Bibliography Available in BibTeΧ.
Book
Essentials of Metaheuristics
Citation:
Sean Luke. 2009. Essentials of Metaheuristics. Available for free at http://cs.gmu.edu/~sean/book/metaheuristics/
Multiagent Behavior and Simulation
Throwing in the Towel: Faithless Bounty Hunters as a Task Allocation Mechanism
PDF
Citation:
Drew Wicke, Ermo Wei, and Sean Luke. 2016. Throwing in the Towel: Faithless Bounty Hunters as a Task Allocation Mechanism. In IJCAI workshop on Interactions with Mixed Agent Types.
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Ant Geometers
PDF
Citation:
Sean Luke, Katherine Russell, and Bryan Hoyle. 2016. Ant Geometers. In 15th International Conference on the Synthesis and Simulation of Living Systems (ALIFE 2016).
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Bounty Hunters and Multiagent Task Allocation
PDF
Citation:
Drew Wicke, David Freelan, and Sean Luke. 2015. Bounty Hunters and Multiagent Task Allocation. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).
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Collaborative Foraging using Beacons
PDF
Note:
This is an updated version of the paper, with some typos and bugfixes corrected from the original AAMAS publication.
Citation:
Brian Hrolenok, Sean Luke, Keith Sullivan, and Christopher Vo. 2010. Collaborative Foraging using Beacons. In Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010). van der Hoek et al, eds. 1197–1204.
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Can You Do Me A Favor?
PDF
Citation:
Keith Sullivan, Sean Luke, and Brian Hrolenok. 2010. Can You Do Me A Favor? In AAMAS 2010 Workshop on Trust in Agent Societies.
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History-based Traffic Control
PDF
Citation:
Gabriel Balan and Sean Luke. 2006. History-based Traffic Control. In Proceedings of the Fifth International Conference on Autonomous Agents and Multi-Agent Systems.
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Tunably Decentralized Algorithms for Cooperative Target Observation
PDF
Citation:
Sean Luke, Keith Sullivan, Liviu Panait, and Gabriel Balan. 2005. Tunably Decentralized Algorithms for Cooperative Target Observation. In Proceedings of the 2005 Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). Pages 911-917.
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Agent-based Dynamics of Social Complexity: Modeling Adaptive Behavior and Long-term Change in Inner Asia
PDF
Citation:
Claudio Cioffi-Revilla, Sean Luke, Dawn C. Parker, J. D. Rogers, W. W. Fitzhugh, W. Honeychurch, B. Frohlich, P. DePriest, and Naran Bazarsad. 2006. Agent-based Dynamics of Social Complexity: Modeling Adaptive Behavior and Long-term Change in Inner Asia. In Proceedings of the First World Congress on Social Simulation.
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MASON: a Multi-agent Simulation Environment
PDF
Citation:
Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, Keith Sullivan, and Gabriel Balan. 2005. MASON: a Multi-agent Simulation Environment. Simulation: Transactions of the society for Modeling and Simulation International. 82(7):517-527.
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Mnemonic Structure and Sociality: a Computational Agent-based Simulation Model
PDF
Citation:
Claudio Cioffi-Revilla, Sean Paus, Sean Luke, James Olds, and Jason Thomas. 2004. Mnemonic Structure and Sociality: a Computational Agent-based Simulation Model. In Proceedings of the Conference on Collective Intentionality IV.
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A Pheromone-based Utility Model for Collaborative Foraging
PDF
Citation:
Liviu Panait and Sean Luke. 2004. A Pheromone-based Utility Model for Collaborative Foraging. In Proceedings of the Third International Joint Conference on Autonomous Angents and Multi Agent Systems (AAMAS), pages 36-43.
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Ant Foraging Revisited
PDF
Citation:
Liviu Panait and Sean Luke. 2004. Ant Foraging Revisited. In Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (ALIFE9), pages 569-574.
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Learning Ant Foraging Behaviors
PDF
Citation:
Liviu Panait and Sean Luke. 2004. Learning ant foraging behaviours. In Jordan Pollack, et al., editors, Artificial Life IX: Ninth International Conference on the Simulation and Synthesis of Living Systems, pages 575-580. The MIT Press.
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Ant Foraging Revisited
PDF
Note:
This one-page poster was considerably extended and improved in a better follow-on paper by the same name: Ant Foraging Revisited.
Citation:
Liviu Panait and Sean Luke, 2003. Ant Foraging Revisited. In Proceedings of the Second International Workshop on the Mathematics and Algorithms of Social Insects, page 184.
