Article published in:
Interaction Studies
Vol. 17:2 (2016) ► pp. 248278


Broz, F., Nehaniv, C. L., Kose-Bagci, H., & Dautenhahn, K.
(2012) Interaction Histories and Short Term Memory: Enactive Development of Turn-taking Behaviors in a Childlike Humanoid Robot. Computing Research Repository. Artificial Intelligence; Adaptation and Self-Organizing Systems.Google Scholar
Cangelosi, A., Metta, G., Sagerer, G., Nolfi, S., Nehaniv, C., Fischer, K., … Zeschel, A.
(2010) Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics. IEEE Transactions on Autonomous Mental Development, 2(1), 167–195. CrossrefGoogle Scholar
Carey, S.
(2009) The Origin of Concepts. Oxford University Press. CrossrefGoogle Scholar
Carey, S., & Xu, F.
(2001) Infants’ knowledge of objects: beyond object files and object tracking. Cognition, 80(1–2), 179–213. CrossrefGoogle Scholar
Cheng, P. W.
(1996) From Covariation to Causation: A Causal Power Theory. Psychological Review, 104(1), 367–405.Google Scholar
Crangle, C., & Suppes, P.
(1994) Language and Learning for Robots. Center for the Study of Language and Information.Google Scholar
Demiris, Y., & Khadhouri, B.
(2006) Content-based control of goal-directed attention during human action perception. In ROMAN 2006 – The 15th IEEE International Symposium on Robot and Human Interactive Communication (pp. 226–231). IEEE. CrossrefGoogle Scholar
Dindo, H., & Schillaci, G.
(2010) An adaptive probabilistic approach to goal-level imitation learning. IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 – Conference Proceedings, 4452–4457. CrossrefGoogle Scholar
[ p. 274 ]
Dominey, P. F.
(2003) Learning Grammatical Constructions in a Miniature Language from Narrated Video Events. In 25th Annual Meeting of the Cognition Science Society, Boston.Google Scholar
Dominey, P. F., & Dodane, C.
(2004) Indeterminacy in language acquisition: the role of child directed speech and joint attention. Journal of Neurolinguistics, 17(2–3), 121–145. CrossrefGoogle Scholar
Dominey, P. F., Mallet, A., & Yoshida, E.
(2007a) Progress in Programming the HRP-2 Humanoid Using Spoken Language. In Proceedings 2007 IEEE International Conference on Robotics and Automation (pp. 2169–2174). IEEE.Google Scholar
(2007b) Real-time cooperative behavior acquisition by a humanoid apprentice. In 2007 7th IEEE-RAS International Conference on Humanoid Robots (pp. 270–275). IEEE.Google Scholar
(2009) Real-time spoken-language programming for cooperative interaction with a humanoid apprentice. International Journal of Humanoid Robotics, 6(1), 147–171. CrossrefGoogle Scholar
Dore, A., Cattoni, A. F., & Regazzoni, C. S.
(2010) Interaction Modeling and Prediction in Smart Spaces: A Bio-Inspired Approach Based on Autobiographical Memory. IEEE Transactions on Systems, Man, and Cybernetics – Part A: Systems and Humans, 40(1), 1191–1205. CrossrefGoogle Scholar
Doshi, F., & Roy, N.
(2008) Spoken language interaction with model uncertainty: an adaptive human – robot interaction system. Connection Science, 20(1), 299–318. CrossrefGoogle Scholar
Fern, A., Givan, R., & Siskind, J. M.
(2002) Specific-to-General Learning for Temporal Events with Application to Learning Event Definitions from Video. J. Artificial Intelligence Res., 103–129.Google Scholar
G. L. Drescher.
(1991) Made-up minds: a constructivist approach to artificial intelligence. MIT press.Google Scholar
Gergely, G., & Csibra, G.
(2003) Teleological reasoning in infancy: the naı̈ve theory of rational action. Trends in Cognitive Sciences, 7(1), 287–292. CrossrefGoogle Scholar
(2005) The social construction of the cultural mind: Imitative learning as a mechanism of human pedagogy. Interaction Studies, 6(1), 463–481.Google Scholar
(2006) Sylvia’s recipe: The role of imitation and pedagogy in the transmission of cultural knowledge. Roots of Human Sociality: Culture, Cognition, and Human Interaction, 229–255.Google Scholar
Gergely, G., Nádasdy, Z., Csibra, G., & Bíró, S.
(1995) Taking the intentional stance at 12 months of age. Cognition, 56(1), 165–193. CrossrefGoogle Scholar
Gori, I., Pattacini, U., Nori, F., Metta, G., & Sandini, G.
