ABSTRACT
Research on interactive systems and robots, i.e. interactive machines that perceive, act and communicate, has applied a multitude of different machine learning frameworks in recent years, many of which are based on a form of reinforcement learning (RL). In this paper, we will provide a brief introduction to the application of machine learning techniques in interactive learning systems. We identify several dimensions along which interactive learning systems can be analyzed. We argue that while many applications of interactive machines seem different at first sight, sufficient commonalities exist in terms of the challenges faced. By identifying these commonalities between (learning) approaches, and by taking interdisciplinary approaches towards the challenges, we anticipate more effective design and development of sophisticated machines that perceive, act and communicate in complex, dynamic and uncertain environments.
- R. Akrour, M. Schoenauer, and M. Sebag. APRIL: Active Preference Learning-Based Reinforcement Learning. In ECML/PKDD (2), pages 116--131, 2012. Google ScholarDigital Library
- B. Argall, S. Chernova, M. M. Veloso, and B. Browning. A Survey of Robot Learning from Demonstration. Robotics and Autonomous Systems, 57(5):469--483, 2009. Google ScholarDigital Library
- B. D. Argall and A. G. Billard. A survey of tactile human--robot interactions. Robotics and Autonomous Systems, 58(10):1159--1176, 2010. Google ScholarDigital Library
- C. G. Atkeson and J. C. Santamaría. A Comparison of Direct and Model-based Reinforcement Learning. In ICRA, pages 3557--3564, 1997.Google ScholarCross Ref
- C. G. Atkeson and S. Schaal. Robot Learning From Demonstration. In ICML, pages 12--20, 1997. Google ScholarDigital Library
- J.-C. Baillie. Developmental Robotics at Aldebaran A-Lab. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- A. Barto and S. Mahadevan. Recent Advances in Hierarchical Reinforcement Learning. Discrete Event Dynamic Systems: Theory and Applications, 13(1-2):41--77, 2003. Google ScholarDigital Library
- Y. Bengio. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, 2(1):1--127, 2009. Google ScholarDigital Library
- C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. Google ScholarDigital Library
- A. Blum and T. M. Mitchell. Combining Labeled and Unlabeled Data with Co-Training. In COLT, pages 92--100, 1998. Google ScholarDigital Library
- D. Bohus, B. Langner, A. Raux, A. W. Black, M. Eskenazi, and A. I. Rudnicky. Online Supervised Learning of Non-Understanding Recovery Policies. In SLT, pages 170--173, 2006.Google ScholarCross Ref
- C. Boutilier. Sequential Optimality and Coordination in Multiagent Systems. In IJCAI, pages 478--485, 1999. Google ScholarDigital Library
- M. H. Bowling and M. M. Veloso. Multiagent Learning Using a Variable Learning Rate. Artif. Intell., 136(2):215--250, 2002. Google ScholarDigital Library
- L. Busoniu, R. Babuska, and B. D. Schutter. A Comprehensive Survey of Multiagent Reinforcement Learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 38(2):156--172, 2008. Google ScholarDigital Library
- M. Cakmak, N. DePalma, R. Arriaga, and A. Thomaz. Exploiting social partners in robot learning. Autonomous Robots, 29(3-4):309--329, 2010. Google ScholarDigital Library
- M. Cakmak and A. L. Thomaz. Designing robot learners that ask good questions. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, HRI '12, pages 17--24, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. H. Jr., and T. M. Mitchell. Toward an Architecture for Never-Ending Language Learning. In AAAI, 2010.Google ScholarDigital Library
- C. Chao, M. Cakmak, and A. Thomaz. Transparent Active Learning for Robots. In Proceedings of the 5th ACM/IEEE international Conference on Human-Robot Interaction, HRI '10, 2010. Google ScholarDigital Library
- X. Chen. Open Knowledge for Human-Robot Interaction. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- H. Cuayáhuitl. Hierarchical Reinforcement Learning for Spoken Dialogue Systems. PhD thesis, School of Informatics, University of Edinburgh, January 2009.Google Scholar