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Evolving Foraging Behaviors
PDF
Note:
This small paper was considerably extended and improved in a better follow-on paper, Learning Ant Foraging Behaviors.
Citation:
Liviu Panait and Sean Luke, 2003. Evolving foraging behaviors. In Proceedings of the Second International Workshop on the Mathematics and Algorithms of Social Insects, pages 131-138.
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MASON: A Multiagent Simulation Library
PDF
Citation:
Sean Luke, Gabriel Catalin Balan, Liviu Panait, Claudio Cioffi-Revilla, and Sean Paus. 2003. MASON: A Multiagent Simulation Library. In Proceedings of the Agent 2003 Conference on Challenges in Social Simulation.
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Replication of Sugarscape Using MASON
PDF
Citation:
Anthony Bigbee, Claudio Cioffi-Revilla, and Sean Luke. 2005. Replication of Sugarscape Using MASON. In Agent-based Approaches in Economic and Social Complex systems IV: Post-Proceedings of the AESCS International Workshop. Vo. 3. 183-190. Springer.
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MASON: A New Multi-agent Simulation Toolkit
PDF
Citation:
Sean Luke, Claudio Cioffi-Revilla, Liviu Panait, and Keith Sullivan. 2004. MASON: A New Multi-agent Simulation Toolkit. In Proceedings of the 2004 SwarmFest Workshop.
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MASON: A Java Multi-agent Simulation Library
PDF
Citation:
Sean Luke, Gabriel Catalin Balan, and Liviu Panait. 2003. MASON: A Java Multi-agent Simulation Library. In Proceedings of the Second International Workshop on the Mathematics and Algorithms of Social Insects.
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Multiagent Learning and Fairness
Unlearning from Demonstration
PDF
Citation:
Keith Sullivan, Ahmned ElMolla, Bill Squires, and Sean Luke. 2013. Unlearning from Demonstration. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI13).
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Multiagent Supervised Training with Agent Hierarchies and Manual Behavior Decomposition
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Citation:
Keith Sullivan and Sean Luke. 2011. Multiagent Supervised Training with Agent Hierarchies and Manual Behavior Decomposition. In Proceedings of the IJCAI 2011 Workshop on Agents Learning Interactively from Human Teachers (ALIHT).
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Long-term Fairness with Bounded Worst-case Losses
(Early Workshop Paper) PDF
(Technical Report, Similar to Journal Article) PDF
Note:
This paper began as a workshop paper, then was revised into a journal article. Along the way we created a tech report which is very close to the journal article.
Citation (Workshop Paper):
Gabriel Balan and Dana Richards and Sean Luke. 2008. Long-term Fairness with Bounded Worst-case Losses. In Proceedings of AAAI Advances in Preference Workshop. 7-12.
Citation (Journal Article):
Gabriel Balan and Dana Richards and Sean Luke. 2011. Long-term fairness with bounded worst-case losses. Autonomous Agents and Multiagent Systems. 22(1) 43-63.
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Theoretical Advantages of Lenient Learners: an Evolutionary Game Theoretic Perspective
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Citation:
Liviu Panait, Karl Tuyls, and Sean Luke. 2008. Theoretical Advantages of Lenient Learners: an Evolutionary Game Theoretic Perspective. Journal of Machine Learning Research. 9(March):423-457.
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An Overview of Cooperative and Competitive Multiagent Learning
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Citation:
Pieter Jan 't Hoen, Karl Tuyls, Liviu Panait, Sean Luke, and J. A. La Poutré. 2005. An Overview of Cooperative and Competitive Multiagent Learning. In Learning and Adaptation in Multi-Agent Systems, First International Workshop (LAMAS). 1-46. Springer.
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Can Good Learners Always Compensate for Poor Learners?
PDF
Citation:
Keith Sullivan, Liviu Panait, and Sean Luke. 2006. Can Good Learners Always Compensate for Poor Learners? In Proceedings of the 2006 Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
Note:
A fuller version of this paper may be found in the following technical report (PDF).
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Lenient Learners in Cooperative Multiagent Systems
PDF
Citation:
Liviu Panait, Keith Sullivan, and Sean Luke. 2006. Lenient Learners in Cooperative Multiagent Systems. In Proceedings of the 2006 Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
Note:
A fuller version of this paper may be found in the following technical report (PDF).
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Collaborative Multi-agent Learning: The State of the Art
PDF
Citation:
Liviu Panait and Sean Luke. 2005. Collaborative Multi-agent Learning: The State of the Art. Autonomous Agents and Multi-agent Systems 11:3 (November). 387-434.