(2012) DForC: A real-time method for reaching, tracking and obstacle avoidance in humanoid robots. In 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012) (pp. 544–551). IEEE.Google Scholar
Gorniak, P., & Roy, D.
(2004) Grounded Semantic Composition for Visual Scenes. Journal of Artificial Intelligence Research, 21, 429–470.Google Scholar
Guez, A., Silver, D., & Dayan, P.
(2013) Scalable and efficient bayes-adaptive reinforcement learning based on Monte-Carlo tree search. Journal of Artificial Intelligence Research, 48, 841–883.
Hayes-Roth, F.
(1997) June 15. Artificial Intelligence: What Works and What Doesn’t? AI Magazine.Google Scholar
[ p. 275 ]
Helmert, M.
(2009) Concise finite-domain representations for PDDL planning tasks. Artificial Intelligence, 173(5–6), 503–535. CrossrefGoogle Scholar
Ho, W. C., Dautenhahn, K., Lim, M. Y., Vargas, P. A., Aylett, R., & Enz, S.
(2009) An initial memory model for virtual and robot companions supporting migration and long-term interaction. In RO-MAN 2009 – The 18th IEEE International Symposium on Robot and Human Interactive Communication (pp. 277–284). IEEE. CrossrefGoogle Scholar
Kalkan, S., Dag, N., Yürüten, O., Borghi, A. M., & Sahin, E.
(2014) Verb concepts from affordances. Interaction Studies Journal, 15(1), 1–37. CrossrefGoogle Scholar
Kaplan, F., & Hafner, V. V.
(2006) The challenges of joint attention. Interaction Studies, 7(1), 135–169.Google Scholar
Kulick, J., Toussaint, M., Lang, T., Lopes, M., Sud-ouest, I. B., & Bat, A.
(2013) Active Learning for Teaching a Robot Grounded Relational Symbols at Stuttgart. In Proceedings of the Twenty-Third international joint conference on Artificial Intelligence.Google Scholar
Lallee, S., Lemaignan, S., Lenz, A., Melhuish, C., Natale, L., Skachek, S., … Dominey, P. F.
(2010) Towards a platform-independent cooperative human-robot interaction system: I. Perception. In 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 4444–4451). IEEE.Google Scholar
Lallee, S., Madden, C., Hoen, M., & Dominey, P. F.
(2010) Linking language with embodied and teleological representations of action for humanoid cognition. Frontiers in Neurorobotics, 4, 8.Google Scholar
Lallee, S., Pattacini, U., Boucher, J. D., Lemaignan, S., Lenz, A., Melhuish, C., … Dominey, P. F.
(2011) Towards a platform-independent cooperative human-robot interaction system: II. Perception, execution and imitation of goal directed actions. In 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (pp. 2895–2902). IEEE.Google Scholar
Lallee, S., Pattacini, U., Lemaignan, S., Lenz, A., Melhuish, C., Natale, L., … Dominey, P. F.
(2012) Towards a Platform-Independent Cooperative Human Robot Interaction System: III An Architecture for Learning and Executing Actions and Shared Plans. IEEE Transactions on Autonomous Mental Development, 4(1), 239–253. CrossrefGoogle Scholar
Lallee, S., Warneken, F., & Dominey, P.
(2009) Learning to collaborate by observation. In Epirob, Venice, Italy.Google Scholar
Lauria, S., Bugmann, G., Kyriacou, T., & Klein, E.
(2002) Mobile robot programming using natural language. Robotics and Autonomous Systems, 38(3–4), 171–181. CrossrefGoogle Scholar
Lévi-Strauss, C.
(1979) Myth and Meaning. Routledge.Google Scholar
Martinez, D., Alenya, G., & Torras, C.
(2015) Relational Reinforcement Learning with Guided Demonstrations. Artificial Intelligence, (Corrected Proof, 19 February 2015). CrossrefGoogle Scholar
McDermott, D., Malik, G., Adele, H., Craig, K., Ashwin, R., Manuela, V., … David, W.
(1998) PDDL – The Planning Domain Definition Language. In tech. report CVC TR–98-003/DCS TR–1165, Yale Center for Computational Vision and Control, Yale Univ., New Haven, Conn..Google Scholar
McGuire, P., Fritsch, J., Steil, J. J., Rothling, F., Fink, G. A., Wachsmuth, S., … Ritter, H.
(2002) Multi-modal human-machine communication for instructing robot grasping tasks. In IEEE/RSJ International Conference on Intelligent Robots and System (Vol. 2, pp. 1082–1088). IEEE.Google Scholar
Metta, G., Sandini, G., Vernon, D., Natale, L., & Nori, F.