- H. Cuayáhuitl and N. Dethlefs. Optimizing situated dialogue management in unknown environments. In INTERSPEECH, pages 1009--1012, 2011.Google Scholar
- H. Cuayáhuitl and N. Dethlefs. Spatially-Aware Dialogue Control Using Hierarchical Reinforcement Learning. ACM Transactions on Speech and Language Processing, 7(3):5:1--5:26, 2011. Google ScholarDigital Library
- H. Cuayáhuitl and N. Dethlefs. Dialogue Systems Using Online Learning: Beyond Empirical Methods. In NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data, SDCTD '12, pages 7--8, Stroudsburg, PA, USA, 2012. Association for Computational Linguistics. Google ScholarDigital Library
- H. Cuayáhuitl and N. Dethlefs. Hierarchical multiagent reinforcement learning for coordinating verbal and non-verbal actions in robots. In ECAI Workshop on Machine Learning for Interactive Systems (MLIS), pages 27--29, Montpellier, France, 2012.Google Scholar
- H. Cuayáhuitl, I. Kruijff-Korbayová, and N. Dethlefs. Hierarchical Dialogue Policy Learning using Flexible State Transitions and Linear Function Approximation. In COLING (Demos), pages 95--102, 2012.Google Scholar
- T. G. Dietterich. Hierarchical reinforcement learning with the MAXQ value function decomposition. International Journal of Artificial Intelligence Research, 13:227--303, 2000. Google ScholarDigital Library
- A. Epshteyn, A. Vogel, and G. DeJong. Active Reinforcement Learning. In ICML, pages 296--303, 2008. Google ScholarDigital Library
- D. Ernst, P. Geurts, and L. Wehenkel. Tree-Based Batch Mode Reinforcement Learning. JMLR, 6:503--556, 2005. Google ScholarDigital Library
- W. Fan and N. Bouguila. Expectation Propagation Learning of Finite Beta-Liouville Mixtures for Spatio-temporal Object Recognition. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- F. Fernández and M. M. Veloso. Probabilistic Policy Reuse in a Reinforcement Learning Agent. In AAMAS, pages 720--727, 2006. Google ScholarDigital Library
- E. Ferreira and F. Lefèvre. Social Signal and User Adaptation in Reinforcement Learning-based Dialogue Management. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- L. A. Ferreira, A. A. Masiero, P. T. A. Junior, and R. A. C. Bianchi. Automatic Interface Optimization through Random Exploration of Available Elements. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- P. Flach. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press, 2012. Google ScholarDigital Library
- I. K. Fodor. A Survey of Dimension Reduction Techniques. Technical Report UCRL-ID-148494, Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, June 2002.Google Scholar
- L. Frommberger. Qualitative Spatial Abstraction in Reinforcement Learning. Cognitive Technologies. Springer, Berlin Heidelberg, Nov. 2010. Google ScholarDigital Library
- L. Frommberger and D. Wolter. Structural knowledge transfer by spatial abstraction for reinforcement learning agents. Adaptive Behavior, 18(6):507--525, 2010. Google ScholarDigital Library
- J. Fürnkranz and E. Hüllermeier. Preference Learning. In Encycl. of Machine Learning, pages 789--795. 2010.Google Scholar
- J. Fürnkranz, E. Hüllermeier, W. Cheng, and S.-H. Park. Preference-based Reinforcement Learning: A Formal Framework and a Policy Iteration Algorithm. Machine Learning, 89(1-2), 2012.Google Scholar
- M. Gasic, F. Jurcícek, B. Thomson, K. Yu, and S. Young. On-Line Policy Optimisation of Spoken Dialogue Systems via Live Interaction with Human Subjects. In ASRU, pages 312--317, 2011.Google ScholarCross Ref
- Z. Ghahramani. Unsupervised Learning. In Advanced Lectures on Machine Learning, pages 72--112, 2003.Google Scholar
- M. Ghavamzadeh, S. Mahadevan, and R. Makar. Hierarchical Multi-Agent Reinforcement Learning. AAMAS, 13(2):197--229, 2006. Google ScholarDigital Library
- F. Hegel, S. Gieselmann, A. Peters, P. Holthaus, and B. Wrede. Towards a typology of meaningful signals and cues in social robotics. In RO-MAN, 2011 IEEE, pages 72--78, 2011.Google ScholarCross Ref
- T. Hester, M. Quinlan, and P. Stone. RTMBA: A Real-Time Model-Based Reinforcement Learning Architecture for Robot Control. In ICRA, pages 85--90, 2012.Google ScholarCross Ref
- T. Hester and P. Stone. Learning and using models. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 4. Springer, 2012.Google Scholar
