Scholars@Duke

• Background
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  Education, Training, & Certifications

  • Ph.D., University of Massachusetts - Amherst 2011
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  Previous Appointments & Affiliations

  • Assistant Professor of Computer Science, Computer Science, Trinity College of Arts & Sciences 2014 - 2016
  • Assistant Professor of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2014 - 2016
• Recognition
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  In the News

  • JUL 14, 2017

    Helping Robots Learn to See in 3-D
• Research
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  Selected Grants

  • Director's Fellowship: Bridging the Gap between Low-level Robot Control and Flexible High-level Task Planning awarded by Defense Advanced Research Projects Agency 2015 - 2019
  • Bridging the Gap between Low-level Robot Control and Flexible High-level Task Planning awarded by Defense Advanced Research Projects Agency 2015 - 2018
  • CRCNS: Representational Foundations of Adaptive Behavior in Natural and Artificial Agents awarded by Princeton University 2015 - 2018
• Publications & Artistic Works
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  Selected Publications

  • Academic Articles

    • Kroemer, O., S. Niekum, and G. Konidaris. “A review of robot learning for manipulation: Challenges, representations, and algorithms.” Journal of Machine Learning Research 22 (January 1, 2021).
    • Abbatematteo, B., E. Rosen, S. Tellex, and G. Konidaris. “Bootstrapping Motor Skill Learning with Motion Planning.” Ieee International Conference on Intelligent Robots and Systems, January 1, 2021, 4926–33. https://doi.org/10.1109/IROS51168.2021.9636661.
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    • Asadi, K., N. Parikh, R. E. Parr, G. D. Konidaris, and M. L. Littman. “Deep Radial-Basis Value Functions for Continuous Control.” 35th Aaai Conference on Artificial Intelligence, Aaai 2021 8A (January 1, 2021): 6696–6704.
    • Keller, B., M. Draelos, K. Zhou, R. Qian, A. N. Kuo, G. Konidaris, K. Hauser, and J. A. Izatt. “Optical Coherence Tomography-Guided Robotic Ophthalmic Microsurgery via Reinforcement Learning from Demonstration.” Ieee Transactions on Robotics 36, no. 4 (August 1, 2020): 1207–18. https://doi.org/10.1109/TRO.2020.2980158.
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    • James, S., B. Rosman, and G. Konidaris. “Learning portable representations for high-level planning.” 37th International Conference on Machine Learning, Icml 2020 PartF168147-6 (January 1, 2020): 4632–41.
    • Cohen, V., B. Burchfiel, T. Nguyen, N. Gopalan, S. Tellex, and G. Konidaris. “Grounding Language Attributes to Objects using Bayesian Eigenobjects.” Ieee International Conference on Intelligent Robots and Systems, November 1, 2019, 1187–94. https://doi.org/10.1109/IROS40897.2019.8968603.
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    • Konidaris, G. “On the necessity of abstraction.” Current Opinion in Behavioral Sciences 29 (October 1, 2019): 1–7. https://doi.org/10.1016/j.cobeha.2018.11.005.
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    • Rosen, E., D. Whitney, E. Phillips, G. Chien, J. Tompkin, G. Konidaris, and S. Tellex. “Communicating and controlling robot arm motion intent through mixed-reality head-mounted displays.” International Journal of Robotics Research 38, no. 12–13 (October 1, 2019): 1513–26. https://doi.org/10.1177/0278364919842925.
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    • Amato, C., G. Konidaris, L. P. Kaelbling, and J. P. How. “Modeling and planning with macro-actions in decentralized POMDPs.” Journal of Artificial Intelligence Research 64 (March 1, 2019): 817–59. https://doi.org/10.1613/jair.1.11418.
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    • Zhou, Y., B. Burchfiel, and G. Konidaris. “Representing, learning, and controlling complex object interactions.” Autonomous Robots 42, no. 7 (October 1, 2018): 1355–67. https://doi.org/10.1007/s10514-018-9740-7.
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    • Nelson, Eliza L., Neil E. Berthier, and George D. Konidaris. “Handedness and Reach-to-Place Kinematics in Adults: Left-Handers Are Not Reversed Right-Handers.” Journal of Motor Behavior 50, no. 4 (July 2018): 381–91. https://doi.org/10.1080/00222895.2017.1363698.