(2008) The iCub humanoid robot. In Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems – PerMIS ’08 (p. 50). New York, New York, USA: ACM Press. CrossrefGoogle Scholar
[ p. 276 ]
Mirza, N. A., Nehaniv, C. L., Dautenhahn, K., & Boekhorst, R.
(2008) Developing social action capabilities in a humanoid robot using an interaction history architecture. In Humanoids 2008 – 8th IEEE-RAS International Conference on Humanoid Robots (pp. 609–616). IEEE.Google Scholar
Ognibene, D., Chinellato, E., Sarabia, M., & Demiris, Y.
(2013) Contextual action recognition and target localization with an active allocation of attention on a humanoid robot. Bioinspiration & Biomimetics, 8(1), 035002.Google Scholar
Pasula, H. M., Zettlemoyer, L. S., & Kaelbling, L. P.
(2007) Learning symbolic models of stochastic domains. Journal of Artificial Intelligence Research, 29, 309–352.Google Scholar
Payne, J. D., & Nadel, L.
(2004) Sleep, dreams, and memory consolidation: the role of the stress hormone cortisol. Learning & Memory (Cold Spring Harbor, N.Y.), 11(1), 671–8.Google Scholar
Petit, M., Lallee, S., Boucher, J.-D., Pointeau, G., Cheminade, P., Ognibene, D., … Dominey, P. F.
(2013) The Coordinating Role of Language in Real-Time Multimodal Learning of Cooperative Tasks. IEEE Transactions on Autonomous Mental Development, 5(1), 3–17. CrossrefGoogle Scholar
Pointeau, G., Petit, M., & Dominey, P. F.
(2013) Embodied Simulation Based on Autobiographical Memory. In Biomimetic and Biohybrid Systems (Vol. 8064, pp. 240–250). Berlin, Heidelberg: Springer Berlin Heidelberg. CrossrefGoogle Scholar
(2013) Robot Learning Rules of Games by Extraction of Intrinsic Properties. In ACHI 2013, The Sixth International Conference on Advances in Computer-Human Interactions (pp. 109–116).Google Scholar
(2014) Successive Developmental Levels of Autobiographical Memory for Learning Through Social Interaction. IEEE Transactions on Autonomous Mental Development, 6(1), 200–212. CrossrefGoogle Scholar
Rader, N. de V., & Zukow-Goldring, P.
(2012) Caregivers’ gestures direct infant attention during early word learning: the importance of dynamic synchrony. Language Sciences, 34(1), 559–568. CrossrefGoogle Scholar
Ross, S., & Pineau, J.
(2008) Online Planning Algorithms for POMDPs. The Journal of Artificial Intelligence Research, 32(1), 663–704.Google Scholar
Roy, D. K.
(2002a) Learning visually grounded words and syntax for a scene description task. Computer Speech & Language, 16(3–4), 353–385. CrossrefGoogle Scholar
(2002b) Learning words from sights and sounds: a computational model. Cognitive Science, 26(1), 113–146. CrossrefGoogle Scholar
Siskind, J. M.
(1996) A computational study of cross-situational techniques for learning word-to-meaning mappings. Cognition, 61(1–2), 39–91. CrossrefGoogle Scholar
Sutton, S., Ronald A., C., & Jacques De Villiers, Johan Schalkwyk, Pieter JE Vermeulen, Michael W. Macon, Y. Y. et al.
(1998) Universal speech tools: the CSLU toolkit. In ICSLP, vol. 98 (pp. 3221–3224).Google Scholar
Takács, B., & Demiris, Y.
(2008) Balancing Spectral Clustering for Segmenting Spatio-temporal Observations of Multi-agent Systems. In 2008 Eighth IEEE International Conference on Data Mining (pp. 580–587). IEEE.Google Scholar
Wang, X.
(1994) Learning Planning Operators by Observation and Practice. In Proceedings of the Second International Conference on AI Planning Systems, AIPS-94 (pp. 335–340). Chicago, IL.Google Scholar
Welke, K., Kaiser, P., Kozlov, A., Adermann, N., Asfour, T., Lewis, M., & Steedman, M.
(2013) Grounded Spatial Symbols for Task Planning Based on Experience. In 13th International Conference on Humanoid Robots (Humanoids). IEEE/RAS.Google Scholar
[ p. 277 ]
Wood, R., Baxter, P., & and Belpaeme, T.
(2011) A review of long-term memory in natural and synthetic systems. Adaptive Behavior, 20(1), 81–103.Google Scholar
Zacks, J. M., Speer, N. K., Swallow, K. M., Braver, T. S., & Reynolds, J. R.
(2007) Event perception: A mind-brain perspective. Psychological bulletin2, 133, 273.[ p. 278 ] CrossrefGoogle Scholar
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