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Collaborative Multi-agent Learning: A Survey
PDF
Note:
This is an early version of the much improved survey Collaborative Multi-agent Learning: the State of the Art, which you should read instead.
Citation:
Liviu Panait and Sean Luke. 2004. Collaborative Multi-agent Learning: A Survey. In AAAI Fall Symposium on Multiagent Learning.
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Evolutionary Algorithms
ECJ Then and Now
PDF
Citation:
Sean Luke. 2017. ECJ Then and Now. In GECCO (2017).
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Is the Meta-EA a Viable Optimization Method?
PDF
Citation:
Sean Luke and AKM Khaled Ahsan Talukder. 2013. Is the Meta-EA a Viable Optimization Method? In Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation (GECCO 2013).
Note:
This version has a few very minor corrections made to the original conference publication.
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Multiobjective Optimization of Co-Clustering Ensembles
PDF
Citation:
Francesco Gullo, AKM Khaled Ahsan Talukder, Sean Luke, Carlotta Domeniconi, and Andrea Tagarelli. Multiobjective Optimization of Co-Clustering Ensembles. In GECCO '12: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. Pages 1495-1496. ACM.
Note:
A fuller version of this paper may be found in the following technical report (PDF).
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Finding Interesting Things: Population-based Adaptive Parameter Sweeping
PDF
Citation:
Sean Luke and Deepankar Sharma and Gabriel Catalin Balan. 2007. Finding Interesting Things: Population-based Adaptive Parameter Sweeping. In GECCO '07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. Pages 86-93. ACM.
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Opportunistic Evolution: Efficient Evolutionary Computation on Large-Scale Computational Grids
PDF
Citation:
Keith Sullivan, Sean Luke, Curt Larock, Sean Cier, and Steven Armentrout. 2008. Opportunistic Evolution: Efficient Evolutionary Computation on Large-Scale Computational Grids. In Genetic and Evolutionary Computation Conference Late Breaking Papers.
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An Application of Evolutionary Algorithms to Study the Extent of SLHF Anomaly Associated with Coastal Earthquakes
PDF
Citation:
Guido Cervone, Liviu Panait, Ramesh Singh, and Sean Luke. 2004. An application of evolutionary algorithms to study the extent of SLHF anomaly associated with coastal earthquakes. In Late Breaking Papers of the Genetic and Evolutionary Computation Conference (GECCO). Springer.
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Coevolution
Large Scale Empirical Analysis of Cooperative Coevolution
PDF
Citation:
Sean Luke, Keith Sullivan, and Faisal Abidi. 2011. Large Scale Empirical Analysis of Cooperative Coevolution. In Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation (GECCO 2011). Pages 151-152. ACM.
Note:
A much fuller version of this paper may be found in the following technical report (PDF).
Note:
This is part 1 of a two paper sequence. The second is Do Multiple Trials Help Univariate Methods?
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Do Multiple Trials Help Univariate Methods?
PDF
Citation:
Daniel Rothman, Sean Luke, and Keith Sullivan. 2011. Do Multiple Trials Help Univariate Methods? In Proceedings of the 2011 Congress of Evolutionary Computation, 2011. Pages 2391-2398. IEEE.
Note:
This is part 2 of a two paper sequence. The second is Large Scale Empirical Analysis of Cooperative Coevolution
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Cooperative Coevolution and Univariate Estimation of Distribution Algorithms
PDF
Citation:
Christopher Vo, Liviu Panait, and Sean Luke. 2007. Cooperative Coevolution and Univariate Estimation of Distribution Algorithms. In Proceedings of the 10th ACM SIGEVO Conference on Foundations of Genetic Algorithms (FOGA). Pages 141-150. ACM.
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Biasing Coevolutionary Search for Optimal Multiagent Behaviors
PDF
Citation:
Liviu Panait, Sean Luke, and R. Paul Wiegand. 2006. Biasing Coevolutionary Search for Optimal Multiagent Behaviors. IEEE Transactions on Evolutionary Computation. 10(6):629-645.
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Archive-based Cooperative Coevolutionary Algorithms
PDF
Citation:
Liviu Panait, Keith Sullivan, and Sean Luke. 2006. Archive-based Cooperative Coevolutionary Algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO).