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    • Konidaris, G., L. P. Kaelbling, and T. Lozano-Perez. “From skills to symbols: Learning symbolic representations for abstract high-level planning.” Journal of Artificial Intelligence Research 61 (January 1, 2018): 215–89. https://doi.org/10.1613/jair.5575.
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    • Wookey, D. S., and G. D. Konidaris. “Regularized feature selection in reinforcement learning.” Machine Learning 100, no. 2–3 (September 17, 2015): 655–76. https://doi.org/10.1007/s10994-015-5518-8.
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    • Amato, C., G. Konidaris, G. Cruz, C. A. Maynor, J. P. How, and L. P. Kaelbling. “Planning for decentralized control of multiple robots under uncertainty.” Proceedings  Ieee International Conference on Robotics and Automation 2015-June, no. June (June 29, 2015): 1241–48. https://doi.org/10.1109/ICRA.2015.7139350.
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    • Niekum, S., S. Osentoski, G. Konidaris, S. Chitta, B. Marthi, and A. G. Barto. “Learning grounded finite-state representations from unstructured demonstrations.” International Journal of Robotics Research 34, no. 2 (March 3, 2015): 131–57. https://doi.org/10.1177/0278364914554471.
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    • Nelson, Eliza L., George D. Konidaris, and Neil E. Berthier. “Hand preference status and reach kinematics in infants.” Infant Behavior & Development 37, no. 4 (November 2014): 615–23. https://doi.org/10.1016/j.infbeh.2014.08.013.
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    • Da Silva, B. C., G. Baldassarre, G. Konidaris, and A. Barto. “Learning parameterized motor skills on a humanoid robot.” Proceedings  Ieee International Conference on Robotics and Automation, September 22, 2014, 5239–44. https://doi.org/10.1109/ICRA.2014.6907629.
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    • Barto, A. G., G. Konidaris, and C. Vigorito. “Behavioral hierarchy: Exploration and representation” 9783642398759 (January 1, 2014): 13–46. https://doi.org/10.1007/978-3-642-39875-9_2.
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    • Da Silva, B. C., G. Konidaris, and A. Barto. “Active learning of parameterized skills.” 31st International Conference on Machine Learning, Icml 2014 5 (January 1, 2014): 3736–45.
    • Hollingsworth, N., J. Meyer, R. McGee, J. Doering, G. Konidaris, and L. Kaelbling. “Optimizing a start-stop controller using policy search.” Proceedings of the National Conference on Artificial Intelligence 4 (January 1, 2014): 2984–89.
    • Konidaris, G., L. P. Kaelbling, and T. Lozano-Perez. “Constructing symbolic representations for high-level planning.” Proceedings of the National Conference on Artificial Intelligence 3 (January 1, 2014): 1932-1938B.
    • Konidaris, G., and F. Doshi-Velez. “Hidden parameter Markov decision processes: An emerging paradigm for modeling families of related tasks.” Aaai Fall Symposium  Technical Report FS (January 1, 2014): 46–48.
    • Goretkin, G., A. Perez, R. Platt, and G. Konidaris. “Optimal sampling-based planning for linear-quadratic kinodynamic systems.” Proceedings  Ieee International Conference on Robotics and Automation, November 14, 2013, 2429–36. https://doi.org/10.1109/ICRA.2013.6630907.
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    • Konidaris, G. “Robots, skills, and symbols.” Acm International Conference Proceeding Series, August 30, 2013, 9–11. https://doi.org/10.1145/2493525.2493528.
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    • Agrawal, V., C. Archibald, M. Bhatt, H. Bui, D. J. Cook, J. Cortés, C. Geib, et al. “The AAAI-13 conference workshops.” Ai Magazine 34, no. 4 (January 1, 2013): 108–15. https://doi.org/10.1609/aimag.v34i4.2511.
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    • Konidaris, G., L. P. Kaelbling, and T. Lozano-Perez. “Symbol acquisition for task-level planning.” Aaai Workshop  Technical Report WS-13-12 (January 1, 2013): 9–15.
    • Markman, V., G. Stojanov, B. Indurkhya, T. Kido, K. Takadama, G. Konidaris, E. Eaton, et al. “Reports of the 2013 AAAI Spring Symposium Series.” Ai Magazine 34, no. 3 (January 1, 2013): 93–98. https://doi.org/10.1609/aimag.v34i3.2493.
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    • Niekum, S., S. Osentoski, G. Konidaris, and A. G. Barto. “Learning and generalization of complex tasks from unstructured demonstrations.” Ieee International Conference on Intelligent Robots and Systems, December 1, 2012, 5239–46. https://doi.org/10.1109/IROS.2012.6386006.
