Higher coordination with less control - A result of information maximization in the sensorimotor loop

This work presents a novel learning method in the context of embodied artificial intelligence and self-organization, which has as few assumptions and restrictions as possible about the world and the underlying model. The learning rule is derived from the principle of maximizing the predictive information in the sensorimotor loop. It is evaluated on robot chains of varying length with individually controlled, non-communicating segments. The comparison of the results shows that maximizing the predictive information per wheel leads to a higher coordinated behavior of the physically connected robots compared to a maximization per robot. Another focus of this paper is the analysis of the effect of the robot chain length on the overall behavior of the robots. It will be shown that longer chains with less capable controllers outperform those of shorter length and more complex controllers. The reason is found and discussed in the information-geometric interpretation of the learning process

Empowerment and State-dependent Noise : An Intrinsic Motivation for Avoiding Unpredictable Agents

Empowerment is a recently introduced intrinsic motivation algorithm based on the embodiment of an agent and the dynamics of the world the agent is situated in. Computed as the channel capacity from an agent’s actuators to an agent’s sensors, it offers a quantitative measure of how much an agent is in control of the world it can perceive. In this paper, we expand the approximation of empowerment as a Gaussian linear channel to compute empowerment based on the covariance matrix between actuators and sensors, incorporating state dependent noise. This allows for the first time the study of continuous systems with several agents. We found that if the behaviour of another agent cannot be predicted accurately, then interacting with that agent will decrease the empowerment of the original agent. This leads to behaviour realizing collision avoidance with other agents, purely from maximising an agent’s empowermentFinal Accepted Versio

Modulation of exploratory behavior for adaptation to the context

For autonomous agents (children, animals or robots), exploratory learning is essential as it allows them to take advantage of their past experiences in order to improve their reactions in any situation similar to a situation already experimented. We have already exposed in Blanchard and Canamero (2005) how a robot can learn which situations it should memorize and try to reach, but we expose here architectures allowing the robot to take initiatives and explore new situations by itself. However, exploring is a risky behavior and we propose to moderate this behavior using novelty and context based on observations of animals behaviors. After having implemented and tested these architectures, we present a very interesting emergent behavior which is low-level imitation modulated by context

Changing the Environment Based on Empowerment as Intrinsic Motivation

This is an open access article distributed under the Creative Commons Attribution License CC BY 3.0 which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.One aspect of intelligence is the ability to restructure your own environment so that the world you live in becomes more beneficial to you. In this paper we investigate how the information-theoretic measure of agent empowerment can provide a task-independent, intrinsic motivation to restructure the world. We show how changes in embodiment and in the environment change the resulting behaviour of the agent and the artefacts left in the world. For this purpose, we introduce an approximation of the established empowerment formalism based on sparse sampling, which is simpler and significantly faster to compute for deterministic dynamics. Sparse sampling also introduces a degree of randomness into the decision making process, which turns out to beneficial for some cases. We then utilize the measure to generate agent behaviour for different agent embodiments in a Minecraft-inspired three dimensional block world. The paradigmatic results demonstrate that empowerment can be used as a suitable generic intrinsic motivation to not only generate actions in given static environments, as shown in the past, but also to modify existing environmental conditions. In doing so, the emerging strategies to modify an agent’s environment turn out to be meaningful to the specific agent capabilities, i.e., de facto to its embodiment.Peer reviewedFinal Published versio

Pioneers, subcultures and cooperatives: The grassroots augmentation of urban places

About the book: There have been numerous possible scenarios depicted on the impact of the internet on urban spaces. Considering ubiquitous/pervasive computing, mobile, wireless connectivity and the acceptance of the Internet as a non extraordinary part of our everyday lives mean that physical urban space is augmented, and digital in itself. This poses new problems as well as opportunities to those who have to deal with it. This book explores the intersection and articulation of physical and digital environments and the ways they can extend and reshape a spirit of place. It considers this from three main perspectives: the implications for the public sphere and urban public or semi-public spaces; the implications for community regeneration and empowerment; and the dilemmas and challenges which the augmentation of space implies for urbanists. Grounded with international real -life case studies, this is an up-to-date, interdisciplinary and holistic overview of the relationships between cities, communities and high technologies

General self-motivation and strategy identification : Case studies based on Sokoban and Pac-Man