- J. Hu and M. P. Wellman. Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm. In ICML, pages 242--250, 1998. Google ScholarDigital Library
- I. Iturrate, J. Omedes, and L. Montesano. Shared Control of a Robot Using EEG-based Feedback Signals. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- T. Joachims. Transductive Inference for Text Classification using Support Vector Machines. In ICML, pages 200--209, 1999. Google ScholarDigital Library
- L. Kaelbling, M. Littman, and A. Moore. Reinforcement Learning: A Survey. JAIR, 4:237--285, 1996. Google Scholar
- W. B. Knox, B. D. Glass, B. C. Love, W. T. Maddox, and P. Stone. How humans teach agents - a new experimental perspective. I. J. Social Robotics, 4(4):409--421, 2012.Google ScholarCross Ref
- J. Kober and J. Peters. Reinforcement learning in robotics: A survey. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 18. Springer, 2012.Google Scholar
- G. Konidaris. Autonomous Robot Skill Acquisition. PhD thesis, Department of Computer Science, University of Massachusetts Amherst, May 2011. Google ScholarDigital Library
- G. Konidaris. Robots, Skills, and Symbols. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- G. Konidaris and A. G. Barto. Efficient Skill Learning using Abstraction Selection. In IJCAI, pages 1107--1112, 2009. Google ScholarDigital Library
- G. D. Konidaris and A. G. Barto. Autonomous shaping: Knowledge transfer in reinforcement learning. In Proceedings of the Twenty Third International Conference on Machine Learning (ICML 2006), pages 489--49, Pittsburgh, PA, June 2006. Google ScholarDigital Library
- S. B. Kotsiantis. Supervised Machine Learning: A Review of Classification Techniques. Informatica (Slovenia), 31(3):249--268, 2007.Google Scholar
- M. Lauer and M. A. Riedmiller. An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems. In ICML, pages 535--542, 2000. Google ScholarDigital Library
- A. Lazaric. Transfer in reinforcement learning: A framework and a survey. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 5. Springer, 2012.Google Scholar
- O. Lemon and O. Pietquin. Machine Learning for Spoken Dialogue Systems. In INTERSPEECH, pages 2685--2688, 2007.Google Scholar
- M. L. Littman. Markov Games as a Framework for Multi-Agent Reinforcement Learning. In ICML, pages 157--163, 1994.Google ScholarDigital Library
- Y. Liu and P. Stone. Value-function-based transfer for reinforcement learning using structure mapping. In Proceedings Of The National Conference On Artificial Intelligence (AAAI), Boston, MA, July 2006. Google ScholarDigital Library
- M. Lopes, F. S. Melo, and L. Montesano. Active Learning for Reward Estimation in Inverse Reinforcement Learning. In ECML/PKDD (2), pages 31--46, 2009. Google ScholarDigital Library
- T. M. Mitchell. Machine learning. McGraw Hill series in Computer Science. McGraw-Hill, 1997. Google ScholarDigital Library
- B. Moldovan, P. Moreno, M. van Otterlo, J. Santos-Victor, and L. De Raedt. Learning relational affordance models for robots in multi-object manipulation tasks. In ICRA, pages 4373--4378, May 2012.Google ScholarCross Ref
- J. Mumm and B. Mutlu. Human-robot proxemics: physical and psychological distancing in human-robot interaction. In Proceedings of the 6th international conference on Human-robot interaction, HRI '11, pages 331--338, New York, NY, USA, 2011. ACM. Google ScholarDigital Library
- B. Mutlu, T. Kanda, J. Forlizzi, J. Hodgins, and H. Ishiguro. Conversational gaze mechanisms for humanlike robots. ACM Trans. Interact. Intell. Syst., 1(2):12:1--12:33, Jan. 2012. Google ScholarDigital Library
- K. Nigam, A. McCallum, S. Thrun, and T. M. Mitchell. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2/3), 2000. Google ScholarDigital Library
- N. J. Nilsson. Human-level artificial intelligence? be serious! AI Magazine, 2005.Google Scholar
- A. Nowe, P. Vrancx, and Y.-M. D. Hauwere. Game theory and multi-agent reinforcement learning. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 14. Springer, 2012.Google Scholar
- F. A. Oliehoek. Decentralized POMDPs. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 15. Springer, 2012.Google Scholar