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    • Da Silva, B. C., G. Konidaris, and A. G. Barto. “Learning parameterized skills.” Proceedings of the 29th International Conference on Machine Learning, Icml 2012 2 (October 10, 2012): 1679–86.
    • Konidaris, G., I. Scheidwasser, and A. G. Barto. “Transfer in reinforcement learning via shared features.” Journal of Machine Learning Research 13 (May 1, 2012): 1333–71.
    • Nelson, Eliza L., George D. Konidaris, Neil E. Berthier, Maurine C. Braun, Matthew F. S. X. Novak, Stephen J. Suomi, and Melinda A. Novak. “Kinematics of reaching and implications for handedness in rhesus monkey infants.” Developmental Psychobiology 54, no. 4 (May 2012): 460–67. https://doi.org/10.1002/dev.20604.
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    • Konidaris, G., S. Kuindersma, R. Grupen, and A. Barto. “Robot learning from demonstration by constructing skill trees.” International Journal of Robotics Research 31, no. 3 (March 1, 2012): 360–75. https://doi.org/10.1177/0278364911428653.
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    • Alani, H., B. An, M. Jain, T. Kido, G. Konidaris, W. Lawless, D. Martin, et al. “Reports of the AAAI 2012 spring symposia.” Ai Magazine 33, no. 3 (January 1, 2012): 109–14. https://doi.org/10.1609/aimag.v33i3.2428.
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    • Perez, A., R. Platt, G. Konidaris, L. Kaelbling, and T. Lozano-Perez. “LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics.” Proceedings  Ieee International Conference on Robotics and Automation, January 1, 2012, 2537–42. https://doi.org/10.1109/ICRA.2012.6225177.
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    • Konidaris, G., S. Kuindersma, R. Grupen, and A. Barto. “Autonomous skill acquisition on a mobile manipulator.” Proceedings of the National Conference on Artificial Intelligence 2 (November 2, 2011): 1468–73.
    • Konidaris, G., S. Osentoski, and P. Thomas. “Value function approximation in reinforcement learning using the Fourier basis.” Proceedings of the National Conference on Artificial Intelligence 1 (November 2, 2011): 380–85.
    • Konidaris, G., S. Niekum, and P. S. Thomas. “TD _γ : Re-evaluating complex backups in temporal difference learning.” Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, Nips 2011, 2011.
    • Konidaris, G., S. Kuindersmay, A. Barto, and R. Grupen. “Constructing skill trees for reinforcement learning agents from demonstration trajectories.” Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, Nips 2010, December 1, 2010.
    • Konidaris, G., and A. Barto. “Skill discovery in continuous reinforcement learning domains using skill chaining.” Advances in Neural Information Processing Systems 22  Proceedings of the 2009 Conference, December 1, 2009, 1015–23.
    • Konidaris, G., and A. Barto. “Efficient skill learning using abstraction selection.” Ijcai International Joint Conference on Artificial Intelligence, January 1, 2009, 1107–12.
    • Konidaris, G. “Autonomous robot skill acquisition.” Proceedings of the National Conference on Artificial Intelligence 3 (December 23, 2008): 1855–56.
    • Konidaris, G., and A. Barto. “Sensorimotor abstraction selection for efficient, autonomous robot skill acquisition.” 2008 Ieee 7th International Conference on Development and Learning, Icdl, December 1, 2008, 151–56. https://doi.org/10.1109/DEVLRN.2008.4640821.
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    • Konidaris, G., and A. Barto. “Building portable options: Skill transfer in reinforcement learning.” Ijcai International Joint Conference on Artificial Intelligence, December 1, 2007, 895–900.
    • Rauchas, S., B. Rosman, G. Konidaris, and I. Sanders. “Language performance at high school and success in first year computer science.” Proceedings of the Thirty Seventh Sigcse Technical Symposium on Computer Science Education, December 1, 2007, 398–402. https://doi.org/10.1145/1121341.1121467.
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    • Konidaris, G., and A. Barto. “Autonomous shaping: Knowledge transfer in reinforcement learning.” Acm International Conference Proceeding Series 148 (December 1, 2006): 489–96. https://doi.org/10.1145/1143844.1143906.
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    • Konidaris, G., and A. Barto. “Autonomous shaping: Knowledge transfer in reinforcement learning.” Icml 2006  Proceedings of the 23rd International Conference on Machine Learning 2006 (October 6, 2006): 489–96.