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Selecting Informative Actions Improves Cooperative Multiagent Learning
PDF
Citation:
Liviu Panait and Sean Luke. 2006. Selecting Informative Actions Improves Cooperative Multiagent Learning. In Proceedings of the 2006 Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
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Time-dependent Collaboration Schemes for Cooperative Coevolutionary Algorithms
PDF
Citation:
Liviu Panait and Sean Luke. 2005. Time-dependent Collaboration Schemes for Cooperative Coevolutionary Algorithms. In AAAI 2005 Fall Symposium on Coevolutionary and Coadaptive Systems. 18-25.
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A Visual Demonstration of Convergence Properties of Cooperative Coevolution
PDF
Citation:
Liviu Panait, R. Paul Wiegand, and Sean Luke. 2004. A Visual Demonstration of Convergence Properties of Cooperative Coevolution. In Parallel Problem Solving from Nature (PPSN 2004). Springer. Pages 892-901.
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A Sensitivity Analysis of a Cooperative Coevolutionary Algorithm Biased for Optimization
PDF
Citation:
Liviu Panait, R. Paul Wiegand, and Sean Luke. 2004. A Sensitivity Analysis of a Cooperative Coevolutionary Algorithm Biased for Optimization. In Genetic and Evolutionary Computation Conference (GECCO). Springer. Pages 587-584.
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Methods for Evolving Robust Programs
PDF
Citation:
Liviu Panait and Sean Luke. 2003. Methods for Evolving Robust Programs. In Genetic and Evolutionary Computation (GECCO-2003). Springer. Pages 1740-1751.
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Improving Coevolutionary Search for Optimal Multiagent Behaviors
PDF
Citation:
Liviu Panait, R. Paul Wiegand, and Sean Luke. 2003. Improving Coevolutionary Search for Optimal Multiagent Behaviors. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03). Georg Gottlob and Toby Walsh, editors. Pages 653-658.
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Guaranteeing Coevolutionary Objective Measures
PDF
Citation:
Sean Luke and R. Paul Wiegand. 2002. Guaranteeing Coevolutionary Objective Measures. In Foundations of Genetic Algorithms VII. Pages 237-251. Morgan Kaufman.
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A Comparison of Two Competitive Fitness Functions
PDF
Citation:
Liviu Panait and Sean Luke. 2002. A Comparison of Two Competitive Fitness Functions. In GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. W. B. Langdon et al, eds. Morgan Kauffman. 503-511.
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When Coevolutionary Algorithms Exhibit Evolutionary Dynamics
PDF
Citation:
Sean Luke and R. Paul Wiegand. 2002. When Coevolutionary Algorithms Exhibit Evolutionary Dynamics. In the Workshop on Understanding Coevolution: Theory and Analysis of Coevolutionary Algorithms (at GECCO 2002). A. Barry, ed. 236-241.
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Genetic Programming
Genetic Programming Needs Better Benchmarks
PDF
Note:
This is the third version of the paper, and differs from the original publication slightly, by fixing a few errors in certain benchmark specifications.
Note:
This paper was produced in concert with gpbenchmarks.org
Note:
The symbolic regression benchmarks in this paper may be found in ECJ.
Citation:
James McDermott and David R. White and Sean Luke and Luca Manzoni and Mauro Castelli and Leonardo Vanneschi and Wojciech Jaśkowski and Krzysztof Krawiec and Robin Harper and Kenneth De Jong and Una-May O'Reilly. 2012. Genetic Programming Needs Better Benchmarks. In GECCO '12: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. ACM.
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Evolving Kernels for Support Vector Machine Classification
PDF
Citation:
Keith M. Sullivan and Sean Luke. 2007. Evolving Kernels for Support Vector Machine Classification. In GECCO '07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. Pages 1702-1707. ACM.
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A Demonstration of Neural Programming Applied to Non-Markovian Problems
PDF
Citation:
Gabriel Catalin Balan and Sean Luke. 2004. A Demonstration of Neural Programming Applied to Non-Markovian Problems. In Genetic and Evolutionary Computation Conference (GECCO). Springer. Pages 422-433.
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Population Implosion in Genetic Programming
PDF
Citation:
Sean Luke, Gabriel Catalin Balan, and Liviu Panait. 2003. Population Implosion in Genetic Programming. In Genetic and Evolutionary Computation (GECCO-2003). Springer. Pages 1729-1739.
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Is the Perfect the Enemy of the Good?
PDF
Citation:
Sean Luke and Liviu Panait. 2002. Is the Perfect the Enemy of the Good? In GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. W. B. Langdon et al, eds. Morgan Kauffman. 820-828.
