Matthew E. Taylor's Publications
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Sorted by Date •
Classified by Publication Type •
Sorted by First Author Last Name •
Classified by Research Category •
Classified by Research Category
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Autonomic Computing
•
DCOP
•
Distributed POMDPs
•
Genetic Algorithms
•
Inference
•
Machine Learning in Practice
•
Ontologies
•
Pedagogy
•
Planning
•
Robotics
•
Reinforcement Learning
•
Security
•
Simulated Robot Soccer
•
Transfer Learning
•
Unspecified
•
Autonomic Computing
- Katherine K. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A.
Maher, Kathryn McKinley, and Doug Burger. Feature Selection and Policy Optimization for Distributed Instruction Placement
Using Reinforcement Learning. In Proceedings of the Seventh International Joint Conference on Parallel Architectures
and Compilation Techniques (PACT), pp. 32–42, October 2008. 19% acceptance rate
PACT-2008
Details
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[pdf]
(297.8kB
)
- Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Doug Burger,
and Kathryn S. McKinley. Policy Search Optimization for Spatial Path Planning. In NIPS-07 workshop on Machine
Learning for Systems Problems, December 2007. (Two page extended abstract.)
NIPS
2007 workshop on Machine Learning for Systems Problems
Superseded by the PACT-08 conference paper Using
Reinforcement Learning to Select Policy Features for Distributed Instruction Placement.
Details
Download:
(unavailable)
DCOP
- Tim Tim, Tong T. Pham, and Matthew E. Taylor. Distributed learning
and multi-objectivity in traffic light control. Connection Science, 26(1):65–83, 2014.
Details
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[pdf]
(756.1kB
)
- Tong Pham, Aly Tawfika, and Matthew E. Taylor. A Simple, Naive Agent-based
Model for the Optimization of a System of Traffic Lights: Insights from an Exploratory Experiment. In Proceedings of
Conference on Agent-Based Modeling in Transportation Planning and Operations, September 2013.
Details
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[pdf]
(2.6MB
)
- Tong Pham, Tim Brys, and Matthew E. Taylor. Learning Coordinated
Traffic Light Control. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-13), May 2013.
ALA-13
Details
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[pdf]
(471.3kB
)
- Marcos A. M. Vieira, Matthew E. Taylor, Prateek Tandon, Manish
Jain, Ramesh Govindan, Gaurav S. Sukhatme, and Milind Tambe. Mitigating
Multi-path Fading in a Mobile Mesh Network. Ad Hoc Networks Journal, 2011.
Details
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[pdf]
(1007.7kB
)
- Scott Alfeld, Kumera Berkele, Stephen A. Desalvo, Tong Pham, Daniel Russo, Lisa Yan, and Matthew
E. Taylor. Reducing the Team Uncertainty Penalty: Empirical and Theoretical Approaches. In Proceedings of the
Workshop on Multiagent Sequential Decision Making in Uncertain Domains (at AAMAS-11), May 2011.
MSDM-11
Details
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[pdf]
(604.8kB
)
- Matthew E. Taylor, Manish Jain, Prateek Tandon, Makoto Yokoo, and Milind
Tambe. Distributed On-line Multi-Agent Optimization Under Uncertainty: Balancing Exploration and Exploitation.
Advances in Complex Systems, 2011.
Details
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[pdf]
(830.8kB
)
- Scott Alfeld, Matthew E. Taylor, Prateek Tandon, and Milind
Tambe. Towards a Theoretic Understanding of DCEE. In Proceedings of the Distributed Constraint Reasoning workshop
(at AAMAS-10), May 2010.
DCR-10
Details
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[pdf]
(378.1kB
)
- Matthew E. Taylor, Manish Jain, Yanquin Jin, Makoto Yooko, and Milind
Tambe. When Should There be a ``Me'' in ``Team''? Distributed Multi-Agent Optimization Under Uncertainty. In Proceedings
of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2010. 24% acceptance rate
Supplemental material is available at http://teamcore.usc.edu/dcop/.
Details
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[pdf]
(2.9MB
)
- Manish Jain, Matthew E. Taylor, Makoto Yokoo, and Milind
Tambe. DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), July 2009. 26%
acceptance rate
IJCAI-2009
Details
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[pdf]
(250.8kB
)
- Manish Jain, Matthew E. Taylor, Makoto Yokoo, and Milind
Tambe. DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
In Proceedings of the Third International Workshop on Agent Technology for Sensor Networks (at AAMAS-09), May 2009.
ATSN-2009
Superseded by the IJCAI-09 conference paper DCOPs
Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
Details
Download:
(unavailable)
- Matthew E. Taylor, Manish Jain, Prateek Tandon, and Milind
Tambe. Using DCOPs to Balance Exploration and Exploitation in Time-Critical Domains. In Proceedings of the IJCAI
2009 Workshop on Distributed Constraint Reasoning, July 2009.
DCR-2009
Details
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[pdf]
(698.3kB
)
Distributed POMDPs
- Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, and Milind
Tambe. Towards Addressing Model Uncertainty: Robust Execution-time Coordination for Teamwork (Short Paper). In
The IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), August 2011. Short Paper: 21% acceptance
rate for papers, additional 28% for short papers
IAT-11
Details
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[pdf]
(189.7kB
)
- Jun-young Kwak, Rong Yang, Zhengyu Yin, Matthew E. Taylor, and Milind
Tambe. Teamwork in Distributed POMDPs: Execution-time Coordination Under Model Uncertainty (Poster). In International
Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2011. Extended Abstract: 22% acceptance rate for papers,
additional 25% for extended abstracts
AAMAS-11
Details
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[pdf]
(201.8kB
)
- Jun-young Kwak, Zhengyu Yin, Rong Yang, Matthew E. Taylor, and Milind
Tambe. Robust Execution-time Coordination in DEC-POMDPs Under Model Uncertainty. In Proceedings of the Workshop
on Multiagent Sequential Decision Making in Uncertain Domains (at AAMAS-11), May 2011.
MSDM-11
Details
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[pdf]
(922.2kB
)
- Jun-young Kwak, Pradeep Varakantham, Matthew E. Taylor, Janusz Marecki,
Paul Scerri, and Milind Tambe. Exploiting Coordination Locales in Distributed
POMDPs via Social Model Shaping. In Proceedings of the Fourth Workshop on Multi-agent Sequential Decision-Making in
Uncertain Domains (at AAMAS-09), May 2009.
MSDM-2009
Superseded by the ICAPS-09 conference paper Exploiting
Coordination Locales in Distributed {POMDP}s via Social Model Shaping.
