1,115 research outputs found
Representation Internal-Manipulation (RIM): A Neuro-Inspired Computational Theory of Consciousness
Many theories, based on neuroscientific and psychological empirical evidence
and on computational concepts, have been elaborated to explain the emergence of
consciousness in the central nervous system. These theories propose key
fundamental mechanisms to explain consciousness, but they only partially
connect such mechanisms to the possible functional and adaptive role of
consciousness. Recently, some cognitive and neuroscientific models try to solve
this gap by linking consciousness to various aspects of goal-directed
behaviour, the pivotal cognitive process that allows mammals to flexibly act in
challenging environments. Here we propose the Representation
Internal-Manipulation (RIM) theory of consciousness, a theory that links the
main elements of consciousness theories to components and functions of
goal-directed behaviour, ascribing a central role for consciousness to the
goal-directed manipulation of internal representations. This manipulation
relies on four specific computational operations to perform the flexible
internal adaptation of all key elements of goal-directed computation, from the
representations of objects to those of goals, actions, and plans. Finally, we
propose the concept of `manipulation agency' relating the sense of agency to
the internal manipulation of representations. This allows us to propose that
the subjective experience of consciousness is associated to the human capacity
to generate and control a simulated internal reality that is vividly perceived
and felt through the same perceptual and emotional mechanisms used to tackle
the external world.Comment: 16 pages, 5 figures, preprin
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
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)
- …