- S. J. Pan. Transfer Learning with Applications on Text, Sensors and Images. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- S. J. Pan and Q. Yang. A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng., 22(10):1345--1359, 2010. Google ScholarDigital Library
- J. Peters and S. Schaal. Natural Actor-Critic. Neurocomputing, 71(7-9):1180--1190, 2008. Google ScholarDigital Library
- R. Pfeifer and C. Scheier. Understanding Intelligence. The MIT Press, Cambridge, Massachusetts, 1999. Google ScholarDigital Library
- P. Poupart and N. A. Vlassis. Model-based Bayesian Reinforcement Learning in Partially Observable Domains. In ISAIM, 2008.Google Scholar
- L. D. Pyeatt and A. E. Howe. Decision Tree Function Approximation in Reinforcement Learning. Technical report, In Proceedings of the Third International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Models, 1998.Google Scholar
- R. Raina, A. Battle, H. Lee, B. Packer, and A. Y. Ng. Self-taught Learning: Transfer Learning from Unlabeled Data. In ICML, pages 759--766, 2007. Google ScholarDigital Library
- L. Rendell, L. Fogarty, W. Hoppitt, T. Morgan, M. Webster, and K. Laland. Cognitive culture: theoretical and empirical insights into social learning strategies. Trends in Cognitive Science, 15(2):68--76, 2011.Google ScholarCross Ref
- M. Riedmiller. Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method. In ECML, pages 317--328, 2005. Google ScholarDigital Library
- M. Riedmiller. Learning Machines that Perceive, Act, and Communicate. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- L. D. Riek. Wizard of oz studies in hri: A systematic review and new reporting guidelines. Journal of Human-Robot Interaction, 1(1):199--136, 2012.Google Scholar
- B. Rosman and S. Ramamoorthy. A Multitask Representation Using Reusable Local Policy Templates. 2012.Google Scholar
- M. Salem, S. Kopp, I. Wachsmuth, K. Rohlfing, and F. Joublin. Generation and evaluation of communicative robot gesture. International Journal of Social Robotics, 4(2):201--217, 2012.Google ScholarCross Ref
- J. Schatzmann, K. Weilhammer, M. N. Stuttle, and S. Young. A Survey of Statistical User Simulation Techniques for Reinforcement-Learning of Dialogue Management Strategies. Knowledge Eng. Review, 21(2):97--126, 2006. Google ScholarDigital Library
- B. Settles. Active Learning Literature Survey. Technical Report Technical Report 1648, University of Wisconsin, Madison, 2009.Google Scholar
- Ö. Simsek, A. P. Wolfe, and A. G. Barto. Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning. In ICML, pages 816--823, 2005. Google ScholarDigital Library
- M. Snel and S. Whiteson. Multi-task reinforcement learning: shaping and feature selection. In Recent Advances in Reinforcement Learning, pages 237--248. Springer, 2012. Google ScholarDigital Library
- P. Stone and M. M. Veloso. Multiagent Systems: A Survey from a Machine Learning Perspective. Autonomous Robots, 8(3):345--383, 2000. Google ScholarDigital Library
- H. Suay and S. Chernova. Effect of human guidance and state space size on interactive reinforcement learning. In RO-MAN, 2011 IEEE, pages 1--6, 2011.Google ScholarCross Ref
- R. S. Sutton and A. G. Barto. Introduction to Reinforcement Learning. MIT Press, Cambridge, MA, USA, 1st edition, 1998. Google ScholarDigital Library
- R. S. Sutton, D. A. McAllester, S. P. Singh, and Y. Mansour. Policy Gradient Methods for Reinforcement Learning with Function Approximation. In NIPS, pages 1057--1063, 1999.Google ScholarDigital Library
- R. S. Sutton, J. Modayil, M. Delp, T. Degris, P. M. Pilarski, A. White, and D. Precup. Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction. In L. Sonenberg, P. Stone, K. Tumer, and P. Yolum, editors, AAMAS, pages 761--768. IFAAMAS, 2011. Google ScholarDigital Library
- R. S. Sutton, D. Precup, and S. Singh. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning. Artificial Intelligence, 112(1-2):181--211, 1999. Google ScholarDigital Library
- C. Szepesvári. Algorithms for Reinforcement Learning. Morgan and Claypool Publishers, 2010. Google ScholarDigital Library