    • Konidaris, G., and A. Barto. “An adaptive robot motivational system.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 4095 LNAI (January 1, 2006): 346–56. https://doi.org/10.1007/11840541_29.
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    • Konidaris, G. D., and G. M. Hayes. “An architecture for behavior-based reinforcement learning.” Adaptive Behavior 13, no. 1 (October 17, 2005): 5–32. https://doi.org/10.1177/105971230501300101.
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    • Stewart, F., T. Taylor, and G. Konidaris. “METAMorph: Experimenting with genetic regulatory networks for artificial development.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 3630 LNAI (January 1, 2005): 108–17. https://doi.org/10.1007/11553090_12.
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  • Conference Papers

    • Pacheck, A., G. Konidaris, and H. Kress-Gazit. “Automatic Encoding and Repair of Reactive High-Level Tasks with Learned Abstract Representations.” In Springer Proceedings in Advanced Robotics, 20 SPAR:509–25, 2022. https://doi.org/10.1007/978-3-030-95459-8_31.
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    • Allen, C., M. Katz, T. Klinger, G. Konidaris, M. Riemer, and G. Tesauro. “Efficient Black-Box Planning Using Macro-Actions with Focused Effects.” In Ijcai International Joint Conference on Artificial Intelligence, 4024–31, 2021.
    • Allen, C., N. Parikh, O. Gottesman, and G. Konidaris. “Learning Markov State Abstractions for Deep Reinforcement Learning.” In Advances in Neural Information Processing Systems, 10:8229–41, 2021.
    • Bagaria, A., J. Senthil, M. Slivinski, and G. Konidaris. “Robustly Learning Composable Options in Deep Reinforcement Learning.” In Ijcai International Joint Conference on Artificial Intelligence, 2161–69, 2021.
    • Corsaro, M., S. Tellex, and G. Konidaris. “Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter.” In Ieee International Conference on Intelligent Robots and Systems, 4647–53, 2021. https://doi.org/10.1109/IROS51168.2021.9636876.
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    • Roy, J., and G. Konidaris. “Visual Transfer for Reinforcement Learning via Wasserstein Domain Confusion.” In 35th Aaai Conference on Artificial Intelligence, Aaai 2021, 11A:9454–62, 2021.
    • Tang, B., M. Corsaro, G. Konidaris, S. Nikolaidis, and S. Tellex. “Learning Collaborative Pushing and Grasping Policies in Dense Clutter.” In Proceedings  Ieee International Conference on Robotics and Automation, 2021-May:12662–68, 2021. https://doi.org/10.1109/ICRA48506.2021.9561828.
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    • Zheng, K., Y. Sung, G. Konidaris, and S. Tellex. “Multi-Resolution POMDP Planning for Multi-Object Search in 3D.” In Ieee International Conference on Intelligent Robots and Systems, 2022–29, 2021. https://doi.org/10.1109/IROS51168.2021.9636737.
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    • Murray, S., G. D. Konidaris, and D. J. Sorin. “Roadmap subsampling for changing environments.” In Ieee International Conference on Intelligent Robots and Systems, 5664–70, 2020. https://doi.org/10.1109/IROS45743.2020.9341431.
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    • Rosen, E., N. Kumar, N. Gopalan, D. Ullman, G. Konidaris, and S. Tellex. “Building plannable representations with mixed reality.” In Ieee International Conference on Intelligent Robots and Systems, 11146–53, 2020. https://doi.org/10.1109/IROS45743.2020.9341627.
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    • Kumar, N., M. Fishman, N. Danas, S. Tellex, M. Littman, and G. Konidaris. “Task Scoping for Efficient Planning in OpenWorlds.” In Aaai 2020  34th Aaai Conference on Artificial Intelligence, 13845–46, 2020.
    • Rosen, E., D. Whitney, E. Phillips, G. Chien, J. Tompkin, G. Konidaris, and S. Tellex. “Communicating Robot Arm Motion Intent Through Mixed Reality Head-Mounted Displays,” 10:301–16, 2020. https://doi.org/10.1007/978-3-030-28619-4_26.
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    • Whitney, D., E. Rosen, E. Phillips, G. Konidaris, and S. Tellex. “Comparing Robot Grasping Teleoperation Across Desktop and Virtual Reality with ROS Reality,” 10:335–50, 2020. https://doi.org/10.1007/978-3-030-28619-4_28.
