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Intrinsic Motivation Systems for Autonomous Mental Development
The mechanism of Intelligent Adaptive Curiosity is presented, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress, thus permitting autonomous mental development.
What is Intrinsic Motivation? A Typology of Computational Approaches
This paper sets the ground for a systematic operational study of intrinsic motivation by presenting a formal typology of possible computational approaches, partly based on existing computational models, but also presents new ways of conceptualizing intrinsic motivation.
dhSegment: A Generic Deep-Learning Approach for Document Segmentation
- S. Oliveira, Benoit Seguin, F. Kaplan
- Computer Science16th International Conference on Frontiers in…
- 27 April 2018
This paper proposes an open-source implementation of a CNN-based pixel-wise predictor coupled with task dependent post-processing blocks and shows that a single CNN-architecture can be used across tasks with competitive results.
Classroom orchestration: The third circle of usability
This work analyzes classroom orchestration as a question of usability in which the classroom is the user and raises design choices that impart visibility, reification and minimalism on classroom Orchestration.
Who is Afraid of the Humanoid? Investigating Cultural Differences in the Acceptance of Robots
- F. Kaplan
- ArtInt. J. Humanoid Robotics
- 1 September 2004
A preliminary exploration of several aspects of the Japanese culture and a survey of the most important myths and novels involving artificial beings in Western literature try to shed light on particular cultural features that may account for contemporary differences in the authors' behavior towards humanoids.
The challenges of joint attention
This article discusses the concept of joint attention and the different skills underlying its development. Research in developmental psychology clearly states that the development of skills to…
An Interactive Table for Supporting Participation Balance in Face-to-Face Collaborative Learning
- K. Bachour, F. Kaplan, P. Dillenbourg
- Computer Science, PsychologyIEEE Transactions on Learning Technologies
- 1 July 2010
It is shown that Reflect leads to more balanced collaboration, but only under certain conditions, and different effects the table has on over and underparticipators.
AIBO’s first words
This paper shows experiments that demonstrate why there has to be a causal role of language on category acquisition and leads effectively to the bootstrapping of communication and shows that other forms of learning do not generate categories usable in communication or make information assumptions which cannot be satisfied.
Crucial factors in the origins of word-meaning
This work was carried out at the Sony Computer Science Laboratory in Paris and the VUB AI laboratory in Brussels (financed by a GOA grant).
Bootstrapping grounded word semantics
It is shown that synonymy and ambiguity arise as emergent properties in the lexicon, due to the situated grounded character of the agent-environment interaction, but that there are also tendencies to dampen them so as to make the language more coherent and thus more optimal from the viewpoints of communicative success, cognitive complexity, and learnability.