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When Short Runs Beat Long Runs
PDF
Citation:
Sean Luke. 2001. When short runs beat long runs. In GECCO-2001: Proceedings of the Genetic and Evolutionary Computation Conference. Lee Spector et al, eds. Morgan Kaufmann. 74-80.
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A Survey and Comparison of Tree Generation Algorithms
PDF
Citation:
Sean Luke and Liviu Panait. 2001. A survey and comparison of tree generation algorithms. In GECCO-2001: Proceedings of the Genetic and Evolutionary Computation Conference. Lee Spector et al, eds. Morgan Kaufmann. 81-88.
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Two Fast Tree-Creation Algorithms for Genetic Programming
PDF
Citation:
Sean Luke. 2000. Two fast tree-creation algorithms for genetic programming. In IEEE Transactions on Evolutionary Computation 4:3 (September 2000), 274-283. IEEE.
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“Genetic” Programming
PDF
Citation:
Sean Luke, Shugo Hamahashi, and Hiroaki Kitano. 1999. "Genetic" programming. In GECCO-99: Proceedings of the Genetic and Evolutionary Computation Conference, Banzhaf, W. et al, eds. San Fransisco: Morgan Kaufmann.
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A Revised Comparison of Crossover and Mutation in Genetic Programming
PDF
Note:
This paper is a revision of a previous paper, with statistical correction and a considerable new set of data. However, the original also has some data that does not appear here, so you may want to consider getting both.
Citation:
Sean Luke and Lee Spector. 1998. A Revised Comparison of Crossover and Mutation in Genetic Programming. In Proceedings of the Third Annual Genetic Programming Conference (GP98). J. Koza et al, eds. 208-213. San Fransisco: Morgan Kaufmann.
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A Comparison of Crossover and Mutation in Genetic Programming
PDF
Note:
This paper has been superceeded by A Revised Comparison of Crossover and Mutation in Genetic Programming. The revised version has new data which corrects some statistical flaws in the original.
Citation:
Sean Luke and Lee Spector. 1997. A Comparison of Crossover and Mutation in Genetic Programming. In Genetic Programming 1997: Proceedings of the Second Annual Conference (GP97). J. Koza et al, eds. San Fransisco: Morgan Kaufmann. 240-248.
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Evolving Teamwork and Coordination with Genetic Programming
PDF
Citation:
Sean Luke and Lee Spector. 1996. Evolving Teamwork and Coordination with Genetic Programming. In Genetic Programming 1996: Proceedings of the First Annual Conference.. J. Koza et al, eds. Cambridge: MIT Press. 141-149.
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Cultural Transmission of Information in Genetic Programming
PDF
Note:
Discussion of this paper appeared as part of the Scientific American (Oct. 96) column "Computing: Programming with Primordial Ooze" (p. 50).
Citation:
Lee Spector and Sean Luke. 1996. Cultural Transmission of Information in Genetic Programming. In Genetic Programming 1996: Proceedings of the First Annual Conference.. J. Koza et al, eds. Cambridge: MIT Press. 200-208.
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Culture Enhances the Evolvability of Cognition
PDF
Citation:
Lee Spector and Sean Luke. 1996. Culture Enhances the Evolvability of Cognition. In Cognitive Science 1996 Conference Proceedings (CogSci96).
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Evolving Graphs and Networks with Edge Encoding: Preliminary Report
PDF
Citation:
Sean Luke and Lee Spector. 1996. Evolving Graphs and Networks with Edge encoding: Preliminary Report. In Late Breaking Papers at the Genetic Programming 1996 Conference (GP96). J. Koza, ed. Stanford: Stanford Bookstore. 117-124.
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Code Bloat
A Comparison of Bloat Control Methods for Genetic Programming
PDF
Citation:
Sean Luke and Liviu Panait. 2006. A Comparison of Bloat Control Methods for Genetic Programming. Evolutionary Computation. 14(13):309-344.
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Alternative Bloat Control Methods
PDF
Citation:
Liviu Panait and Sean Luke. 2004. Alternative Bloat Control Methods. In Genetic and Evolutionary Computation Conference (GECCO). Springer. Pages 630-641.
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Modification Point Depth and Genome Growth in Genetic Programming
PDF
Citation:
Sean Luke. 2003. Modification Point Depth and Genome Growth in Genetic Programming. Evolutionary Computation. 11(1):67-106.
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Fighting Bloat With Nonparametric Parsimony Pressure
PDF
Citation:
Sean Luke and Liviu Panait. 2002. Fighting Bloat With Nonparametric Parsimony Pressure. In Parallel Problem Solving from Nature - PPSN VII (LNCS 2439). Juan Julian Merelo Guervos et al, eds. Springer Verlag. 411-421.
