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

Comment les robots construisent leur monde : Expériences sur la convergence des catégories sensorielles

Trabajo presentado a la Demi-journée Scientifique de l'Association pour la Recherche Cognitive: Evolution et Cognition, celebrada en Paris el 8 de diciembre del 2000.N

Situated grounded word semantics

Trabajo presentado a la 16th International Joint Conference on Artificial Intelligence (ijcai) celebrada en Estocolmo (Suecia) del 31 de julio al 6 de agosto de 1999.The paper reports on experiments in which autonomous visually grounded agents bootstrap an ontology and a shared lexicon without prior design nor other forms of human intervention. The agents do so while playing a particular language game called the guessing game. We show that synonymy and polysemy arise as emergent properties in the language but also that there are tendencies to dampen it so as to make the language more coherent and thus more optimal from the viewpoints of communicative success, cognitive complexity, and learnability.This research was conducted at the Sony Computer Science Laboratory.Peer reviewe

Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance

An architecture for evolving robust shared communication systems in noisy environments

Trabajo presentado al Sony Research Forum celebrado en Tokyo en 1998.Our research aims to provide a means for independent entities to autonomously develop a set of shared conventions which will allow them to communicate with each other. The communication system thus developed needs to be efficient, robust, learnable and tolerant of noise occurring at all stages in the communication process. This paper proposes a system based on repeated interactions, coupled with learning procedures that allows shared communication systems to be developed even in the presence of noise.This research was carried out at the Sony Computer Science Laboratory in Paris.N

Layout analysis on newspaper archives

The study of newspaper layout evolution through historical corpora has been addressed by diverse qualitative and quantitative methods in the past few years. The recent availability of large corpora of newspapers is now making the quantitative analysis of layout evolution ever more popular. This research investigates a method for the automatic detection of layout evolution on scanned images with a factorial analysis approach. The notion of eigenpages is defined by analogy with eigenfaces used in face recognition processes. The corpus of scanned newspapers that was used contains 4 million press articles, covering about 200 years of archives. This method can automatically detect layout changes of a given newspaper over time, rebuilding a part of its past publishing strategy and retracing major changes in its history in terms of layout. Besides these advantages, it also makes it possible to compare several newspapers at the same time and therefore to compare the layout changes of multiple newspapers based only on scans of their issues

Simple models of distributed co-ordination

Distributed co-ordination is the result of dynamical processes enabling independent agents to coordinate their actions without the need of a central co-ordinator. In the past few years, several computational models have illustrated the role played by such dynamics for self-organizing communication systems. In particular, it has been shown that agents could bootstrap shared convention systems based on simple local adaptation rules. Such models have played a pivotal role for our understanding of emergent language processes. However, only few formal or theoretical results have been published about such systems. Deliberately simple computational models are discussed in this paper in order to make progress in understanding the underlying dynamics responsible for distributed coordination and the scaling laws of such systems. In particular, the paper focuses on explaining the convergence speed of those models, a largely under-investigated issue. Conjectures obtained through empirical and qualitative studies of these simple models are compared with results of more complex simulations and discussed in relation to theoretical models formalized using Markov chains, game theory and Polya processes

Linguistic Capitalism and Algorithmic Mediation

Google’s highly successful business model is based on selling words that appear in search queries. Organizing several million auctions per minute, the company has created the first global linguistic market and demonstrated that linguistic capitalism is a lucrative business domain, one in which billions of dollars can be realized per year. Google’s services need to be interpreted from this perspective. This article argues that linguistic capitalism implies not an economy of attention but an economy of expression. As several million users worldwide daily express themselves through one of Google’s interfaces, the texts they produce are systematically mediated by algorithms. In this new context, natural languages could progressively evolve to seamlessly integrate the linguistic biases of algorithms and the economical constraints of the global linguistic economy

Le cercle vertueux de l'annotation

Annoter est bon pour la compréhension immédiate du lecteur. Lire des textes annotés permet de mieux les comprendre. Cette double pertinence de l’annotation, confirmée par l’expérience, peut expliquer son succès séculaire