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List of all publications whose research was partially or wholly funded by the IM-CLeVeR project, sorted by reference type.

This folder holds the following references to publications, filtered by year.

There are 39 references in this bibliography folder for Incollection type.

Santucci, V, Baldassarre, G, and Mirolli, M (2014).
Cumulative learning through intrinsic reinforcements
In: Artificial Life, Evolution and Complexity, ed. by Cagnoni S., Mirolli M., Villani M., pp. 107-122, Springer , Berlin.

Baldassarre, G and Mirolli, M (2013).
Computational and robotic models of the hierarchical organization of behavior: an overview
In: Computational and Robotic Models of the Hierarchical Organisation of Behaviour, ed. by Baldassarre Gianluca and Mirolli Marco, Springer Verlag, Berlin.

Baldassarre, G and Mirolli, M (2013).
Decidiing which skill to learn when: Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM)
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 257-278, Springer Verlag, Berlin.

Baldassarre, G and Mirolli, M (2013).
Intrinsically Motivated Learning Systems: An Overview
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 1-14, Springer Verlag, Berlin.

Nehmzow, U, Gatsoulis, Y, Kerr, E, Condell, J, Siddique, N, and McGinnity, M (2013).
Novelty Detection as an Intrinsic Motivation for Cumulative Learning Robots
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, Springer-Verlag, Berlin.

Lonini, L, Dimitrakakis, C, Rothkopf, C, and Triesch, J (2013).
Generalization and Interference in Human Motor Control
In: Computational and Robotic Models of the Hierarchical Organization of Behavior, pp. 155-176, Springer-Verlag, Berlin.

Lee, M, Law, J, and Huelse, M (2013).
A developmental framework for cumulative learning robots
In: Computational and robotic model of the hierarchical organization of behavior, ed. by Baldassarre, G., Mirolli, M., pp. 177-212, Springer, Berlin.

Baldassarre, G and Mirolli, M (2013).
Deciding which skill to learn when: Temporal-Difference Competence-Based Intrinsic Motivation (TD-CB-IM)
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre, G., Mirolli, M., pp. 257-278, Springer, Berlin.

Schmidhuber, J (2013).
Maximizing Fun By Creating Data With Easily Reducible Subjective Complexity
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre, G., Mirolli, M., pp. 95-128, Springer, Berlin.

Rothkopf, C and Ballard, D (2013).
Learning to coordinate repertoirs of behaviors: credit assignment and module activation
In: Computational and Robotic Models of the Hierarchical Organization of Behavior, ed. by Baldassarre, G., Mirolli, M., Springer.

Baldassarre, G, Caligiore, D, and Mannella, F (2013).
The hierarchical organisation of cortical and basal-ganglia systems: a computationally-informed review and integrated hypothesis
In: Computational and Robotic Models of the Hierarchical Organisation of Behaviour, ed. by Baldassarre Gianluca and Mirolli, Marco, Springer Verlag, Berlin.

Mirolli, M and Baldassarre, G (2013).
Functions and mechanisms of intrinsic motivations: the knowledge versus competence distinction
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 49-72, Springer Verlag, Berlin.

Gurney, K, Lepora, N, Shah, A, Koene, A, and Redgrave, P (2013).
Action discovery and intrinsically motivation: a biologically constrained formalisation
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli, Marco, pp. 151-181, Springer-Verlag, Berlin.

Redgrave, P, Gurney, K, Stafford, T, Thirkettle, M, and Lewis, J (2013).
The role of the basal ganglia in discovering novel actions
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 129-150, Springer-Verlag, Berlin.

Stafford, T, Walton, T, Hetherington, L, Thirkettle, M, Gurney, K, and Redgrave, P (2013).
A novel behavioural task for researching intrinsic motivations
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre Gianluca and Mirolli Marco, pp. 395-410, Springer-Verlag, Berlin.

Taffoni, F, Formica, D, Schiavone, G, Scorcia, M, Tomassetti, A, Polizzi di Sorrentino, E, Sabbatini, G, Truppa, V, Mannella, F, Fiore, V, Mirolli, M, Baldassarre, G, Visalberghi, E, Keller, F, and Guglielmelli, E (2012).
The ``Mechatronic Board'': A tool to study intrinsic motivations in humans, monkeys, and humanoid robots
In: Intrinsically Motivated Learning in Natural and Artificial Systems, ed. by Baldassarre, G. & Mirolli, M, pp. 411-432, Springer, Berlin.

Gurney, K and Humphries, M (2012).
Methodological Issues in Modelling at Multiple Levels of Description
In: Computational Systems Neurobiology, ed. by N. Le Novere, pp. 259--281, Springer.

Shah, A (2012).
Psychological and Neuroscientific Connections With Reinforcement Learning
In: Reinforcement Learning: State of the Art, ed. by Marco Wiering and Martijn van Otterlo, chap. 16, pp. 507-537, Springer Verlag, Berlin Heidelberg, Book Chapter.

Vautrelle, N, Leriche, M, and Redgrave, P (2012).
Visual activation of short latency reinforcement mechanisms in the basal ganglia.
In: The Routledge Handbook of Motor Control and Motor Learning, ed. by Albert Gollhofer, Wolfgang Taube, and Jens Bo Nielsen, chap. 6, pp. 113--134, Routledge, Abington, Oxon, United Kingdom.

Schmidhuber, J (2012).
A Formal Theory of Creativity to Model the Creation of Art.
In: Computers and Creativity, ed. by McCormack, J. and d'Inverno, M., MIT Press.

Baldassarre, G (2011).
What are intrinsic motivations? A biological perspective
In: Proceedings of the International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob-2011), ed. by Cangelosi A., Triesch J., Fasel I., Rohlfing K., Nori F., Oudeyer P.Y., Schlesinger M., Nagai Y., pp. E1-8, IEEE, Piscataway, NJ.

Chambers, J, Gurney, K, Humphries, M, and Prescott, T (2011).
Mechanisms of choice in the primate brain: a quick look at positive feedback
In: Modelling Natural Action Selection (2011), ed. by Anil K. Seth, Tony J. Prescott, and Joanna J. Bryson , chap. 17, pp. 390--418, Cambridge University Press.

Mannella, F, Mirolli, M, and Baldassarre, G (2010).
The interplay of pavlovian and instrumental processes in devaluation experiments: a computational embodied neuroscience model tested with a simulated rat
In: Modelling Perception With Artificial Neural Networks, ed. by Tosh Colin, Ruxton Graeme, pp. 93-113, Cambridge University Press, Cambridge.

Caligiore, D, Mirolli, M, Parisi, D, and Baldassarre, G (2010).
A bioinspired hierarchical reinforcement learning architecture for modeling learning of multiple skills with continuous states and actions
In: Proceedings of the Tenth International Conference on Epigenetic Robotics (EpiRob2010), ed. by Johansson Birger, Sahin Erol, Balkenius Christian, vol. 149, pp. 27-34, Lund University, Lund, Sweden. Lund University Cognitive Studies.

Ognibene, D, Pezzulo G, and Baldassarre, G (2010).
Learning to look in different enviroments: an active-vision model which learns and readapts visual routines
In: From Animals to Animats 11 – Proceedings of the 11th International Conference on Simulation of Adaptive Behavior, SAB 2010., ed. by Doncieux S., Benoit G., Guillot A., Hallam J., Meyer J. A., Mouret J. B., vol. 6226, pp. 199-210, Springer, Berlin. Lectures Notes in Computer Science.