Object-Oriented Dynamics Learning through Multi-Level Abstraction

Object-based approaches for learning action-conditioned dynamics has demonstrated promise for generalization and interpretability. However, existing approaches suffer from structural limitations and optimization difficulties for common environments with multiple dynamic objects. In this paper, we present a novel self-supervised learning framework, called Multi-level Abstraction Object-oriented Predictor (MAOP), which employs a three-level learning architecture that enables efficient object-based dynamics learning from raw visual observations. We also design a spatial-temporal relational reasoning mechanism for MAOP to support instance-level dynamics learning and handle partial observability. Our results show that MAOP significantly outperforms previous methods in terms of sample efficiency and generalization over novel environments for learning environment models. We also demonstrate that learned dynamics models enable efficient planning in unseen environments, comparable to true environment models. In addition, MAOP learns semantically and visually interpretable disentangled representations.Comment: Accepted to the Thirthy-Fourth AAAI Conference On Artificial Intelligence (AAAI), 202

An evolutionary model for the dynamics of vertical integration and network-based production

The concept of modularity has gained recently a growing attention in the management literature as a key to explain the contemporary trends of industrial dynamics. A stronger exploitation of external «networkbased» economies with respect to internal «bureaucracy-based» economies is one of the major consequences of the diffusion of flexible production systems and of modular architectures for products. To explain this connection, in this paper a model is presented which tries to explain the co-evolution of technology and organization as the outcome of a complex evolutionary process. In particular, through a set of exploratory agent-based simulations we try to show the existence of qualitatively different dynamic processes in coincidence of different phases and specific conditions of product and technology development, as well as the existence of a relationship between this dynamics and other competitive factors, determining a wide set of structurally different dynamical patterns in coincidence of different combinations of factors.modularity, modular networks

From specialists to generalists : inductive biases of deep learning for higher level cognition

Les réseaux de neurones actuels obtiennent des résultats de pointe dans une gamme de domaines problématiques difficiles. Avec suffisamment de données et de calculs, les réseaux de neurones actuels peuvent obtenir des résultats de niveau humain sur presque toutes les tâches. En ce sens, nous avons pu former des spécialistes capables d'effectuer très bien une tâche particulière, que ce soit le jeu de Go, jouer à des jeux Atari, manipuler le cube Rubik, mettre des légendes sur des images ou dessiner des images avec des légendes. Le prochain défi pour l'IA est de concevoir des méthodes pour former des généralistes qui, lorsqu'ils sont exposés à plusieurs tâches pendant l'entraînement, peuvent s'adapter rapidement à de nouvelles tâches inconnues. Sans aucune hypothèse sur la distribution génératrice de données, il peut ne pas être possible d'obtenir une meilleure généralisation et une meilleure adaptation à de nouvelles tâches (inconnues). Les réseaux de neurones actuels obtiennent des résultats de pointe dans une gamme de domaines problématiques difficiles. Une possibilité fascinante est que l'intelligence humaine et animale puisse être expliquée par quelques principes, plutôt qu'une encyclopédie de faits. Si tel était le cas, nous pourrions plus facilement à la fois comprendre notre propre intelligence et construire des machines intelligentes. Tout comme en physique, les principes eux-mêmes ne suffiraient pas à prédire le comportement de systèmes complexes comme le cerveau, et des calculs importants pourraient être nécessaires pour simuler l'intelligence humaine. De plus, nous savons que les vrais cerveaux intègrent des connaissances a priori détaillées spécifiques à une tâche qui ne pourraient pas tenir dans une courte liste de principes simples. Nous pensons donc que cette courte liste explique plutôt la capacité des cerveaux à apprendre et à s'adapter efficacement à de nouveaux environnements, ce qui est une grande partie de ce dont nous avons besoin pour l'IA. Si cette hypothèse de simplicité des principes était correcte, cela suggérerait que l'étude du type de biais inductifs (une autre façon de penser aux principes de conception et aux a priori, dans le cas des systèmes d'apprentissage) que les humains et les animaux exploitent pourrait aider à la fois à clarifier ces principes et à fournir source d'inspiration pour la recherche en IA. L'apprentissage en profondeur exploite déjà plusieurs biais inductifs clés, et mon travail envisage une liste plus large, en se concentrant sur ceux qui concernent principalement le traitement cognitif de niveau supérieur. Mon travail se concentre sur la conception de tels modèles en y incorporant des hypothèses fortes mais générales (biais inductifs) qui permettent un raisonnement de haut niveau sur la structure du monde. Ce programme de recherche est à la fois ambitieux et pratique, produisant des algorithmes concrets ainsi qu'une vision cohérente pour une recherche à long terme vers la généralisation dans un monde complexe et changeant.Current neural networks achieve state-of-the-art results across a range of challenging problem domains. Given enough data, and computation, current neural networks can achieve human-level results on mostly any task. In the sense, that we have been able to train \textit{specialists} that can perform a particular task really well whether it's the game of GO, playing Atari games, Rubik's cube manipulation, image caption or drawing images given captions. The next challenge for AI is to devise methods to train \textit{generalists} that when exposed to multiple tasks during training can quickly adapt to new unknown tasks. Without any assumptions about the data generating distribution it may not be possible to achieve better generalization and adaption to new (unknown) tasks. A fascinating possibility is that human and animal intelligence could be explained by a few principles (rather than an encyclopedia). If that was the case, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human intelligence. In addition, we know that real brains incorporate some detailed task-specific a priori knowledge which could not fit in a short list of simple principles. So we think of that short list rather as explaining the ability of brains to learn and adapt efficiently to new environments, which is a great part of what we need for AI. If that simplicity of principles hypothesis was correct it would suggest that studying the kind of inductive biases (another way to think about principles of design and priors, in the case of learning systems) that humans and animals exploit could help both clarify these principles and provide inspiration for AI research. Deep learning already exploits several key inductive biases, and my work considers a larger list, focusing on those which concern mostly higher-level cognitive processing. My work focuses on designing such models by incorporating in them strong but general assumptions (inductive biases) that enable high-level reasoning about the structure of the world. This research program is both ambitious and practical, yielding concrete algorithms as well as a cohesive vision for long-term research towards generalization in a complex and changing world