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    • Gopalan, N., E. Rosen, G. Konidaris, and S. Tellex. “Simultaneously Learning Transferable Symbols and Language Groundings from Perceptual Data for Instruction Following.” In Robotics: Science and Systems, 2020. https://doi.org/10.15607/RSS.2020.XVI.102.
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    • Ames, B., and G. Konidaris. “Bounded-Error LQR-Trees.” In Ieee International Conference on Intelligent Robots and Systems, 144–50, 2019. https://doi.org/10.1109/IROS40897.2019.8967750.
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    • Murray, S., W. Floyd-Jones, G. Konidaris, and D. J. Sorin. “A programmable architecture for robot motion planning acceleration.” In Proceedings of the International Conference on Application Specific Systems, Architectures and Processors, 2019-July:185–88, 2019. https://doi.org/10.1109/ASAP.2019.000-4.
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    • Demarinis, N., S. Tellex, V. P. Kemerlis, G. Konidaris, and R. Fonseca. “Scanning the internet for ROS: A view of security in robotics research.” In Proceedings  Ieee International Conference on Robotics and Automation, 2019-May:8514–21, 2019. https://doi.org/10.1109/ICRA.2019.8794451.
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    • Gadre, S. Y., E. Rosen, G. Chien, E. Phillips, S. Tellex, and G. Konidaris. “End-user robot programming using mixed reality.” In Proceedings  Ieee International Conference on Robotics and Automation, 2019-May:2707–13, 2019. https://doi.org/10.1109/ICRA.2019.8793988.
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    • Kim, S., K. Asadi, M. Littman, and G. Konidaris. “DeepMellow: Removing the need for a target network in deep q-learning.” In Ijcai International Joint Conference on Artificial Intelligence, 2019-August:2733–39, 2019. https://doi.org/10.24963/ijcai.2019/379.
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    • Kim, S., K. Asadi, M. Littman, and G. Konidaris. “Removing the target network from deep Q-networks with the mellowmax operator.” In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, Aamas, 4:2060–62, 2019.
    • Levy, A., R. Platt, G. Konidaris, and K. Saenko. “Learning multi-level hierarchies with hindsight.” In 7th International Conference on Learning Representations, Iclr 2019, 2019.
    • Levy, A., R. Platt, G. Konidaris, and K. Saenko. “Learning multi-level hierarchies with hindsight.” In 7th International Conference on Learning Representations, Iclr 2019, 2019.
    • Ames, B., A. Thackston, and G. Konidaris. “Learning Symbolic Representations for Planning with Parameterized Skills.” In Ieee International Conference on Intelligent Robots and Systems, 526–33, 2018. https://doi.org/10.1109/IROS.2018.8594313.
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    • Burchfiel, B., and G. Konidaris. “Hybrid Bayesian Eigenobjects: Combining Linear Subspace and Deep Network Methods for 3D Robot Vision.” In Ieee International Conference on Intelligent Robots and Systems, 6843–50, 2018. https://doi.org/10.1109/IROS.2018.8593795.
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    • Abel, D., Y. Jinnai, G. Yue, G. Konidaris, and M. L. Littman. “Policy and Value Transfer in Lifelong Reinforcement Learning.” In 35th International Conference on Machine Learning, Icml 2018, 1:30–41, 2018.
    • Andersen, G., and G. Konidaris. “Active exploration for learning symbolic representations.” In Advances in Neural Information Processing Systems, 2017-December:5010–20, 2017.
    • Burchfiel, B., and G. Konidaris. “Bayesian Eigenobjects: A unified framework for 3D robot perception.” In Robotics: Science and Systems, Vol. 13, 2017. https://doi.org/10.15607/rss.2017.xiii.011.
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    • James, S., G. Konidaris, and B. Rosman. “An analysis of Monte Carlo tree search.” In 31st Aaai Conference on Artificial Intelligence, Aaai 2017, 3576–82, 2017.
    • Killian, T. W., G. Konidaris, and F. Doshi-Velez. “Robust and efficient transfer learning with hidden parameter Markov decision processes.” In 31st Aaai Conference on Artificial Intelligence, Aaai 2017, 4949–50, 2017.
    • Killian, T., S. Daulton, G. Konidaris, and F. Doshi-Velez. “Robust and efficient transfer learning with hidden parameter Markov decision processes.” In Advances in Neural Information Processing Systems, 2017-December:6251–62, 2017.