Details
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[pdf]
(449.4kB
)
- Pradeep Varakantham, Jun-young Kwak, Matthew E. Taylor, Janusz Marecki,
Paul Scerri, and Milind Tambe. Exploiting Coordination Locales in Distributed
POMDPs via Social Model Shaping. In Proceedings of the Nineteenth International Conference on Automated Planning
and Scheduling (ICAPS), September 2009. 34% acceptance rate
ICAPS-2009
Details
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[pdf]
(1.2MB
)
Genetic Algorithms
- Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Bertrand A. Maher,
Doug Burger, and Kathryn S. McKinley. Evolving Compiler Heuristics to Manage Communication and Contention. In Proceedings
of the Twenty-Fourth Conference on Artificial Intelligence (AAAI), July 2010. Nectar Track, 25% acceptance rate
AAAI-2010. This paper is based on results presented in our
earlier PACT-08 paper.
Details
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[pdf]
(127.8kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison.
In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI), pp. 1675–1678, July
2007. Nectar Track, 38% acceptance rate
AAAI-2007
Details
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[pdf]
(99.7kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Comparing Evolutionary and Temporal Difference Methods for Reinforcement Learning. In Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO), pp. 1321–28, July 2006. 46% acceptance rate, Best
Paper Award in GA track (of 85 submissions)
Best Paper
Award (Genetic Algorithms Track) at GECCO-2006.
Details
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[pdf]
(235.9kB
)
Inference
- Matthew E. Taylor, Cynthia Matuszek, Pace Reagan Smith, and Michael Witbrock.
Guiding Inference with Policy Search Reinforcement Learning. In Proceedings of the Twentieth International FLAIRS
Conference (FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
Details
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[pdf]
(138.5kB
)
Machine Learning in Practice
- Shimon Whiteson, Matthew E. Taylor, and Peter
Stone. Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement
Learning. Journal of Autonomous Agents and Multi-Agent Systems, 21(1):1–27, 2010.
Details
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[pdf]
(760.6kB
)
- Katherine K. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A.
Maher, Kathryn McKinley, and Doug Burger. Feature Selection and Policy Optimization for Distributed Instruction Placement
Using Reinforcement Learning. In Proceedings of the Seventh International Joint Conference on Parallel Architectures
and Compilation Techniques (PACT), pp. 32–42, October 2008. 19% acceptance rate
PACT-2008
Details
Download:
[pdf]
(297.8kB
)
- Matthew E. Taylor, Cynthia Matuszek, Pace Reagan Smith, and Michael Witbrock.
Guiding Inference with Policy Search Reinforcement Learning. In Proceedings of the Twentieth International FLAIRS
Conference (FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
Details
Download:
[pdf]
(138.5kB
)
- Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, and Michael Witbrock.
Autonomous Classification of Knowledge into an Ontology. In Proceedings of the Twentieth International FLAIRS Conference
(FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
Details
Download:
[pdf]
(107.8kB
)
- Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Doug Burger,
and Kathryn S. McKinley. Policy Search Optimization for Spatial Path Planning. In NIPS-07 workshop on Machine
Learning for Systems Problems, December 2007. (Two page extended abstract.)
NIPS
2007 workshop on Machine Learning for Systems Problems
Superseded by the PACT-08 conference paper Using
Reinforcement Learning to Select Policy Features for Distributed Instruction Placement.
Details
Download:
(unavailable)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Comparing Evolutionary and Temporal Difference Methods for Reinforcement Learning. In Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO), pp. 1321–28, July 2006. 46% acceptance rate, Best
Paper Award in GA track (of 85 submissions)
Best Paper
Award (Genetic Algorithms Track) at GECCO-2006.
Details
Download:
[pdf]
(235.9kB
)
Ontologies
- Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, and Michael Witbrock.
Autonomous Classification of Knowledge into an Ontology. In Proceedings of the Twentieth International FLAIRS Conference
(FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
Details
Download:
[pdf]
(107.8kB
)
Pedagogy
- Matthew E. Taylor. Model Assignment: Reinforcement Learning in a Generalized
Mario Domain. In Proceedings of the Second Symposium on Educational Advances in Artificial Intelligence, August
2011.
EAAI-11
Assignment
Webpage
Details
Download:
(unavailable)
- Matthew E. Taylor. Teaching Reinforcement Learning with Mario: An Argument
and Case Study. In Proceedings of the Second Symposium on Educational Advances in Artificial Intelligence, August
2011.
EAAI-11
Details
Download:
[pdf]
(1.3MB
)
Planning
- Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 488–505,
September 2008. 19% acceptance rate
ECML-2008
Details
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[pdf]
(304.9kB
)
- Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In The Adaptive Learning Agents and Multi-Agent
Systems (ALAMAS+ALAG) workshop at AAMAS, May 2008.
AAMAS
2008 workshop on Adaptive Learning Agents and Multi-Agent Systems
Superseded by the ECML-08 conference paper Transferring
Instances for Model-Based Reinforcement Learning.
Details
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(unavailable)
- Matthew E. Taylor, Gregory Kuhlmann, and Peter
Stone. Accelerating Search with Transferred Heuristics. In ICAPS-07 workshop on AI Planning and Learning,
September 2007.
ICAPS 2007 workshop on AI Planning and Learning
Details
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[pdf]
(139.9kB
)
Robotics
- Ravi Balasubramanian and Matthew E. Taylor. Learning for Mobile-Robot
Error Recovery (Extended Abstract). In The AAAI 2013 Spring Symposium --- Designing Intelligent Robots: Reintegrating
AI II, March 2013.
Designing Intelligent Robots
Details
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[pdf]
(490.0kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas. Autonomous Selection of Inter-Task
Mappings in Transfer Learning (extended abstract). In The AAAI 2013 Spring Symposium --- Lifelong Machine Learning,
March 2013.
Lifelong Machine Learning
Details
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[pdf]
(215.4kB
)
- Sanjeev Sharma and Matthew E. Taylor. Autonomous Waypoint Generation Strategy for On-Line Navigation in Unknown Environments.
In IROS Workshop on Robot Motion Planning: Online, Reactive, and in Real-Time, October 2012.
Details
Download:
[pdf]
(901.0kB
)
- Scott Alfeld, Matthew E. Taylor, Prateek Tandon, and Milind
Tambe. Towards a Theoretic Understanding of DCEE. In Proceedings of the Distributed Constraint Reasoning workshop
(at AAMAS-10), May 2010.
DCR-10
Details
Download:
[pdf]
(378.1kB
)
- Samuel Barrett, Matthew E. Taylor, and Peter
Stone. Transfer Learning for Reinforcement Learning on a Physical Robot. In Proceedings of the Adaptive and
Learning Agents workshop (at AAMAS-10), May 2010.
ALA-10
Details
Download:
[pdf]
(684.9kB
)
- Manish Jain, Matthew E. Taylor, Makoto Yokoo, and Milind
Tambe. DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
In Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), July 2009. 26%
acceptance rate
IJCAI-2009
Details
Download:
[pdf]
(250.8kB
)
- Manish Jain, Matthew E. Taylor, Makoto Yokoo, and Milind
Tambe. DCOPs Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
In Proceedings of the Third International Workshop on Agent Technology for Sensor Networks (at AAMAS-09), May 2009.
ATSN-2009
Superseded by the IJCAI-09 conference paper DCOPs
Meet the Real World: Exploring Unknown Reward Matrices with Applications to Mobile Sensor Networks.