- I. Szita. Reinforcement learning in games. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 17. Springer, 2012.Google Scholar
- M. Tan. Multi-Agent Reinforcement Learning: Independent versus Cooperative Agents. In ICML, pages 330--337, 1993.Google Scholar
- M. Taylor and P. Stone. Transfer Learning for Reinforcement Learning Domains: A Survey. JMLR, 10:1633--1685, 2009. Google ScholarDigital Library
- M. E. Taylor and P. Stone. Cross-domain transfer for reinforcement learning. In Proceedings of the Twenty Fourth International Conference on Machine Learning (ICML 2007), Corvallis, Oregon, June 2007. Google ScholarDigital Library
- G. Tesauro. Temporal Difference Learning and TD-Gammon. Commun. ACM, 38(3):58--68, 1995. Google ScholarDigital Library
- A. L. Thomaz and C. Breazeal. Teachable robots: Understanding human teaching behavior to build more effective robot learners. Artificial Intelligence, 172:716--737, 2008. Google ScholarDigital Library
- S. Thrun. Learning To Learn: Introduction. Kluwer Academic Publishers, 1996.Google ScholarCross Ref
- S. Thrun and J. O'Sullivan. Discovering Structure in Multiple Learning Tasks: The TC Algorithm. In ICML, pages 489--497, 1996.Google ScholarDigital Library
- R. Toris, H. B. Suay, and S. Chernova. A practical comparison of three robot learning from demonstration algorithms. In Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction, HRI '12, pages 261--262, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- L. Torrey, J. Shavlik, T. Walker, and R. Maclin. Skill acquisition via transfer learning and advice taking. In Proceedings of the Seventeenth European Conference on Machine Learning (ECML'06), pages 425--436, Berlin, Germany, Sept. 2006. Google ScholarDigital Library
- W. Uther and M. Veloso. Adversarial Reinforcement Learning. 1997.Google Scholar
- M. Valko, M. Ghavamzadeh, and A. Lazaric. Semi-Supervised Apprenticeship Learning. JMLR: EWRL10 Workshop and Conference Proceedings, 24:131--141, 2012.Google Scholar
- M. van Otterlo. The Logic of Adaptive Behavior: Knowledge Representation and Algorithms for Adaptive Sequential Decision Making under Uncertainty in First-Order and Relational Domains. IOS Press, Amsterdam, The Netherlands, 2009.Google Scholar
- M. van Otterlo. Solving relational and first-order logical markov decision processes: A survey. In M. Wiering and M. van Otterlo, editors, Reinforcement Learning: State-of-the-Art, chapter 8. Springer, 2012.Google Scholar
- U. von Luxburg. A Tutorial on Spectral Clustering. Statistics and Computing, 17(4):395--416, 2007. Google ScholarDigital Library
- M. Waibel, M. Beetz, J. Civera, R. d'Andrea, J. Elfring, D. Galvez-Lopez, K. Haussermann, R. J. M. Janssen, J. M. M. Montiel, A. Perzylo, B. Schiessle, M. Tenorth, O. Zweigle, and M. J. G. van de Molengraft. Roboearth -- a world wide web for robots. IEEE Robotics & Automation Magazine, 18(2):69--82, 2011.Google ScholarCross Ref
- M. Wiering and M. van Otterlo. Reinforcement Learning: State-of-the-Art. Springer, 2012.Google Scholar
- H. Xiong, S. Szedmak, and J. Piater. Homogeneity Analysis for Object-Action Relation Reasoning in Kitchen Scenario. In Proceedings of the Second Workshop on Machine Learning for Interactive Systems (MLIS). ACM ICPS, 2013. Google ScholarDigital Library
- D. Yarowsky. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods. In ACL, pages 189--196, 1995. Google ScholarDigital Library
- J. Young, J. Sung, A. Voida, E. Sharlin, T. Igarashi, H. Christensen, and R. Grinter. Evaluating human-robot interaction. International Journal of Social Robotics, 3(1):53--67, 2011.Google ScholarCross Ref
- S. Young, M. Gasic, B. Thomson, and J. D. Williams. POMDP-Based Statistical Spoken Dialog Systems: A Review. Proceedings of the IEEE, 101(5):1160--1179, 2013.Google ScholarCross Ref
- S. Zhifei and E. M. Joo. A survey of inverse reinforcement learning techniques. International Journal of Intelligent Computing and Cybernetics, 5(3):293--311, 2012.Google ScholarCross Ref
- X. Zhu. Semi-Supervised Learning Literature Survey. Technical Report Technical Report 1530, University of Wisconsin, Madison, 2006.Google Scholar
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