InteLLigence
 
Research on Machine Learning
Description
Machine Learning aims to enable machines to adapt under unknown and uncertain conditions. Our research spans various aspects of machine learning, such as clustering and classification, however emphasis is placed on reinforcement learning, whereby an agent learns how to act rationally in an unknown environment through trial and error. Our goal is to design efficient learning algorithms that make full utilization of the training data and converge quickly to the desired result.
Related Publications
  • Xenou K., Chalkiadakis G., Afantenos S.: Deep Reinforcement Learning in Strategic Board Game Environments, In Proc. of the 16th European Conference on Multi-Agent Systems (EUMAS-2018), Bergen, Norway, December 2018.
    Publication Type: Conference Publications [abstract] [link]
  • Kotti M., Diakoloukas V., Papangelis A., Lagoudakis M., Stylianou Y.: A Case Study on the Importance of Belief State Representation for Dialogue Policy Management, Proceedings of Interspeech 2018, Hyberabad, India, September 2018, pp. 986-990.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M.: Least-Squares Reinforcement Learning Methods, in Claude Sammut and Geoffrey I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2015, pp. 1-9.
    Publication Type: Book Chapters [abstract] [link]
  • Lagoudakis M.: Value Function Approximation, in Claude Sammut and Geoffrey I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2015, pp. 1-15.
    Publication Type: Book Chapters [abstract] [link]
  • Panagopoulos A., Chalkiadakis G., Jennings N.: Towards Optimal Solar Tracking: A Dynamic Programming Approach, Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-2015), Austin, Texas, USA, January 2015
    Publication Type: Conference Publications [abstract] [file]
  • Rexakis I., Lagoudakis M.: Directed Policy Search for Decision Making Using Relevance Vector Machines, International Journal on Artificial Intelligence Tools (JAIT), 23 (4), 2014, pp. 1-21.
    Publication Type: Journal Publications [abstract] [link][file]
  • Babas K., Chalkiadakis G., Tripolitakis E.: You Are What You Consume: A Bayesian Method for Personalized Recommendations, In Proceedings of the 7th ACM Conference on Recommender Systems (ACM RecSys 2013).
    Publication Type: Conference Publications [abstract] [file]
  • Rexakis I., Lagoudakis M.: Directed Policy Search using Relevance Vector Machines, Proceedings of the 2012 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Athens, Greece, November 2012, pp. 25-32. Best Student Paper Award [certificate]
    Publication Type: Conference Publications [abstract] [link][file]
  • Skoulakis I., Lagoudakis M.: Efficient Reinforcement Learning in Adversarial Games, Proceedings of the 2012 IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Athens, Greece, November 2012, pp. 704-711.
    Publication Type: Conference Publications [abstract] [link][file]
  • Matthews T., Ramchurn S., Chalkiadakis G.: Competing with Humans in Fantasy Football: Team Formation in Large Partially Observable Domains, In Proceedings of the 26th AAAI Conference on Artificial Intelligence (AAAI-2012), Toronto, ON, Canada, July 2012.
    Publication Type: Conference Publications [abstract] [file]
  • Teacy W., Chalkiadakis G., Farinelli A., Rogers A., Jennings N., McClean S., Parr G.: Decentralized Bayesian Reinforcement Learning for Online Agent Collaboration, In Proc. of the 11th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS-2012), Valencia, Spain
    Publication Type: Conference Publications [abstract] [file]
  • Rexakis I., Lagoudakis M.: Directed Exploration of Policy Space using Support Vector Classifiers, Proceedings of the 2011 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), Paris, France, April 2011, pp. 112-119.
    Publication Type: Conference Publications [abstract] [link][file]
  • Pazis J., Lagoudakis M.: Reinforcement Learning in Multidimensional Continuous Action Spaces, Proceedings of the 2011 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL 2011), Paris, France, April 2011, pp. 97-104.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M.: Least-Squares Reinforcement Learning Methods , in Claude Sammut and Geoffrey I. Webb (Eds.), Encyclopedia of Machine Learning, Springer, 2010, pp. 595-600.
    Publication Type: Book Chapters [abstract] [link]
  • Lagoudakis M.: Value Function Approximation, in Claude Sammut and Geoffrey I. Webb (Eds.), Encyclopedia of Machine Learning, Springer, 2010, pp. 1011-1021.
    Publication Type: Book Chapters [abstract] [link]
  • Vasilikos V., Lagoudakis M.: Optimization of Heuristic Search using Recursive Algorithm Selection and Reinforcement Learning , Annals of Mathematics and Artificial Intelligence, 60 (1-2), 2010, pp. 119-151.
    Publication Type: Journal Publications [abstract] [link]
  • Rovatsou M., Lagoudakis M.: Minimax Search and Reinforcement Learning for Adversarial Tetris, Proceedings of the 6th Hellenic Conference on Artificial Intelligence (SETN'10), Athens, Greece, May 2010, pp. 417-422.
    Publication Type: Conference Publications [abstract] [link][file]
  • Korokithakis S., Lagoudakis M.: Heuristic Rule Induction for Decision Making in Near-Deterministic Domains, Proceedings of the 6th Hellenic Conference on Artificial Intelligence (SETN'10), Athens, Greece, May 2010, pp. 339-344.