Details
Download:
(unavailable)
Reinforcement Learning
- Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew E. Taylor. Online
Multi-Task Learning for Policy Gradient Methods. In Proceedings of the 31st International Conferences on Machine Learning
(ICML), June 2014. 25% acceptance rate
Details
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[pdf]
(3.1MB
)
- Haitham Bou Ammar, Eric Eaton, Matthew E. Taylor, Decibal C. Mocanu, Kurt Driessens,
Gerhard Weiss, and Karl Tuyls. An Automated Measure of MDP Similarity for Transfer in Reinforcement Learning. In Proceedings
of the Machine Learning for Interactive Systems workshop (at AAAI-14), July 2014.
Details
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[pdf]
(456.0kB
)
- Tim Brys, Ann Nowé, Daniel Kudenko, and Matthew E. Taylor. Combining
Multiple Correlated Reward and Shaping Signals by Measuring Confidence. In Proceedings of the 28th AAAI Conference
on Artificial Intelligence (AAAI), July 2014. 28% acceptance rate
Details
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[pdf]
(529.7kB
)
- Tim Brys, Anna Harutyunyan, Peter Vrancx, Matthew E. Taylor, Daniel Kudenko,
and Ann Nowé. Multi-Objectivization of Reinforcement Learning Problems by Reward Shaping. In Proceedings
of the IEEE 2014 International Joint Conference on Neural Networks (IJCNN), July 2014. 59% acceptance rate
Details
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[pdf]
(524.2kB
)
- Tim Brys, Matthew E. Taylor, and Ann Nowé. Using Ensemble Techniques
and Multi-Objectivization to Solve Reinforcement Learning Problems. In Proceedings of the 21st European Conference
on Artificial Intelligence (ECAI), August 2014. 41% acceptance rate for short papers
Details
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[pdf]
(151.7kB
)
- Tim Brys, Kristof Van Moffaert, Ann Nowe, and Matthew E. Taylor. Adaptive
Objective Selection for Correlated Objectives in Multi-Objective Reinforcement Learning (Extended Abstract). In The
13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2014. Extended abstract: 24% acceptance
rate for papers, additional 22% for extended abstracts
Details
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[pdf]
(182.1kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis
Vlahavas. An Autonomous Transfer Learning Algorithm for TD-Learners. In Proceedings of the 8th Hellenic Conference
on Artificial Intelligence (SETN), May 2014. 50% acceptance rate
Details
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[pdf]
(249.9kB
)
- Chris HolmesParker, Matthew E. Taylor, Adrian Agogino, and Kagan Tumer. CLEANing
the Reward: Counterfactual Actions Remove Exploratory Action Noise in Multiagent Learning. In Proceedings of the 2014
IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), August 2014. 43% acceptance rate
Details
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[pdf]
(560.5kB
)
- Chris HolmesParker, Matthew E. Taylor, Adrian Agogino, and Kagan Tumer.
CLEANing the Reward: Counterfactual Actions Remove Exploratory Action Noise in Multiagent Learning (Extended Abstract).
In The Thirteenth International Joint Conference on Autonomous Agents and Multiagent Systems, May 2014. Extended abstract:
24% acceptance rate for papers, additional 22% for extended abstracts
Details
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[pdf]
(195.4kB
)
- Chris HolmesParker, Matthew E. Taylor, Yusen Zhan, and Kagan Tumer. Exploiting
Structure and Agent-Centric Rewards to Promote Coordination in Large Multiagent Systems. In Proceedings of the Adaptive
and Learning Agents workshop (at AAMAS-14), May 2014.
Details
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[pdf]
(586.4kB
)
- Robert Loftin, Bei Peng, James MacGlashan, Michael Littman, Matthew E. Taylor,
David Roberts, and Jeff Huang. Learning Something from Nothing: Leveraging Implicit Human Feedback Strategies. In Proceedings
of the 23rd IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), August 2014.
Details
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[pdf]
(434.7kB
)
- Robert Loftin, Bei Peng, James MacGlashan, Machiael L. Littman, Matthew E. Taylor,
Jeff Huang, and David L. Roberts. A Strategy-Aware Technique for Learning Behaviors from Discrete Human Feedback. In
Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), July 2014. 28% acceptance rate
Details
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[pdf]
(667.3kB
)
- James Macglashan, Michael L. Littman, Robert Loftin, Bei Peng, David Roberts, and Matthew
E. Taylor. Training an Agent to Ground Commands with Reward and Punishment. In Proceedings of the Machine Learning
for Interactive Systems workshop (at AAAI-14), July 2014.
Details
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[pdf]
(439.2kB
)
- Matthew E. Taylor, Nicholas Carboni, Anestis Fachantidis, Ioannis Vlahavas,
and Lisa Torrey. Reinforcement learning agents providing advice in complex video games. Connection Science,
26(1):45–63, 2014.
Details
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[pdf]
(587.5kB
)
- Tim Tim, Tong T. Pham, and Matthew E. Taylor. Distributed learning
and multi-objectivity in traffic light control. Connection Science, 26(1):65–83, 2014.
Details
Download:
[pdf]
(756.1kB
)
- Yusen Zhan, Anestis Fachantidis, Ioannis Vlahavas, and Matthew E. Taylor.
Agents Teaching Humans in Reinforcement Learning Tasks. In Proceedings of the Adaptive and Learning Agents workshop
(at AAMAS-14), May 2014.
Details
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[pdf]
(422.7kB
)
- Haitham Bou Ammar, Matthew E. Taylor, Karl Tuyls, and Gerhard Weiss. Reinforcement
Learning Transfer using a Sparse Coded Inter-Task Mapping. In LNAI Post-proceedings of the European Workshop on Multi-agent
Systems, Springer-Verlag, 2013.
Details
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[pdf]
(535.0kB
)
- Haitham Bou Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens,
Karl Tuyls, and Gerhard Weiss. Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted
Boltzmann Machines. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECML PKDD), September 2013. 25% acceptance rate
Details
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[pdf]
(519.2kB
)
- Haitham Bou Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens,
Karl Tuyls, and Gerhard Weiss. Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted
Boltzmann Machines. In The 25th Benelux Conference on Artificial Intelligence (BNAIC), November 2013.
Details
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[pdf]
(85.8kB
)
- Ravi Balasubramanian and Matthew E. Taylor. Learning for Mobile-Robot
Error Recovery (Extended Abstract). In The AAAI 2013 Spring Symposium --- Designing Intelligent Robots: Reintegrating
AI II, March 2013.
Designing Intelligent Robots
Details
Download:
[pdf]
(490.0kB
)
- Nicholas Carboni and Matthew E. Taylor. Preliminary Results for
1 vs. 1 Tactics in Starcraft. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-13), May
2013.
ALA-13
Details
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[pdf]
(386.5kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas. Autonomous Selection of Inter-Task
Mappings in Transfer Learning (extended abstract). In The AAAI 2013 Spring Symposium --- Lifelong Machine Learning,
March 2013.