Probabilistic Modeling Paradigms for Audio Source Separation

This is the author's final version of the article, first published as E. Vincent, M. G. Jafari, S. A. Abdallah, M. D. Plumbley, M. E. Davies. Probabilistic Modeling Paradigms for Audio Source Separation. In W. Wang (Ed), Machine Audition: Principles, Algorithms and Systems. Chapter 7, pp. 162-185. IGI Global, 2011. ISBN 978-1-61520-919-4. DOI: 10.4018/978-1-61520-919-4.ch007file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04file: VincentJafariAbdallahPD11-probabilistic.pdf:v\VincentJafariAbdallahPD11-probabilistic.pdf:PDF owner: markp timestamp: 2011.02.04Most sound scenes result from the superposition of several sources, which can be separately perceived and analyzed by human listeners. Source separation aims to provide machine listeners with similar skills by extracting the sounds of individual sources from a given scene. Existing separation systems operate either by emulating the human auditory system or by inferring the parameters of probabilistic sound models. In this chapter, the authors focus on the latter approach and provide a joint overview of established and recent models, including independent component analysis, local time-frequency models and spectral template-based models. They show that most models are instances of one of the following two general paradigms: linear modeling or variance modeling. They compare the merits of either paradigm and report objective performance figures. They also,conclude by discussing promising combinations of probabilistic priors and inference algorithms that could form the basis of future state-of-the-art systems

Inductive Biases for Deep Learning of Higher-Level Cognition

A fascinating hypothesis is that human and animal intelligence could be explained by a few principles (rather than an encyclopedic list of heuristics). If that hypothesis was correct, we could more easily both understand our own intelligence and build intelligent machines. Just like in physics, the principles themselves would not be sufficient to predict the behavior of complex systems like brains, and substantial computation might be needed to simulate human-like intelligence. This hypothesis would suggest that studying the kind of inductive biases that humans and animals exploit could help both clarify these principles and provide inspiration for AI research and neuroscience theories. Deep learning already exploits several key inductive biases, and this work considers a larger list, focusing on those which concern mostly higher-level and sequential conscious processing. The objective of clarifying these particular principles is that they could potentially help us build AI systems benefiting from humans' abilities in terms of flexible out-of-distribution and systematic generalization, which is currently an area where a large gap exists between state-of-the-art machine learning and human intelligence.Comment: This document contains a review of authors research as part of the requirement of AG's predoctoral exam, an overview of the main contributions of the authors few recent papers (co-authored with several other co-authors) as well as a vision of proposed future researc

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic