Matthew E. Taylor

Marcos Augusto M. Vieira, Matthew E. Taylor, Prateek Tandon, Manish Jain, Ramesh Govindan, Gaurav S. Sukhatme, Milind Tambe: Mitigating multi-path fading in a mobile mesh network. Ad Hoc Networks 11(4): 1510-1521 (2013)

Haitham Bou-Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessens, Gerhard Weiss: Reinforcement learning transfer via sparse coding. AAMAS 2012: 383-390

Lisa Torrey, Matthew E. Taylor: Towards student/teacher learning in sequential decision tasks. AAMAS 2012: 1383-1384

Frank Schweitzer, Matthew E. Taylor: Editorial: Agents and Multi-Agent Systems. Advances in Complex Systems 14(2) (2011)

Matthew E. Taylor, Manish Jain, Prateek Tandon, Makoto Yokoo, Milind Tambe: Distributed on-Line Multi-Agent Optimization under Uncertainty: Balancing Exploration and Exploitation. Advances in Complex Systems 14(3): 471-528 (2011)

Matthew E. Taylor, Peter Stone: An Introduction to Intertask Transfer for Reinforcement Learning. AI Magazine 32(1): 15-34 (2011)

Matthew Edmund Taylor, Halit Bener Suay, Sonia Chernova: Using Human Demonstrations to Improve Reinforcement Learning. AAAI Spring Symposium: Help Me Help You: Bridging the Gaps in Human-Agent Collaboration 2011

Haitham Bou-Ammar, Matthew E. Taylor: Reinforcement Learning Transfer via Common Subspaces. ALA 2011: 21-36

Paul Scerri, Balajee Kannan, Prasanna Velagapudi, Kate Macarthur, Peter Stone, Matthew E. Taylor, John Dolan, Alessandro Farinelli, Archie C. Chapman, Bernadine Dias, George Kantor: Flood Disaster Mitigation: A Real-World Challenge Problem for Multi-agent Unmanned Surface Vehicles. AAMAS Workshops 2011: 252-269

Jason Tsai, Natalie Fridman, Emma Bowring, Matthew Brown, Shira Epstein, Gal A. Kaminka, Stacy Marsella, Andrew Ogden, Inbal Rika, Ankur Sheel, Matthew E. Taylor, Xuezhi Wang, Avishay Zilka, Milind Tambe: ESCAPES: evacuation simulation with children, authorities, parents, emotions, and social comparison. AAMAS 2011: 457-464

Matthew E. Taylor, Halit Bener Suay, Sonia Chernova: Integrating reinforcement learning with human demonstrations of varying ability. AAMAS 2011: 617-624

Matthew E. Taylor, Brian Kulis, Fei Sha: Metric learning for reinforcement learning agents. AAMAS 2011: 777-784

Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe: Teamwork in distributed POMDPs: execution-time coordination under model uncertainty. AAMAS 2011: 1261-1262

Haitham Bou-Ammar, Matthew E. Taylor, Karl Tuyls, Gerhard Weiss: Reinforcement Learning Transfer Using a Sparse Coded Inter-task Mapping. EUMAS 2011: 1-16

Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, Ioannis P. Vlahavas: Transfer Learning via Multiple Inter-task Mappings. EWRL 2011: 225-236

Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe: Towards Addressing Model Uncertainty: Robust Execution-Time Coordination for Teamwork. IAT 2011: 204-207

Shimon Whiteson, Matthew E. Taylor, Peter Stone: Critical factors in the empirical performance of temporal difference and evolutionary methods for reinforcement learning. Autonomous Agents and Multi-Agent Systems 21(1): 1-35 (2010)

Matthew E. Taylor, Christopher Kiekintveld, Craig Western, Milind Tambe: A Framework for Evaluating Deployed Security Systems: Is There a Chink in your ARMOR? Informatica (Slovenia) 34(2): 129-140 (2010)

Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, Milind Tambe: Teamwork and Coordination under Model Uncertainty in DEC-POMDPs. Interactive Decision Theory and Game Theory 2010

Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley: Evolving Compiler Heuristics to Manage Communication and Contention. AAAI 2010

Matthew E. Taylor, Manish Jain, Yanquin Jin, Makoto Yokoo, Milind Tambe: When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty. AAMAS 2010: 109-116

Matthew E. Taylor, Karl Tuyls (Eds.): Adaptive and Learning Agents, Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009, Revised Selected Papers. Lecture Notes in Computer Science 5924, Springer 2010, isbn 978-3-642-11813-5

Matthew E. Taylor: Transfer in Reinforcement Learning Domains. Studies in Computational Intelligence 216, Springer 2009, isbn 978-3-642-01881-7, pp. 1-218