Lifelong Machine Learning
Details
Download:
[pdf]
(215.4kB
)
- Tong Pham, Tim Brys, and Matthew E. Taylor. Learning Coordinated
Traffic Light Control. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-13), May 2013.
ALA-13
Details
Download:
[pdf]
(471.3kB
)
- Lisa Torrey and Matthew E. Taylor. Teaching on a Budget: Agents Advising
Agents in Reinforcement Learning. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
May 2013. 23% acceptance rate
AAMAS-13
Details
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[pdf]
(253.0kB
)
- Matthew Adams, Robert Loftin, Matthew E. Taylor, Michael Littman,
and David Roberts. An Empirical Analysis of RL's Drift From Its Behaviorism Roots. In Proceedings of the Adaptive
and Learning Agents workshop (at AAMAS-12), June 2012.
ALA-12
Details
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[pdf]
(338.4kB
)
- Haitham Bou Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessen, and Gerhard
Weiss. Reinforcement Learning Transfer via Sparse Coding. In International Conference on Autonomous Agents and Multiagent
Systems (AAMAS), June 2012. 20% acceptance rate
AAMAS-12
Details
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[pdf]
(286.7kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas.
Transfer Learning via Multiple Inter-Task Mappings. In Scott Sanner and Marcus Hutter, editors, Recent Advances
in Reinforcement Learning, Lecture Notes in Artificial Intelligence, pp. 225–236, Springer-Verlag, Berlin, 2012.
Details
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[pdf]
(176.6kB
)
- Sanjeev Sharma and Matthew E. Taylor. Autonomous Waypoint Generation Strategy for On-Line Navigation in Unknown Environments.
In IROS Workshop on Robot Motion Planning: Online, Reactive, and in Real-Time, October 2012.
Details
Download:
[pdf]
(901.0kB
)
- Lisa Torrey and Matthew E. Taylor. Towards Student/Teacher Learning
in Sequential Decision Tasks. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
June 2012. Extended Abstract: 20% acceptance rate for papers, additional 23% for extended abstracts
AAMAS-12
Details
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[pdf]
(138.6kB
)
- Lisa Torrey and Matthew E. Taylor. Help an Agent Out: Student/Teacher Learning
in Sequential Decision Tasks. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-12), June 2012.
ALA-12
Details
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[pdf]
(380.1kB
)
- Haitham Bou Ammar, Matthew E. Taylor, and Karl Tuyls. Common Sub-Space Transfer
for Reinforcement Learning Tasks (Poster). In The 23rd Benelux Conference on Artificial Intelligence (BNAIC), November
2011. 44% overall acceptance rate
BNAIC-11
Details
Download:
(unavailable)
- Haitham Bou Ammar, Matthew E. Taylor, Karl Tuyls, and Gerhard Weiss. Reinforcement
Learning Transfer using a Sparse Coded Inter-Task Mapping. In Proceedings of the European Workshop on Multi-agent Systems,
November 2011.
EUMAS-11
Details
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[pdf]
(359.2kB
)
- Haitham Bou Ammar and Matthew E. Taylor. Common Subspace Transfer for Reinforcement
Learning Tasks. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-11), May 2011.
ALA-11
Details
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[pdf]
(445.0kB
)
- Matthew E. Taylor, Halit Bener Suay, and Sonia Chernova. Integrating
Reinforcement Learning with Human Demonstrations of Varying Ability. In Proceedings of the International Conference
on Autonomous Agents and Multiagent Systems (AAMAS), May 2011. 22% acceptance rate
AAMAS-11
Details
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[pdf]
(157.4kB
)
- Matthew E. Taylor, Brian Kulis, and Fei Sha. Metric Learning for Reinforcement
Learning Agents. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
May 2011. 22% acceptance rate
AAMAS-11
Details
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[pdf]
(250.2kB
)
- Matthew E. Taylor and Peter
Stone. An Introduction to Inter-task Transfer for Reinforcement Learning. AI Magazine, 32(1):15–34,
2011.
Details
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[pdf]
(237.0kB
)
- Matthew E. Taylor. Model Assignment: Reinforcement Learning in a Generalized
Mario Domain. In Proceedings of the Second Symposium on Educational Advances in Artificial Intelligence, August
2011.
EAAI-11
Assignment
Webpage
Details
Download:
(unavailable)
- Matthew E. Taylor. Teaching Reinforcement Learning with Mario: An Argument
and Case Study. In Proceedings of the Second Symposium on Educational Advances in Artificial Intelligence, August
2011.
EAAI-11
Details
Download:
[pdf]
(1.3MB
)
- Matthew E. Taylor, Halit Bener Suay, and Sonia Chernova. Using Human
Demonstrations to Improve Reinforcement Learning. In The AAAI 2011 Spring Symposium --- Help Me Help You: Bridging
the Gaps in Human-Agent Collaboration, March 2011.
HMHY2011
Details
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[pdf]
(116.5kB
)
- Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter
Stone. Protecting Against Evaluation Overfitting in Empirical Reinforcement Learning. In Proceedings of the
IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), April 2011.
ADPRL
2011
Details
Download:
[pdf]
(165.3kB
)
- Samuel Barrett, Matthew E. Taylor, and Peter
Stone. Transfer Learning for Reinforcement Learning on a Physical Robot. In Proceedings of the Adaptive and
Learning Agents workshop (at AAMAS-10), May 2010.
ALA-10
Details
Download:
[pdf]
(684.9kB
)
- Marc Ponsen, Matthew E. Taylor, and Karl Tuyls. Abstraction and Generalization
in Reinforcement Learning. In Matthew E. Taylor and Karl Tuyls, editors,
Adaptive Agents and Multi-Agent Systems IV, pp. 1–33, Springer-Verlag, 2010.
Details
Download:
[pdf]
(1.5MB
)
- Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Bertrand A. Maher,
Doug Burger, and Kathryn S. McKinley. Evolving Compiler Heuristics to Manage Communication and Contention. In Proceedings
of the Twenty-Fourth Conference on Artificial Intelligence (AAAI), July 2010. Nectar Track, 25% acceptance rate
AAAI-2010. This paper is based on results presented in our
earlier PACT-08 paper.
Details
Download:
[pdf]
(127.8kB
)
- Matthew E. Taylor and Karl Tuyls, editors. Adaptive Agents and Multi-Agent
Systems IV, Lecture Notes in Computer Science, Springer-Verlag, 2010.
Many chapters are extended versions of
papers appearing at the AAMAS 2009 workshop on Adaptive and Learning
Agents. Publisher's website: http://www.springer.com/computer/ai/book/978-3-642-11813-5
Details
Download:
(unavailable)
- Matthew E. Taylor and Sonia Chernova. Integrating Human Demonstration
and Reinforcement Learning: Initial Results in Human-Agent Transfer. In Proceedings of the Agents Learning Interactively
from Human Teachers workshop (at AAMAS-10), May 2010.