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.In this paper, we use empowerment, a recently introduced biologically inspired measure, to allow an AI player to assign utility values to potential future states within a previously unencountered game without requiring explicit specification of goal states. We further introduce strategic affinity, a method of grouping action sequences together to form "strategies," by examining the overlap in the sets of potential future states following each such action sequence. We also demonstrate an information-theoretic method of predicting future utility. Combining these methods, we extend empowerment to soft-horizon empowerment which enables the player to select a repertoire of action sequences that aim to maintain anticipated utility. We show how this method provides a proto-heuristic for nonterminal states prior to specifying concrete game goals, and propose it as a principled candidate model for "intuitive" strategy selection, in line with other recent work on "self-motivated agent behavior." We demonstrate that the technique, despite being generically defined independently of scenario, performs quite well in relatively disparate scenarios, such as a Sokoban-inspired box-pushing scenario and in a Pac-Man-inspired predator game, suggesting novel and principle-based candidate routes toward more general game-playing algorithms.Peer reviewedFinal Accepted Versio

Ongoing Emergence: A Core Concept in Epigenetic Robotics

We propose ongoing emergence as a core concept in epigenetic robotics. Ongoing emergence refers to the continuous development and integration of new skills and is exhibited when six criteria are satisfied: (1) continuous skill acquisition, (2) incorporation of new skills with existing skills, (3) autonomous development of values and goals, (4) bootstrapping of initial skills, (5) stability of skills, and (6) reproducibility. In this paper we: (a) provide a conceptual synthesis of ongoing emergence based on previous theorizing, (b) review current research in epigenetic robotics in light of ongoing emergence, (c) provide prototypical examples of ongoing emergence from infant development, and (d) outline computational issues relevant to creating robots exhibiting ongoing emergence

Approximation of empowerment in the continuous domain

The empowerment formalism offers a goal-independent utility function fully derived from an agent's embodiment. It produces intrinsic motivations which can be used to generate self-organizing behaviours in agents. One obstacle to the application of empowerment in more demanding (esp. continuous) domains is that previous ways of calculating empowerment have been very time consuming and only provided a proof-of-concept. In this paper we present a new approach to efficiently approximate empowerment as a parallel, linear, Gaussian channel capacity problem. We use pendulum balancing to demonstrate this new method, and compare it to earlier approximation methods.Peer reviewe

Emerging Linguistic Functions in Early Infancy

This paper presents results from experimental studies on early language acquisition in infants and attempts to interpret the experimental results within the framework of the Ecological Theory of Language Acquisition (ETLA) recently proposed by (Lacerda et al., 2004a). From this perspective, the infant’s first steps in the acquisition of the ambient language are seen as a consequence of the infant’s general capacity to represent sensory input and the infant’s interaction with other actors in its immediate ecological environment. On the basis of available experimental evidence, it will be argued that ETLA offers a productive alternative to traditional descriptive views of the language acquisition process by presenting an operative model of how early linguistic function may emerge through interaction

Creatividad en el aula de clase: la importancia de las didácticas disruptivas en relación a la generación de motivaciones para enseñar y aprender: Criatividade em sala de aula: a importância da didática disruptiva em relação à geração de motivações para ensinar e aprender

Los procesos de aprendizaje implican una mediación de procesos de pensamiento creativo. Así mismo, se ha considerado que la ruptura de paradigmas fundamentada en el uso de la flexibilidad perceptual y cognitiva aporta en el aprendizaje. En atención a estos preceptos, se propuso adelantar un estudio teórico de corte reflexivo y exploratorio con el ánimo de hacer un acercamiento a la relación que existe entre creatividad y didácticas de clase, entre modelos disruptivos y prácticas pedagógicas. Tras hacer una revisión metódica de diversas publicaciones y haciendo contrastes entre teorías de aprendizaje y modelos didácticos disruptivos, se obtuvieron resultados que permiten reconocer vínculos entre la innovación pedagógica, la creatividad y el aprendizaje. Se concluye que la implementación de nuevos modelos didácticos es algo que debe tener en consideración la necesidad de establecer cómo las motivaciones intrínsecas y extrínsecas afectan al estudiante tanto en la apropiación y construcción del conocimiento como en el involucramiento con sus propios procesos de aprendizaje