Razvan C. Bunescu, Vitor R. Carvalho, Jan Chomicki, Vincent Conitzer, Michael T. Cox, Virginia Dignum, Zachary Dodds, Mark Dredze, David Furcy, Evgeniy Gabrilovich, Mehmet H. Göker, Hans W. Guesgen, Haym Hirsh, Dietmar Jannach, Ulrich Junker, Wolfgang Ketter, Alfred Kobsa, Sven Koenig, Tessa A. Lau, Lundy Lewis, Eric T. Matson, Ted Metzler, Rada Mihalcea, Bamshad Mobasher, Joelle Pineau, Pascal Poupart, Anita Raja, Wheeler Ruml, Norman M. Sadeh, Guy Shani, Daniel G. Shapiro, Sarabjot Singh Anand, Matthew E. Taylor, Kiri Wagstaff, Trey Smith, William E. Walsh, Ron Zhou: AAAI 2008 Workshop Reports. AI Magazine 30(1): 108-118 (2009)

Matthew E. Taylor, Peter Stone: Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research 10: 1633-1685 (2009)

Matthew E. Taylor: Assisting Transfer-Enabled Machine Learning Algorithms: Leveraging Human Knowledge for Curriculum Design. AAAI Spring Symposium: Agents that Learn from Human Teachers 2009: 141-143

Pradeep Varakantham, Jun-young Kwak, Matthew E. Taylor, Janusz Marecki, Paul Scerri, Milind Tambe: Exploiting Coordination Locales in Distributed POMDPs via Social Model Shaping. ICAPS 2009

Marc J. V. Ponsen, Matthew E. Taylor, Karl Tuyls: Abstraction and Generalization in Reinforcement Learning: A Summary and Framework. ALA 2009: 1-32

Manish Jain, Matthew E. Taylor, Milind Tambe, Makoto Yokoo: DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks. IJCAI 2009: 181-186

Katherine E. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A. Maher, Doug Burger, Kathryn S. McKinley: Feature selection and policy optimization for distributed instruction placement using reinforcement learning. PACT 2008: 32-42

Matthew E. Taylor, Gregory Kuhlmann, Peter Stone: Transfer Learning and Intelligence: an Argument and Approach. AGI 2008: 326-337

Matthew E. Taylor, Gregory Kuhlmann, Peter Stone: Autonomous transfer for reinforcement learning. AAMAS (1) 2008: 283-290

Matthew E. Taylor, Nicholas K. Jong, Peter Stone: Transferring Instances for Model-Based Reinforcement Learning. ECML/PKDD (2) 2008: 488-505

Shimon Whiteson, Matthew E. Taylor, Peter Stone: Empirical Studies in Action Selection with Reinforcement Learning. Adaptive Behaviour 15(1): 33-50 (2007)

Matthew E. Taylor, Peter Stone, Yaxin Liu: Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. Journal of Machine Learning Research 8: 2125-2167 (2007)

Matthew E. Taylor, Shimon Whiteson, Peter Stone: Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison. AAAI 2007: 1675-1678

Matthew E. Taylor, Peter Stone: Representation Transfer via Elaboration. AAAI 2007: 1906-1907

Matthew E. Taylor: Autonomous Inter-Task Transfer in Reinforcement Learning Domains. AAAI 2007: 1951-1952

Matthew E. Taylor, Shimon Whiteson, Peter Stone: Transfer via inter-task mappings in policy search reinforcement learning. AAMAS 2007: 37

Matthew E. Taylor, Peter Stone: Towards reinforcement learning representation transfer. AAMAS 2007: 100

Mazda Ahmadi, Matthew E. Taylor, Peter Stone: IFSA: incremental feature-set augmentation for reinforcement learning tasks. AAMAS 2007: 186

Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, Michael J. Witbrock: Autonomous Classification of Knowledge into an Ontology. FLAIRS Conference 2007: 140-145

Matthew E. Taylor, Cynthia Matuszek, Pace Reagan Smith, Michael J. Witbrock: Guiding Inference with Policy Search Reinforcement Learning. FLAIRS Conference 2007: 146-151

Matthew E. Taylor, Peter Stone: Cross-domain transfer for reinforcement learning. ICML 2007: 879-886

Matthew E. Taylor, Peter Stone: Inter-Task Action Correlation for Reinforcement Learning Tasks. AAAI 2006: 1901-1903

Matthew E. Taylor, Shimon Whiteson, Peter Stone: Comparing evolutionary and temporal difference methods in a reinforcement learning domain. GECCO 2006: 1321-1328

Matthew E. Taylor, Peter Stone, Yaxin Liu: Value Functions for RL-Based Behavior Transfer: A Comparative Study. AAAI 2005: 880-885

Matthew E. Taylor, Peter Stone: Behavior transfer for value-function-based reinforcement learning. AAMAS 2005: 53-59

Peter Stone, Gregory Kuhlmann, Matthew E. Taylor, Yaxin Liu: Keepaway Soccer: From Machine Learning Testbed to Benchmark. RoboCup 2005: 93-105