ALIHT-10
Details
Download:
[pdf]
(142.6kB
)
- Shimon Whiteson, Matthew E. Taylor, and Peter
Stone. Critical Factors in the Empirical Performance of Temporal Difference and Evolutionary Methods for Reinforcement
Learning. Journal of Autonomous Agents and Multi-Agent Systems, 21(1):1–27, 2010.
Details
Download:
[pdf]
(760.6kB
)
- Matthew E. Taylor. Transfer in Reinforcement Learning Domains,
Studies in Computational Intelligence, Springer-Verlag, 2009.
A book based on my PhD thesis.
Publisher's
Webpage.
Details
Download:
(unavailable)
- Matthew E. Taylor and Peter
Stone. Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research,
10(1):1633–1685, 2009.
Details
Download:
[pdf]
(399.8kB
)
- Matthew E. Taylor and Peter Stone.
Categorizing Transfer for Reinforcement Learning. In Poster at the Multidisciplinary Symposium on Reinforcement
Learning, June 2009.
MSRL-09.
Details
Download:
[pdf]
(144.5kB
)
- Matthew E. Taylor. Assisting Transfer-Enabled Machine Learning Algorithms:
Leveraging Human Knowledge for Curriculum Design. In The AAAI 2009 Spring Symposium on Agents that Learn from Human
Teachers, March 2009.
AAAI 2009 Spring Symposium on Agents
that Learn from Human Teachers
Details
Download:
[pdf]
(39.8kB
)
- Shimon Whiteson, Brian Tanner, Matthew E. Taylor, and Peter
Stone. Generalized Domains for Empirical Evaluations in Reinforcement Learning. In Proceedings of the Fourth
Workshop on Evaluation Methods for Machine Learning at ICML-09, June 2009.
Fourth
annual workshop on Evaluation Methods for Machine Learning
Details
Download:
[pdf]
(90.2kB
)
- Katherine K. Coons, Behnam Robatmili, Matthew E. Taylor, Bertrand A.
Maher, Kathryn McKinley, and Doug Burger. Feature Selection and Policy Optimization for Distributed Instruction Placement
Using Reinforcement Learning. In Proceedings of the Seventh International Joint Conference on Parallel Architectures
and Compilation Techniques (PACT), pp. 32–42, October 2008. 19% acceptance rate
PACT-2008
Details
Download:
[pdf]
(297.8kB
)
- Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 488–505,
September 2008. 19% acceptance rate
ECML-2008
Details
Download:
[pdf]
(304.9kB
)
- Matthew E. Taylor, Gregory Kuhlmann, and Peter
Stone. Autonomous Transfer for Reinforcement Learning. In Proceedings of the Seventh International Joint Conference
on Autonomous Agents and Multiagent Systems (AAMAS), pp. 283–290, May 2008. 22% acceptance rate
AAMAS-2008
Details
Download:
[pdf]
(233.7kB
)
- Matthew E. Taylor. Autonomous Inter-Task Transfer in Reinforcement
Learning Domains. Ph.D. Thesis, Department of Computer Sciences, The University of Texas at Austin, 2008. Available as
Technical Report UT-AI-TR-08-5.
Details
Download:
[pdf]
(2.3MB
)
- Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In The Adaptive Learning Agents and Multi-Agent
Systems (ALAMAS+ALAG) workshop at AAMAS, May 2008.
AAMAS
2008 workshop on Adaptive Learning Agents and Multi-Agent Systems
Superseded by the ECML-08 conference paper Transferring
Instances for Model-Based Reinforcement Learning.
Details
Download:
(unavailable)
- Mazda Ahmadi, Matthew E. Taylor, and Peter
Stone. IFSA: Incremental Feature-Set Augmentation for Reinforcement Learning Tasks. In Proceedings of the the
Sixth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 1120–1127, May 2007.
22% acceptance rate, Finalist for Best Student Paper
Best
Student Paper Nomination at AAMAS-2007.
Details
Download:
[pdf]
(261.6kB
)
- Matthew E. Taylor and Peter
Stone. Cross-Domain Transfer for Reinforcement Learning. In Proceedings of the Twenty-Fourth International
Conference on Machine Learning (ICML), June 2007. 29% acceptance rate
ICML-2007
Details
Download:
[pdf]
(220.7kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Temporal Difference and Policy Search Methods for Reinforcement Learning: An Empirical Comparison.
In Proceedings of the Twenty-Second Conference on Artificial Intelligence (AAAI), pp. 1675–1678, July
2007. Nectar Track, 38% acceptance rate
AAAI-2007
Details
Download:
[pdf]
(99.7kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning. In Proceedings of the Sixth
International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 156–163, May 2007. 22%
acceptance rate
AAMAS-2007
Details
Download:
[pdf]
(222.5kB
)
- Matthew E. Taylor, Cynthia Matuszek, Pace Reagan Smith, and Michael Witbrock.
Guiding Inference with Policy Search Reinforcement Learning. In Proceedings of the Twentieth International FLAIRS
Conference (FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
Details
Download:
[pdf]
(138.5kB
)
- Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, and Michael Witbrock.
Autonomous Classification of Knowledge into an Ontology. In Proceedings of the Twentieth International FLAIRS Conference
(FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
Details
Download:
[pdf]
(107.8kB
)
- Matthew E. Taylor, Peter Stone,
and Yaxin Liu. Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. Journal of Machine Learning
Research, 8(1):2125–2167, 2007.
Details
Download:
[pdf]
(499.9kB
)
- Matthew E. Taylor and Peter
Stone. Towards Reinforcement Learning Representation Transfer (Poster). In The Sixth International Joint Conference
on Autonomous Agents and Multiagent Systems (AAMAS), pp. 683–685, May 2007. Poster: 22% acceptance rate for
talks, additional 25% for posters.
AAMAS-2007.
Superseded by the symposium
paper Representation Transfer for
Reinforcement Learning.
Details
Download:
(unavailable)
- Matthew E. Taylor, Katherine E. Coons, Behnam Robatmili, Doug Burger,
and Kathryn S. McKinley. Policy Search Optimization for Spatial Path Planning. In NIPS-07 workshop on Machine
Learning for Systems Problems, December 2007. (Two page extended abstract.)
NIPS
2007 workshop on Machine Learning for Systems Problems
Superseded by the PACT-08 conference paper Using
Reinforcement Learning to Select Policy Features for Distributed Instruction Placement.
Details
Download:
(unavailable)
- Matthew E. Taylor and Peter
Stone. Representation Transfer for Reinforcement Learning. In AAAI 2007 Fall Symposium on Computational
Approaches to Representation Change during Learning and Development, November 2007.
2007
AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development
Details
Download:
[pdf]
(144.9kB
)
- Shimon Whiteson, Matthew E. Taylor, and Peter
Stone. Empirical Studies in Action Selection for Reinforcement Learning. Adaptive Behavior, 15(1), 2007.