    • Murray, S., W. Floyd-Jones, Y. Qi, G. Konidaris, and D. J. Sorin. “The microarchitecture of a real-Time robot motion planning accelerator.” In Proceedings of the Annual International Symposium on Microarchitecture, Micro, Vol. 2016-December, 2016. https://doi.org/10.1109/MICRO.2016.7783748.
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    • Amato, C., G. Konidaris, A. Anders, G. Cruz, J. P. How, and L. P. Kaelbling. “Policy search for multi-robot coordination under uncertainty.” In International Journal of Robotics Research, 35:1760–78, 2016. https://doi.org/10.1177/0278364916679611.
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    • Doshi-Velez, F., and G. Konidaris. “Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations.” In Ijcai International Joint Conference on Artificial Intelligence, 2016-January:1432–40, 2016.
    • Konidaris, G. “Constructing abstraction hierarchies using a skill-symbol loop.” In Ijcai International Joint Conference on Artificial Intelligence, 2016-January:1648–54, 2016.
    • Masson, W., P. Ranchod, and G. Konidaris. “Reinforcement learning with parameterized actions.” In 30th Aaai Conference on Artificial Intelligence, Aaai 2016, 1934–40, 2016.
    • Murray, S., W. Floyd-Jones, Y. Qi, D. Sorin, G. Konidaris, and D. Robotics. “Robot motion planning on a chip.” In Robotics: Science and Systems, Vol. 12, 2016.
    • Zhou, Y., and G. Konidaris. “Representing and learning complex object interactions.” In Robotics: Science and Systems, Vol. 12, 2016. https://doi.org/10.15607/rss.2016.xii.039.
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    • Ranchod, P., B. Rosman, and G. Konidaris. “Nonparametric Bayesian reward segmentation for skill discovery using inverse reinforcement learning.” In Ieee International Conference on Intelligent Robots and Systems, 2015-December:471–77, 2015. https://doi.org/10.1109/IROS.2015.7353414.
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    • Albrecht, S. V., A. M. S. Barreto, D. Braziunas, D. L. Buckeridge, H. Cuayáhuitl, N. Dethlefs, M. Endres, et al. “Reports of the AAAI 2014 conference workshops.” In Ai Magazine, 36:87–98, 2015. https://doi.org/10.1609/aimag.v36i1.2575.
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    • Amato, C., G. Konidaris, A. Anders, G. Cruz, J. P. How, and L. P. Kaelbling. “Policy search for multi-robot coordination under uncertainty.” In Robotics: Science and Systems, Vol. 11, 2015. https://doi.org/10.15607/RSS.2015.XI.007.
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    • Amato, C., G. Konidaris, S. Omidshafiei, A. A. Agha-Mohammadi, J. P. How, and L. P. Kaelbling. “Probabilistic planning for decentralized multi-robot systems.” In Aaai Fall Symposium  Technical Report, FS-15-06:10–12, 2015.
    • Konidaris, G., L. P. Kaelbling, and T. Lozano-Perez. “Symbol acquisition for probabilistic high-level planning.” In Ijcai International Joint Conference on Artificial Intelligence, 2015-January:3619–27, 2015.
    • Thomas, P. S., S. Niekum, G. Theocharous, and G. Konidaris. “Policy evaluation using the Ω-return.” In Advances in Neural Information Processing Systems, 2015-January:334–42, 2015.
    • Amato, C., G. D. Konidaris, and L. P. Kaelbling. “Planning with macro-actions in decentralized POMDPs.” In 13th International Conference on Autonomous Agents and Multiagent Systems, Aamas 2014, 2:1273–80, 2014.
    • Konidaris, G., S. Toumpis, and S. Gitzenis. “Primal decomposition and online algorithms for flow optimization in wireless DTNs.” In Globecom  Ieee Global Telecommunications Conference, 84–90, 2013. https://doi.org/10.1109/GLOCOM.2013.6831052.
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    • Gitzenis, S., G. Konidaris, and S. Toumpis. “Flow optimization in delay tolerant networks using dual decomposition.” In 2012 10th International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, Wiopt 2012, 444–51, 2012.
    • Konidaris, G., S. Niekum, and P. S. Thomas. “TD γ : Re-evaluating complex backups in temporal difference learning.” In Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011, Nips 2011, 2011.
    • Georgopoulos, L., G. M. Hayes, and G. D. Konidaris. “A forward model of optic flow for detecting external forces.” In Ieee International Conference on Intelligent Robots and Systems, 913–18, 2007. https://doi.org/10.1109/IROS.2007.4399014.
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