optimization in Real-Time Strategy Games, Workshop on Artificial Intelligence in Adversarial Real-Time Games, AIIDE, 2014
  
  
• S. Ontañón and M. Buro, Adversarial Hierarchical-Task Network Planning for Complex Real-Time Games, Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)
  
  
• N. Barriga, M. Stanescu, and M. Buro, Puppet Search: Enhancing Scripted Behavior by Look-Ahead Search with Applications to Real-Time Strategy Games, Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2015
  
  
• M. Stanescu, N. Barriga, and M. Buro, Using Lanchester Attrition Laws for Combat Prediction in StarCraft, Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2015
  
  
• D. Churchill and M. Buro, Hierarchical Portfolio Search: Prismata's Robust AI Architecture for Games with Large Search Spaces , Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2015
  
  
• D. Schneider and M. Buro, StarCraft Unit Motion: Analysis and Search Enhancements , Third Workshop on Artificial Intelligence in Adversarial Real-Time Games at AIIDE, 2015
  
  
• M. Stanescu, N.A. Barriga, A. Hess, and M. Buro, Evaluating Real-Time Strategy Game States Using Convolutional Neural Networks, IEEE Conference on Computational Intelligence and Games (CIG), 2016
  
  
• N.A. Barriga, M. Stanescu, and M. Buro, Combining Strategic Learning and Tactical Search in Real-Time Strategy Games, Artificial Intelligence and Interactive Digital Entertainment Conference (AIIDE), 2017
  
  
• S. Zhang and M. Buro, Improving Hearthstone AI by Learning High-Level Rollout Policies and Bucketing Chance Node Events, IEEE Conference on Computational Intelligence and Games (CIG), 2017
  
  
• M. Stanescu and M. Buro, Spatial Action Decomposition Learning Applied to RTS Combat Games, AIIDE Workshop on Artificial Intelligence for Strategy Games, 2018
  
  
• D. Rebstock, C. Solinas, M. Buro, Learning Policies from Human Data for Skat , RL in Games Workshop at AAAI 2019 (non-archived workshop paper - see below for archived conference version)
  
  
• C. Solinas, D. Rebstock, M. Buro, Improving Search with Supervised Learning in Trick-Based Card Games , AAAI 2019
  
  
• D. Churchill, M. Buro, R. Kelly, Robust Continuous Build-Order Optimization in StarCraft , IEEE CoG 2019
  
  
• D. Rebstock, C. Solinas, M. Buro, N. Sturtevant, Policy Based Inference in Trick-Taking Card Games , IEEE CoG 2019
  
  
• D. Rebstock, C. Solinas, M. Buro, Learning Policies from Human Data for Skat , IEEE CoG 2019
  
  
• A. Seify and M. Buro, Single-Agent Optimization Through Policy Iteration Using Monte-Carlo Tree Search , AAAI 2020 Workshop on Reinforcement Learning in Games
  
  
• V. Bhatt and M. Buro, Inference-Based Deterministic Messaging for Multi-Agent Communication, AAAI 2021
  
  
• J. Tuero and M. Buro, Bayes DistNet - A Robust Neural Network for Algorithm Runtime Distribution Predictions, AAAI 2021
  
  
• W. Feng, L. Lan, M. Buro, and Z. Luo, Online Mulitple-Pedestrian Tracking with Detection-Pair-Based Graph Convolutional Networks, IEEE JIoT 2022
  
  
• C. Solinas, D. Rebstock, N.R. Sturtevant, and M. Buro, History Filtering in Imperfect Information Games: Algorithms and Complexity, NeuRIPS 2023

Theses

• M. Buro, A contribution to the determination of Rado's Sigma(5) - or - How to catch busy beavers?, Diploma thesis (1990) in German, Technical University of Aachen, Germany
  
  
• M. Buro, Methods for the evaluation of game positions using examples, Ph.D. thesis (1994) in German, University of Paderborn, Germany

Theses I Supervised

Technical Reports

• M. Buro, A contribution to the determination of Rado's Sigma(5) - or - How to catch busy beavers?, Computer Science Technical Report 146 (1990), RWTH-Aachen, Germany
  
  
• M. Buro and H. Kleine Büning, Report on a SAT Competition, Technical Report tr-ri-92-110 (1992), University of Paderborn, Germany;
  
  
• M. Buro (Editor), The First International Paderborn Computer Othello Tournament, Technical Report tr-ri-94-141 (1994), University of Paderborn, Germany
  
  
• M. Buro, An Evaluation Function for Othello Based on Statistics , NECI Technical Report #31 (1997)
  
  
• M. Buro, Toward Opening Book Learning , NECI Technical Note #2 (1997)
  
  
• M. Buro, Experiments with Multi-ProbCut and a New High-Quality Evaluation Function for Othello , NECI Technical Report #96 (1997)
  