Details
Download:
[pdf]
(828.6kB
)
- Shimon Whiteson, Matthew E. Taylor, and Peter
Stone. Adaptive Tile Coding for Value Function Approximation. Technical Report AI-TR-07-339, University of Texas
at Austin, 2007.
Details
Download:
[pdf]
(329.4kB
)
- Peter Stone, Gregory Kuhlmann, Matthew
E. Taylor, and Yaxin Liu. Keepaway Soccer: From Machine Learning Testbed to Benchmark. In Itsuki Noda, Adam
Jacoff, Ansgar Bredenfeld, and Yasutake Takahashi, editors, RoboCup-2005: Robot Soccer World Cup IX, pp. 93–105,
Springer-Verlag, Berlin, 2006. 28% acceptance rate at RoboCup-2005
Some simulations
of keepaway referenced in the paper and keepaway software.
Official version from Publisher's
Webpage© Springer-Verlag
Details
Download:
[pdf]
(567.7kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Comparing Evolutionary and Temporal Difference Methods for Reinforcement Learning. In Proceedings of
the Genetic and Evolutionary Computation Conference (GECCO), pp. 1321–28, July 2006. 46% acceptance rate, Best
Paper Award in GA track (of 85 submissions)
Best Paper
Award (Genetic Algorithms Track) at GECCO-2006.
Details
Download:
[pdf]
(235.9kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Transfer Learning for Policy Search Methods. In ICML workshop on Structural Knowledge Transfer for Machine
Learning, June 2006.
ICML-2006 workshop on Structural Knowledge
Transfer for Machine Learning.
Superseded by the conference paper Transfer
via Inter-Task Mappings in Policy Search Reinforcement Learning.
Details
Download:
(unavailable)
- Shimon Whiteson, Matthew E. Taylor, and Peter
Stone. Adaptive Tile Coding for Reinforcement Learning. In NIPS workshop on: Towards a New Reinforcement Learning?,
December 2006.
NIPS-2006 (Poster).
Superseded by the technical report
Adaptive Tile Coding for Value Function Approximation.
Details
Download:
(unavailable)
- Matthew E. Taylor, Peter Stone,
and Yaxin Liu. Value Functions for RL-Based Behavior Transfer: A Comparative Study. In Proceedings of the Twentieth
National Conference on Artificial Intelligence (AAAI), July 2005. 18% acceptance rate.
AAAI-2005.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
Details
Download:
[pdf]
(147.3kB
)
- Matthew E. Taylor and Peter
Stone. Behavior Transfer for Value-Function-Based Reinforcement Learning. In Proceedings of the Fourth International
Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 53–59, July 2005. 25% acceptance rate.
AAMAS-2005.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
Details
Download:
[pdf]
(230.4kB
)
- Matthew E. Taylor and Peter
Stone. Speeding up Reinforcement Learning with Behavior Transfer. In AAAI 2004 Fall Symposium on Real-life
Reinforcement Learning, October 2004.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
Details
Download:
[pdf]
(144.9kB
)
Security
- Matthew E. Taylor, Christopher Kiekintveld, and Milind
Tambe. Evaluating Deployed Decision Support Systems for Security: Challenges, Arguments, and Approaches. In Milind Tambe, editors, Security Games: Theory, Deployed Applications, Lessons
Learned, pp. 254–283, Cambridge University Press, 2011.
Details
Download:
[pdf]
(2.6MB
)
- Matthew E. Taylor, Christopher Kiekintveld, Craig Western, and Milind
Tambe. A Framework for Evaluating Deployed Security Systems: Is There a Chink in your ARMOR?. Informatica,
34(2):129–139, 2010.
Details
Download:
[pdf]
(402.6kB
)
- Matthew E. Taylor, Chris Kiekintveld, Craig Western, and Milind
Tambe. Is There a Chink in Your ARMOR? Towards Robust Evaluations for Deployed Security Systems. In Proceedings
of the IJCAI 2009 Workshop on Quantitative Risk Analysis for Security Applications, July 2009.
QRASA-2009
Superseded
by the journal article A Framework
for Evaluating Deployed Security Systems: Is There a Chink in your ARMOR?.
Details
Download:
[pdf]
(939.1kB
)
- Matthew E. Taylor, Chris Kiekintveld, Craig Western, and Milind
Tambe. Beyond Runtimes and Optimality: Challenges and Opportunities in Evaluating Deployed Security Systems. In
Proceedings of the AAMAS-09 Workshop on Agent Design: Advancing from Practice to Theory, May 2009.
ADAPT-2009
Details
Download:
[pdf]
(71.5kB
)
- Jason Tsai, Emma Bowring, Shira Epstein, Natalie Fridman, Prakhar Garg, Gal Kaminka, Andrew Ogden, Milind
Tambe, and Matthew E. Taylor. Agent-based Evacuation Modeling: Simulating
the Los Angeles International Airport. In Proceedings of the Workshop on Emergency Management: Incident, Resource,
and Supply Chain Management, November 2009.
EMWS09-2009
Details
Download:
[pdf]
(68.6kB
)
Simulated Robot Soccer
- Peter Stone, Gregory Kuhlmann, Matthew
E. Taylor, and Yaxin Liu. Keepaway Soccer: From Machine Learning Testbed to Benchmark. In Itsuki Noda, Adam
Jacoff, Ansgar Bredenfeld, and Yasutake Takahashi, editors, RoboCup-2005: Robot Soccer World Cup IX, pp. 93–105,
Springer-Verlag, Berlin, 2006. 28% acceptance rate at RoboCup-2005
Some simulations
of keepaway referenced in the paper and keepaway software.
Official version from Publisher's
Webpage© Springer-Verlag
Details
Download:
[pdf]
(567.7kB
)
Transfer Learning
- Haitham Bou Ammar, Eric Eaton, Paul Ruvolo, and Matthew E. Taylor. Online
Multi-Task Learning for Policy Gradient Methods. In Proceedings of the 31st International Conferences on Machine Learning
(ICML), June 2014. 25% acceptance rate
Details
Download:
[pdf]
(3.1MB
)
- Haitham Bou Ammar, Eric Eaton, Matthew E. Taylor, Decibal C. Mocanu, Kurt Driessens,
Gerhard Weiss, and Karl Tuyls. An Automated Measure of MDP Similarity for Transfer in Reinforcement Learning. In Proceedings
of the Machine Learning for Interactive Systems workshop (at AAAI-14), July 2014.
Details
Download:
[pdf]
(456.0kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis
Vlahavas. An Autonomous Transfer Learning Algorithm for TD-Learners. In Proceedings of the 8th Hellenic Conference
on Artificial Intelligence (SETN), May 2014. 50% acceptance rate
Details
Download:
[pdf]
(249.9kB
)
- Matthew E. Taylor, Nicholas Carboni, Anestis Fachantidis, Ioannis Vlahavas,
and Lisa Torrey. Reinforcement learning agents providing advice in complex video games. Connection Science,
26(1):45–63, 2014.
