16,264 research outputs found
Intrinsic Motivation Systems for Autonomous Mental Development
Exploratory activities seem to be intrinsically rewarding
for children and crucial for their cognitive development.
Can a machine be endowed with such an intrinsic motivation
system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development.The complexity of the robot’s activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations
which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Key words: Active learning, autonomy, behavior, complexity,
curiosity, development, developmental trajectory, epigenetic
robotics, intrinsic motivation, learning, reinforcement learning,
values
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
Intrinsic rewards were introduced to simulate how human intelligence works;
they are usually evaluated by intrinsically-motivated play, i.e., playing games
without extrinsic rewards but evaluated with extrinsic rewards. However, none
of the existing intrinsic reward approaches can achieve human-level performance
under this very challenging setting of intrinsically-motivated play. In this
work, we propose a novel megalomania-driven intrinsic reward (called
mega-reward), which, to our knowledge, is the first approach that achieves
human-level performance in intrinsically-motivated play. Intuitively,
mega-reward comes from the observation that infants' intelligence develops when
they try to gain more control on entities in an environment; therefore,
mega-reward aims to maximize the control capabilities of agents on given
entities in a given environment. To formalize mega-reward, a relational
transition model is proposed to bridge the gaps between direct and latent
control. Experimental studies show that mega-reward (i) can greatly outperform
all state-of-the-art intrinsic reward approaches, (ii) generally achieves the
same level of performance as Ex-PPO and professional human-level scores, and
(iii) has also a superior performance when it is incorporated with extrinsic
rewards
Psychological factors affecting equine performance
For optimal individual performance within any equestrian discipline horses must be in peak physical condition and have the correct psychological state. This review discusses the psychological factors that affect the performance of the horse and, in turn, identifies areas within the competition horse industry where current behavioral research and established behavioral modification techniques could be applied to further enhance the performance of animals. In particular, the role of affective processes underpinning temperament, mood and emotional reaction in determining discipline-specific performance is discussed. A comparison is then made between the training and the competition environment and the review completes with a discussion on how behavioral modification techniques and general husbandry can be used advantageously from a performance perspective
Reading in the digital age: using electronic books as a teaching tool for beginning readers
This study investigated the eBook reading experiences of eight grade 1 students. Eight students were given ten 25-minute sessions with the software programs over 15 weeks. Qualitative data were collected from students, teachers, and parents through questionnaires, interviews, observations and field notes.The results suggest the promise of electronic books in enhancing the reading motivation of beginning readers.Beginning readers\u27 motivation to read and the texts they choose to read impact on their literacy achievement and willingness to engage with reading activities in the primary years of schooling
Novelty detection and learning drives
This document presents Deliverable 5.1 of the IM-CLeVeR (Intrinsically Motivated Cumulative Learning Versatile Robots) EU FP7 project. It represents one of two deliverables from Workpackage 5 (Novelty Detection and Drives for Autonomous Learning)
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