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Lexicographic Parsimony Pressure
PDF
Citation:
Sean Luke and Liviu Panait. 2002. Lexicographic Parsimony Pressure. In GECCO-2002: Proceedings of the Genetic and Evolutionary Computation Conference. W. B. Langdon et al, eds. Morgan Kauffman. 829-836.
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Code Growth Is Not Caused By Introns
PDF
Citation:
Sean Luke. 2000. Code growth is not caused by introns. In Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference (GECCO-2000). 228-235.
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Robotics
Portable Sensor Motes as a Distributed Communication Medium for Large Groups of Mobile Robots
PDF
Citation:
Sean Luke and Katherine Russell. 2016. Portable Sensor Motes as a Distributed Communication Medium for Large Groups of Mobile Robots. In NATO Specialists Meeting on Swarm Centric Solution for Intelligent Sensor Networks (SET-222).
Note:
This is a short position paper summarizing our past work on using sensor motes as beacons for pheromone robotics.
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Supporting Mobile Swarm Robotics in Low Power and Lossy Sensor Networks
PDF
Citation:
Kevin Andrea, Robert Simon, and Sean Luke. 2016. Supporting Mobile Swarm Robotics in Low Power and Lossy Sensor Networks. In NATO Specialists Meeting on Swarm Centric Solution for Intelligent Sensor Networks (SET-222).
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Swarm Robot Foraging with Wireless Sensor Motes
PDF
Citation:
Katherine Russell, Michael Schader, Kevin Andrea, and Sean Luke. 2015. Swarm Robot Foraging with Wireless Sensor Motes. In International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015).
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Training Heterogeneous Teams of Robots
PDF
Citation:
Keith Sullivan, Ermo Wei, Bill Squires, Drew Wicke, and Sean Luke. 2015. Training Heterogeneous Teams of Robots In Autonomous Robots and Multibot Systems Workshop (ARMS).
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RoboPatriots: George Mason University 2015 RoboCup Team
PDF
Citation:
David Freelan, Drew Wicke, Chris Burns, Carl Walker, Laura Hovatter, Colin Ward, Daniel Lofaro, and Sean Luke. 2015. RoboPatriots: George Mason University 2015 RoboCup Team. In Proceedings of the 2015 RoboCup Workshop.
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Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition
PDF
Citation:
David Freelan, Drew Wicke, Keith Sullivan, and Sean Luke. 2014. Towards Rapid Multi-robot Learning from Demonstration at the RoboCup Competition. In Proceedings of the 2014 RoboCup Workshop.
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RoboPatriots: George Mason University 2014 RoboCup Team
PDF
Citation:
David Freelan, Drew Wicke, Chau Thai, Joshua Snider, Anna Papadogiannakis, and Sean Luke. 2014. RoboPatriots: George Mason University 2014 RoboCup Team. In Proceedings of the 2014 RoboCup Workshop.
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Real-Time Training of Team Soccer Behaviors
PDF
Citation:
Keith Sullivan and Sean Luke. 2012. Real-Time Training of Team Soccer Behaviors. In Proceedings of the 2012 RoboCup Workshop.
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RoboPatriots: George Mason University 2012 RoboCup Team
PDF
Citation:
Keith Sullivan, Katherine Russell, Kevin Andrea, Barak Stout, and Sean Luke. 2012. RoboPatriots: George Mason University 2012 RoboCup Team. In Proceedings of the 2012 RoboCup Workshop.
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Learning from Demonstration with Swarm Hierarchies
PDF
Citation:
Keith Sullivan and Sean Luke. 2012. Learning from Demonstration with Swarm Hierarchies. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS).
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RoboPatriots: George Mason University 2011 RoboCup Team
PDF
Citation:
Keith Sullivan, Christopher Vo, and Sean Luke. 2011. RoboPatriots: George Mason University 2011 RoboCup Team In Proceedings of the 2011 RoboCup Workshop.
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Hierarchical Learning from Demonstration on Humanoid Robots
PDF
Citation:
Keith Sullivan, Sean Luke, and Vittorio Ziparo. 2010. Hierarchical Learning from Demonstration on Humanoid Robots. In Proceedings of the Humanoid Robots Learning from Interaction Workshop, at Humanoids 2010.