Details
Download:
[pdf]
(587.5kB
)
- Haitham Bou Ammar, Matthew E. Taylor, Karl Tuyls, and Gerhard Weiss. Reinforcement
Learning Transfer using a Sparse Coded Inter-Task Mapping. In LNAI Post-proceedings of the European Workshop on Multi-agent
Systems, Springer-Verlag, 2013.
Details
Download:
[pdf]
(535.0kB
)
- Haitham Bou Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens,
Karl Tuyls, and Gerhard Weiss. Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted
Boltzmann Machines. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases (ECML PKDD), September 2013. 25% acceptance rate
Details
Download:
[pdf]
(519.2kB
)
- Haitham Bou Ammar, Decebal Constantin Mocanu, Matthew E. Taylor, Kurt Driessens,
Karl Tuyls, and Gerhard Weiss. Automatically Mapped Transfer Between Reinforcement Learning Tasks via Three-Way Restricted
Boltzmann Machines. In The 25th Benelux Conference on Artificial Intelligence (BNAIC), November 2013.
Details
Download:
[pdf]
(85.8kB
)
- Ravi Balasubramanian and Matthew E. Taylor. Learning for Mobile-Robot
Error Recovery (Extended Abstract). In The AAAI 2013 Spring Symposium --- Designing Intelligent Robots: Reintegrating
AI II, March 2013.
Designing Intelligent Robots
Details
Download:
[pdf]
(490.0kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas. Autonomous Selection of Inter-Task
Mappings in Transfer Learning (extended abstract). In The AAAI 2013 Spring Symposium --- Lifelong Machine Learning,
March 2013.
Lifelong Machine Learning
Details
Download:
[pdf]
(215.4kB
)
- Lisa Torrey and Matthew E. Taylor. Teaching on a Budget: Agents Advising
Agents in Reinforcement Learning. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
May 2013. 23% acceptance rate
AAMAS-13
Details
Download:
[pdf]
(253.0kB
)
- Haitham Bou Ammar, Karl Tuyls, Matthew E. Taylor, Kurt Driessen, and Gerhard
Weiss. Reinforcement Learning Transfer via Sparse Coding. In International Conference on Autonomous Agents and Multiagent
Systems (AAMAS), June 2012. 20% acceptance rate
AAMAS-12
Details
Download:
[pdf]
(286.7kB
)
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas.
Transfer Learning via Multiple Inter-Task Mappings. In Scott Sanner and Marcus Hutter, editors, Recent Advances
in Reinforcement Learning, Lecture Notes in Artificial Intelligence, pp. 225–236, Springer-Verlag, Berlin, 2012.
Details
Download:
[pdf]
(176.6kB
)
- Lisa Torrey and Matthew E. Taylor. Towards Student/Teacher Learning
in Sequential Decision Tasks. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS),
June 2012. Extended Abstract: 20% acceptance rate for papers, additional 23% for extended abstracts
AAMAS-12
Details
Download:
[pdf]
(138.6kB
)
- Haitham Bou Ammar, Matthew E. Taylor, and Karl Tuyls. Common Sub-Space Transfer
for Reinforcement Learning Tasks (Poster). In The 23rd Benelux Conference on Artificial Intelligence (BNAIC), November
2011. 44% overall acceptance rate
BNAIC-11
Details
Download:
(unavailable)
- Haitham Bou Ammar, Matthew E. Taylor, Karl Tuyls, and Gerhard Weiss. Reinforcement
Learning Transfer using a Sparse Coded Inter-Task Mapping. In Proceedings of the European Workshop on Multi-agent Systems,
November 2011.
EUMAS-11
Details
Download:
[pdf]
(359.2kB
)
- Haitham Bou Ammar and Matthew E. Taylor. Common Subspace Transfer for Reinforcement
Learning Tasks. In Proceedings of the Adaptive and Learning Agents workshop (at AAMAS-11), May 2011.
ALA-11
Details
Download:
[pdf]
(445.0kB
)
- Matthew E. Taylor, Halit Bener Suay, and Sonia Chernova. Integrating
Reinforcement Learning with Human Demonstrations of Varying Ability. In Proceedings of the International Conference
on Autonomous Agents and Multiagent Systems (AAMAS), May 2011. 22% acceptance rate
AAMAS-11
Details
Download:
[pdf]
(157.4kB
)
- Matthew E. Taylor and Peter
Stone. An Introduction to Inter-task Transfer for Reinforcement Learning. AI Magazine, 32(1):15–34,
2011.
Details
Download:
[pdf]
(237.0kB
)
- Matthew E. Taylor, Halit Bener Suay, and Sonia Chernova. Using Human
Demonstrations to Improve Reinforcement Learning. In The AAAI 2011 Spring Symposium --- Help Me Help You: Bridging
the Gaps in Human-Agent Collaboration, March 2011.
HMHY2011
Details
Download:
[pdf]
(116.5kB
)
- Samuel Barrett, Matthew E. Taylor, and Peter
Stone. Transfer Learning for Reinforcement Learning on a Physical Robot. In Proceedings of the Adaptive and
Learning Agents workshop (at AAMAS-10), May 2010.
ALA-10
Details
Download:
[pdf]
(684.9kB
)
- Matthew E. Taylor and Sonia Chernova. Integrating Human Demonstration
and Reinforcement Learning: Initial Results in Human-Agent Transfer. In Proceedings of the Agents Learning Interactively
from Human Teachers workshop (at AAMAS-10), May 2010.
ALIHT-10
Details
Download:
[pdf]
(142.6kB
)
- Matthew E. Taylor. Transfer in Reinforcement Learning Domains,
Studies in Computational Intelligence, Springer-Verlag, 2009.
A book based on my PhD thesis.
Publisher's
Webpage.
Details
Download:
(unavailable)
- Matthew E. Taylor and Peter
Stone. Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research,
10(1):1633–1685, 2009.
Details
Download:
[pdf]
(399.8kB
)
- Matthew E. Taylor and Peter Stone.
Categorizing Transfer for Reinforcement Learning. In Poster at the Multidisciplinary Symposium on Reinforcement
Learning, June 2009.
MSRL-09.
Details
Download:
[pdf]
(144.5kB
)
- Matthew E. Taylor. Assisting Transfer-Enabled Machine Learning Algorithms:
Leveraging Human Knowledge for Curriculum Design. In The AAAI 2009 Spring Symposium on Agents that Learn from Human
Teachers, March 2009.
AAAI 2009 Spring Symposium on Agents
that Learn from Human Teachers
Details
Download:
[pdf]
(39.8kB
)
- Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In Proceedings of the European Conference
on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), pp. 488–505,
September 2008. 19% acceptance rate
ECML-2008
Details
Download:
[pdf]
(304.9kB
)
- Matthew E. Taylor, Gregory Kuhlmann, and Peter
Stone. Autonomous Transfer for Reinforcement Learning. In Proceedings of the Seventh International Joint Conference
on Autonomous Agents and Multiagent Systems (AAMAS), pp. 283–290, May 2008. 22% acceptance rate
AAMAS-2008
Details
Download:
[pdf]
(233.7kB
)
- Matthew E. Taylor, Gregory Kuhlmann, and Peter
Stone. Transfer Learning and Intelligence: an Argument and Approach. In Proceedings of the First Conference
on Artificial General Intelligence (AGI), March 2008. 50% acceptance rate
AGI-2008
A video of talk is available here.