    Publication Type: Conference Publications [abstract] [link][file]
  • Rachelson E., Lagoudakis M.: On the Locality of Action Domination in Sequential Decision Making, Proceedings of the 11th International Symposium on Artificial Intelligence and Mathematics (ISAIM), Ft. Lauderdale, FL, USA, January 2010.
    Publication Type: Conference Publications [abstract] [link][file]
  • Pazis J., Lagoudakis M.: Binary Action Search for Learning Continuous-Action Control Policies, Proceedings of the 26th International Conference on Machine Learning (ICML), Montreal, Quebec, Canada, June 2009, pp. 793–800.
    [video of the presentation at ICML]
    Publication Type: Conference Publications [abstract] [link][file]
  • Pazis J., Lagoudakis M.: Learning Continuous-Action Control Policies, Proceedings of the 2009 IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), Nashville, TN, USA, March 2009, pp. 169-176.
    Publication Type: Conference Publications [abstract] [link][file]
  • Rexakis I., Lagoudakis M.: Classifier-Based Policy Representation, Proceedings of the 2008 IEEE International Conference on Machine Learning and Applications (ICMLA'08), San Diego, CA, USA, December 2008, pp. 91-98.
    Publication Type: Conference Publications [abstract] [link][file]
  • Dimitrakakis C., Lagoudakis M.: Rollout Sampling Approximate Policy Iteration, Machine Learning 72 (3), 2008, pp. 157-171.
    Publication Type: Journal Publications [abstract] [link][file]
  • Dimitrakakis C., Lagoudakis M.: Rollout Sampling Approximate Policy Iteration, (Extended Abstract) Proceedings of the 2008 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2008), Antwerp, Belgium, September 2008, pp. 7.
    Publication Type: Workshop Proceedings [abstract] [link][file]
  • Lagoudakis M., Parr R.: Least-Squares Policy Iteration, Journal of Machine Learning Research (JMLR), 4, 2003, pp. 1107-1149.
    Publication Type: Journal Publications [abstract] [link][file]
  • Lagoudakis M., Parr R.: Reinforcement Learning as Classification: Leveraging Modern Classifiers, Proceedings of the 20th International Conference on Machine Learning (ICML-03), Washington, DC, U.S.A., August 2003, pp. 424-431.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M., Parr R.: Approximate Policy Iteration using Large-Margin Classifiers, Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, Mexico, August 2003, pp. 1432-1434.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M., Parr R.: Learning in Zero-Sum Team Markov Games using Factored Value Functions, Proceedings of NIPS*2002: Neural Information Processing Systems, Vancouver, BC, Canada, December 2002, pp. 1659-1666.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M., Parr R.: Value Function Approximation in Zero-Sum Markov Games, Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI-02), Edmonton, AB, Canada, August 2002, pp. 283-292.
    Publication Type: Conference Publications [abstract] [link][file]
  • Guestrin C., Lagoudakis M., Parr R.: Coordinated Reinforcement Learning, Proceedings of the 19th International Conference on Machine Learning (ICML-02), Sydney, Australia, July 2002, pp. 227-234.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M., Parr R., Littman M.: Least-Squares Methods in Reinforcement Learning for Control, Lecture Notes on Artificial Intelligence, Vol. 2308, Springer, Proceedings of the 2nd Hellenic Conference on Artificial Intelligence (SETN-02), Thessaloniki, Greece, April 2002, pp. 249-260.
    Publication Type: Conference Publications [abstract] [link][file]
  • Guestrin C., Lagoudakis M., Parr R.: Coordinated Reinforcement Learning, Proceedings of the 2002 AAAI Spring Symposium Series: Collaborative Learning Agents, Stanford, CA, USA, March 2002.
    Publication Type: Miscellaneous [abstract] [file]
  • Lagoudakis M., Parr R.: Model-Free Least-Squares Policy Iteration, Proceedings of NIPS*2001: Neural Information Processing Systems, Vancouver, BC, Canada, December 2001, pp. 1547-1554.
    Publication Type: Conference Publications [abstract] [link][file]
  • Lagoudakis M., Littman M., Parr R.: Selecting the Right Algorithm, Proceedings of the 2001 AAAI Fall Symposium Series: Using Uncertainty within Computation, Cape Cod, MA, USA, November 2001.
    Publication Type: Miscellaneous [abstract] [file]
  • Lagoudakis M., Littman M.: Learning to Select Branching Rules in the DPLL Procedure for Satisfiability, Electronic Notes in Discrete Mathematics (ENDM), Vol. 9, Elsevier, LICS 2001 Workshop on Theory and Applications of Satisfiability Testing (SAT-2001), Boston, MA, USA, June 2001.
    Publication Type: Conference Publications [abstract] [file]
  • Lagoudakis M., Littman M.: Reinforcement Learning for Algorithm Selection, (Student Abstract), Proceedings of the 17th National Conference on Artificial Intelligence (AAAI-00), Austin, TX, USA, July 2000, pp. 1081.
    Publication Type: Workshop Proceedings [abstract] [link][file]
  • Lagoudakis M., Littman M.: Algorithm Selection using Reinforcement Learning, Proceedings of the 17th International Conference on Machine Learning (ICML-00), Stanford, CA, USA, June 2000, pp. 511-518.
    Publication Type: Conference Publications [abstract] [link][file]