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RoboPatriots: George Mason University 2010 RoboCup Team
PDF
Citation:
Keith Sullivan, Christopher Vo, Sean Luke, and Jyh-Ming Lien. 2010. RoboPatriots: George Mason University 2010 RoboCup Team In Proceedings of the 2010 RoboCup Workshop.
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Learn to Behave! Rapid Training of Behavior Automata
PDF
Citation:
Sean Luke and Vittorio Ziparo. 2010. Learn to Behave! Rapid Training of Behavior Automata. In Proceedings of the Adaptive and Learning Agents Workshop at AAMAS 2010. Marek Grześ and Matthew Taylor, editors. 61–68.
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RoboPatriots: George Mason University 2009 RoboCup Team
PDF
Citation:
Keith Sullivan, Brian Davidson, Christopher Vo, Brian Hrolenok, and Sean Luke. 2009. RoboPatriots: George Mason University 2009 RoboCup Team. In Proceedings of the 2009 RoboCup Workshop.
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RoboPatriots: George Mason University 2008 RoboCup Team
PDF
Citation:
Keith Sullivan, Brian Davidson, Christopher Vo, Brian Hrolenok, and Sean Luke. 2008. RoboPatriots: George Mason University 2008 RoboCup Team. In Proceedings of the 2008 RoboCup Workshop.
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Three RoboCup Simulation League Commentator Systems
PDF
Citation:
E. Elisabeth Andre, Kim Binsted, Kumiko Tanaka-Ishii, Sean Luke, Gerd Herzog, and Thomas Rist. 2000. Three RoboCup simulation league commentator systems. In AI Magazine, 21:1 (Spring 2000), 57-66. AAAI.
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Character Design for Soccer Commentary
PDF
Citation:
Kim Binsted and Sean Luke. 1998. Character design for soccer commentary. In Robot Soccer World Cup II: Proceedings of the second RoboCup Workshop, H. Kitano, ed. 23-35. Springer-Verlag.
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Genetic Programming Produced Competitive Soccer Softbot Teams for RoboCup97
PDF
Note:
This paper is similar to an earlier workshop paper. The key difference being that the workshop paper, which was not for a Genetic Programming audience, is short on experimental details and long on introductions to how GP works. There also exists a short invited paper detailing how this experiment could have been improved. Also available is a short sidebar for an AI Magazine article.
Citation:
Sean Luke. 1998. Genetic Programming Produced Competitive Soccer Softbot Teams for RoboCup97. In Proceedings of the Third Annual Genetic Programming Conference (GP98). J. Koza et al, eds. 204-222. San Fransisco: Morgan Kaufmann.
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Evolving SoccerBots: A Retrospective
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Note:
This short invited paper was meant to complement the more complete GP98 and RoboCup97 papers, and an AI Magazine sidebar, by discussing things that could have been improved from our previous attempt.
Citation:
Sean Luke. 1998. Evolving soccerbots: a retrospective. In Proceedings of 12th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI).
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Co-evolving Soccer Softbot Team Coordination with Genetic Programming
PDF
Note:
Also available is a short sidebar for an AI Magazine article. There also exists a similar paper written for GP97 which discusses more of the project implementation details but spends little time explaining what Genetic Programming is and how it works. And finally, there exists a short invited paper detailing how this experiment could have been improved.
Citation:
Luke, Charles Hohn, Jonathan Farris, Gary Jackson, and James Hendler. 1998. Co-evolving Soccer Softbot Team Coordination with Genetic Programming. In RoboCup-97: Robot Soccer World Cup I (Lecture Notes in Artificial Intelligence No. 1395), H. Kitano, ed. Berlin: Springer-Verlag. 398-411.
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Coevolving Soccer Softbots
PDF
Note:
This is a short sidebar about the RoboCup project. A more complete paper can be found here.
Citation:
Sean Luke. 1998. Coevolving Soccer Softbots. In AI Magazine 19:3 (Fall 1998), pp. 54.
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Biological Modeling
Evolutionary Computation and the C-value Paradox
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Citation:
Sean Luke. 2005. Evolutionary Computation and the C-value Paradox. In Proceedings of the 2005 Genetic and Evolutionary Computation Conference (GECCO). Pages 91-97.
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The Perfect C. elegans Project: An Initial Report
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Citation:
Hiroaki Kitano, Shugo Hamahashi, and Sean Luke. 1998. The perfect C. elegans project: an initial report. In Artificial Life, 4:2 (Spring 1998), 141-156. MIT Press.