Details
Download:
[pdf]
(149.0kB
)
- Matthew E. Taylor. Autonomous Inter-Task Transfer in Reinforcement
Learning Domains. Ph.D. Thesis, Department of Computer Sciences, The University of Texas at Austin, 2008. Available as
Technical Report UT-AI-TR-08-5.
Details
Download:
[pdf]
(2.3MB
)
- Matthew E. Taylor, Nicholas K. Jong, and Peter
Stone. Transferring Instances for Model-Based Reinforcement Learning. In The Adaptive Learning Agents and Multi-Agent
Systems (ALAMAS+ALAG) workshop at AAMAS, May 2008.
AAMAS
2008 workshop on Adaptive Learning Agents and Multi-Agent Systems
Superseded by the ECML-08 conference paper Transferring
Instances for Model-Based Reinforcement Learning.
Details
Download:
(unavailable)
- Matthew E. Taylor and Peter
Stone. Cross-Domain Transfer for Reinforcement Learning. In Proceedings of the Twenty-Fourth International
Conference on Machine Learning (ICML), June 2007. 29% acceptance rate
ICML-2007
Details
Download:
[pdf]
(220.7kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Transfer via Inter-Task Mappings in Policy Search Reinforcement Learning. In Proceedings of the Sixth
International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 156–163, May 2007. 22%
acceptance rate
AAMAS-2007
Details
Download:
[pdf]
(222.5kB
)
- Matthew E. Taylor, Peter Stone,
and Yaxin Liu. Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. Journal of Machine Learning
Research, 8(1):2125–2167, 2007.
Details
Download:
[pdf]
(499.9kB
)
- Matthew E. Taylor and Peter
Stone. Towards Reinforcement Learning Representation Transfer (Poster). In The Sixth International Joint Conference
on Autonomous Agents and Multiagent Systems (AAMAS), pp. 683–685, May 2007. Poster: 22% acceptance rate for
talks, additional 25% for posters.
AAMAS-2007.
Superseded by the symposium
paper Representation Transfer for
Reinforcement Learning.
Details
Download:
(unavailable)
- Matthew E. Taylor, Gregory Kuhlmann, and Peter
Stone. Accelerating Search with Transferred Heuristics. In ICAPS-07 workshop on AI Planning and Learning,
September 2007.
ICAPS 2007 workshop on AI Planning and Learning
Details
Download:
[pdf]
(139.9kB
)
- Matthew E. Taylor and Peter
Stone. Representation Transfer for Reinforcement Learning. In AAAI 2007 Fall Symposium on Computational
Approaches to Representation Change during Learning and Development, November 2007.
2007
AAAI Fall Symposium: Computational Approaches to Representation Change during Learning and Development
Details
Download:
[pdf]
(144.9kB
)
- Matthew E. Taylor, Shimon Whiteson, and Peter
Stone. Transfer Learning for Policy Search Methods. In ICML workshop on Structural Knowledge Transfer for Machine
Learning, June 2006.
ICML-2006 workshop on Structural Knowledge
Transfer for Machine Learning.
Superseded by the conference paper Transfer
via Inter-Task Mappings in Policy Search Reinforcement Learning.
Details
Download:
(unavailable)
- Shimon Whiteson, Matthew E. Taylor, and Peter
Stone. Adaptive Tile Coding for Reinforcement Learning. In NIPS workshop on: Towards a New Reinforcement Learning?,
December 2006.
NIPS-2006 (Poster).
Superseded by the technical report
Adaptive Tile Coding for Value Function Approximation.
Details
Download:
(unavailable)
- Matthew E. Taylor, Peter Stone,
and Yaxin Liu. Value Functions for RL-Based Behavior Transfer: A Comparative Study. In Proceedings of the Twentieth
National Conference on Artificial Intelligence (AAAI), July 2005. 18% acceptance rate.
AAAI-2005.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
Details
Download:
[pdf]
(147.3kB
)
- Matthew E. Taylor and Peter
Stone. Behavior Transfer for Value-Function-Based Reinforcement Learning. In Proceedings of the Fourth International
Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 53–59, July 2005. 25% acceptance rate.
AAMAS-2005.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
Details
Download:
[pdf]
(230.4kB
)
- Matthew E. Taylor and Peter
Stone. Speeding up Reinforcement Learning with Behavior Transfer. In AAAI 2004 Fall Symposium on Real-life
Reinforcement Learning, October 2004.
Superseded by the journal article Transfer
Learning via Inter-Task Mappings for Temporal Difference Learning.
Details
Download:
[pdf]
(144.9kB
)
Unspecified
- Anestis Fachantidis, Ioannis Partalas, Matthew E. Taylor, and Ioannis Vlahavas.
Transfer Learning via Multiple Inter-Task Mappings. In Proceedings of European Workshop on Reinforcement Learning
(at ECML-11), September 2011.
EWRL-11
Details
Download:
[pdf]
(175.5kB
)
- W. Bradley Knox, Matthew E. Taylor, and Peter
Stone. Understanding Human Teaching Modalities in Reinforcement Learning Environments: A Preliminary Report. In
Proceedings of the Agents Learning Interactively from Human Teachers workshop (at IJCAI-11), July 2011.
ALIHT-11
Details
Download:
[pdf]
(372.4kB
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- Paul Scerri, Balajee Kannan, Pras Velagapudi, Kate Macarthur, Peter Stone,
Matthew E. Taylor, John Dolan, Alessandro Farinelli, Archie Chapman, Bernadine
Dias, and George Kantor. Flood Disaster Mitigation: A Real-world Challenge Problem for Multi-Agent Unmanned Surface Vehicles.
In Proceedings of the Autonomous Robots and Multirobot Systems workshop (at AAMAS-11), May 2011.
ARMS-11
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- Matthew E. Taylor, Manish Jain, Christopher Kiekintveld, Jun-young Kwak,
Rong Yang, Zhengyu Yin, and Milind Tambe. Two Decades of Multiagent Teamwork
Research: Past, Present, and Future. In C. Guttmann, F. Dignum, and M. Georgeff, editors, Collaborative Agents - REsearch
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- Jason Tsai, Natalie Fridman, Emma Bowring, Matthew Brown, Shira Epstein, Gal Kaminka, Stacy Marsella, Andrew Ogden, Inbal
Rika, Ankur Sheel, Matthew E. Taylor, Xuezhi Wang, Avishay Zilka, and
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Emotions, and Social Comparison. In Proceedings of the International Conference on Autonomous Agents and Multiagent
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AAMAS-11
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