  
• M. Buro, The Othello Match of the Year: Takeshi Murakami vs. Logistello , NECI Technical Note #012N (1997)
  
  
• M. Buro, From Simple Features to Sophisticated Evaluation Functions , NECI Technical Report #60 (1998)
  
  
• M. Buro, Efficient Approximation of Backgammon Race Equities , NECI Technical Report #34 (1999)
  
  
• M. Buro and I. Durdanovic, GSA (Generic Server Architecture), NECI Software Release #SW-0025-C (1999)
  
  
• M. Buro, Simple Amazons Endgames and their Connection to Hamilton Circuits in Cubic Subgrid Graphs , NECI Technical Report #71 (2000)
  
  
• M. Buro, Improving Heuristic Mini-Max Search by Supervised Learning , NECI Technical Report #106 (2000)
  
  
• M. Buro and I. Durdanovic, An Overview of NECI's Generic Game Server, NECI Technical Report #74 (2001), HTML version
  
  
• M. Buro, ORTS: A Hack-Free RTS Game Environment, NECI Technical Report #36 (2002)
  
  

Honors and Invited Talks

• The 1996 ICCA Journal Award for the article "ProbCut: An Effective Selective Extension of the Alpha-Beta Algorithm", ICCA Journal 18(2) 1995, 71-76
  
  
• Participation in the "Hall of Champions" event at AAAI-97
  
  
• "How Machines have Learned to Play Othello", Invited talk at GPW'97, Hakone, Japan (1997)
  
  
• "Logistello - A Strong Learning Othello Program", Invited lecture at Nihon University, Tokyo, Japan (1997)
  
  
• "Efficient Approximation of Backgammon Race Equities", Invited talk at University of Paderborn, Germany (1999)
  
  
• "Is one neuron really sufficient to play games at world-champion level?", Invited talk at the workshop on Machine Learning in Games, ICML-99
  
  
• "How Machines have Learned to Play Othello", Invited plenary talk at IJCNN'99, Washington D.C.
  
  
• "Features are More Important than Weights", Invited tutorial at IJCNN'99, Washington D.C.
  
  
• "Is one neuron sufficient to play games at world-champion level?", Invited talk at University of Massachusetts, November 1999
  
  
• "Machine Learning applied to Heuristic Mini-Max Search", Invited talk at University of Alberta, March 2001
  
  
• "Making machines smarter, not just faster.", Invited talk at University of Alberta, November 2001
  
  
• "Machine Learning in Games", Invited talk at the BNMI Workshop on Artificial Intelligence, August 2002
  
  
• "Games Research at UofA", Invited talk at the Relic.com, Vancouver, May 2003
  
  
• M. Buro and T. Furtak, RTS Games as Test-Bed for Real-Time Research, Invited Paper at the Workshop on Game AI, JCIS 2003, 481-484 (extended version)
  
  
• Presentation about ORTS at the Microsoft Academic Days event, January 2006
  
  
• The First ORTS Game AI Competition at AIIDE-06, June 2006
  
  
• Invited Talk on Search and Abstraction in Real-Time Strategy Games at SOCS 2016, July 2016
  
  
• Invited Talk on Game AI Challenges at CPCC 2018 in Shenyang, China, August 2018

Miscellany

• M. Buro, L'apprentissage des ouvertures chez Logistello , Magazine de la Fèdèration Française d'Othello FFORUM 37 (1995), 18-20
  
  
• M. Buro (Editor), The Third International Paderborn Computer Othello Tournament (1995)
  
  
• M. Buro (Editor), The Fourth International Paderborn Computer Othello Tournament (1996)
  
  
• M. Buro, An Overview of Logistello, (1997)
  
  
• M. Buro (Editor), Papers of the Workshop on Computer Games, NECI, Aug.97, (1997)
  
  
• M. Buro, Multi-ProbCut Search, GAMES-Group Talk at U.of.A, October 2002
  
  
• M. Buro, A Generalized Linear Evaluation Model, Heuristic Search Class lecture at U.of.A, October 2002
  
  
• M. Buro, RTDS meeting kick-off, October 2002
  
  
• M. Buro, AI Projects you didn't know you are interested in, AI Seminar UofA, October 2002
  
  
• M. Buro, ORTS - A Hack-Free RTS Game Toolkit, RTDS meeting, October 2002
  
  
• M. Buro, Evaluation, Search, Planning, and beyond, 10-minute madness, UofA, November 2002
  
  
• J. Schaeffer, V. Bulitko, M. Buro, Bots Get Smart, IEEE Spectrum, Dec. 2008, pp. 44-49
  
  
• M. Buro, D. Churchill, Real-Time Strategy Game Competitions, AI Magazine Vol 33, No 3, pp. 106-108, 2012