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Biology: See It Again — for the First Time
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Citation:
Sean Luke, Shugo Hamahashi, Koji Kyoda, and Hiroki Ueda. 1998. Biology: See It Again—for the First Time. In IEEE Intelligent Systems. 13:5 (September/October 1998). 6-8.
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Knowledge Representation
SHOE: A Blueprint for the Semantic Web
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Citation:
Jeff Heflin, James Hendler, and Sean Luke. 2003. SHOE: A Blueprint for the Semantic Web. In Dieter Fensel, James Hendler, Henry Lieberman, and Wolfgang Wahlster, editors, Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. Pages 29-64. MIT Press.
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Coping with Changing Ontologies in a Distributed Environment
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Citation:
Jeff Heflin, James Hendler, and Sean Luke. 1999. Coping with Changing Ontologies in a Distributed Environment. Ontology Management. Papers from the AAAI Workshop. WS-99-13. AAAI Press. pp. 74-79.
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Applying Ontology to the Web: A Case Study
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Citation:
Jeff Heflin, James Hendler, and Sean Luke. 1999. Applying Ontology to the Web: A Case Study. In J. Mira, J. Sanchez-Andres (Eds.), International Work-Conference on Artificial and Natural Neural Networks (IWANN). Vol 2. Springer, Berlin. pp. 715-724.
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SHOE: A Knowledge Representation Language for Internet Applications
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Note:
A revised version of this paper formed Chapter 2 ("SHOE: A Blueprint for the Semantic Web") of the book Spinning the Semantic Web, edited by Dieter Fensel, James Hendler, Henry Lieberman, and Wolfgang Wahlster. MIT Press. 2003. ISBN 0-262-06232-1
Also Available As:
UMCP-CSD Technical Report CS-TR-4078, or UMCP-UMIACS Technical Report UMIACS-TR-99-71, accessible online at the Library of the Department of Computer Science, University of Maryland at College Park.
Citation:
Jeff Heflin, James Hendler, and Sean Luke. 1999. SHOE: A Knowledge Representation Language for Internet Applications. Technical Report CS-TR-4078. Department of Computer Science, University of Maryland at College Park.
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Reading Between the Lines: Using SHOE to Discover Implicit Knowledge from the Web
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Citation:
Jeff Heflin, James Hendler, and Sean Luke. 1998. Reading between the lines: using SHOE to discover implicit knowledge from the web. In AI and Information Integration, Papers from the 1998 Workshop. (WS-98-14). AAAI Press. 51-57.
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Web Agents That Work
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Note:
This article covers similar issues as the paper, Ontology-based Web Agents.
Citation:
Sean Luke and James Hendler. 1997. Web Agents That Work. In IEEE Multimedia. 4:3 (July-September 1997). IEEE Press. 76-80.
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Ontology-based Web Agents
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Citation:
Sean Luke, Lee Spector, David Rager, and James Hendler. 1997. Ontology-based Web Agents. In Proceedings of the First International Conference on Autonomous Agents (AutonomousAgents97). W. L. Johnson, ed. New York: Association for Computing Machinery. 59-66.
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Ontology-Based Knowledge Discovery on the World-Wide Web
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Note:
This paper has been superceeded by Ontology-based Web Agents
Citation:
Luke, Lee Spector, and David Rager. 1996. Ontology-Based Knowledge Discovery on the World-Wide Web. In Working Notes of the Workshop on Internet-Based Information Systems at the 13th National Conference on Artificial Intelligence (AAAI96). A. Franz and H. Kitano, eds. AAAI. 96-102.
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Using the Parka Parallel Knowledge Representation System (Version 3.2)
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Also Available As:
UMCP-CSD Technical Report CS-TR-3485, accessible online at the Library of the Department of Computer Science, University of Maryland at College Park.
Citation:
William Anderson, Brian Kettler, Sean Luke, and James Hendler. 1995. Using the Parka Parallel Knowledge Representation System (Version 3.2). Technical Report. Department of Computer Science, University of Maryland at College Park.
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Dissertation
Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat
(Recommended) Double-Sided Version in PDF
Single-Sided Version in PDF

Citation:
Sean Luke. 2000. Issues in Scaling Genetic Programming: Breeding Strategies, Tree Generation, and Code Bloat. Ph.D. Dissertation, Department of Computer Science, University of Maryland, College Park, Maryland.
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Errata:
  1. In Algorithm 2 (p. 6), the line P<-P\{q} should read P<-P\{s}

  2. Figures 5.2 through 5.5 (p. 38-39) are not in proper evolutionary-time order. The proper order is 5.4, 5.5, 5.2, 5.3.