Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

Behavioural robustness and the distributed mechanisms hypothesis

A current challenge in neuroscience and systems biology is to better understand properties that allow organisms to exhibit and sustain appropriate behaviours despite the effects of perturbations (behavioural robustness). There are still significant theoretical difficulties in this endeavour, mainly due to the context-dependent nature of the problem. Biological robustness, in general, is considered in the literature as a property that emerges from the internal structure of organisms, rather than being a dynamical phenomenon involving agent-internal controls, the organism body, and the environment. Our hypothesis is that the capacity for behavioural robustness is rooted in dynamical processes that are distributed between agent ‘brain’, body, and environment, rather than warranted exclusively by organisms’ internal mechanisms. Distribution is operationally defined here based on perturbation analyses. Evolutionary Robotics (ER) techniques are used here to construct four computational models to study behavioural robustness from a systemic perspective. Dynamical systems theory provides the conceptual framework for these investigations. The first model evolves situated agents in a goalseeking scenario in the presence of neural noise perturbations. Results suggest that evolution implicitly selects neural systems that are noise-resistant during coupling behaviour by concentrating search in regions of the fitness landscape that retain functionality for goal approaching. The second model evolves situated, dynamically limited agents exhibiting minimalcognitive behaviour (categorization task). Results indicate a small but significant tendency toward better performance under most types of perturbations by agents showing further cognitivebehavioural dependency on their environments. The third model evolves experience-dependent robust behaviour in embodied, one-legged walking agents. Evidence suggests that robustness is rooted in both internal and external dynamics, but robust motion emerges always from the systemin-coupling. The fourth model implements a historically dependent, mobile-object tracking task under sensorimotor perturbations. Results indicate two different modes of distribution, one in which inner controls necessarily depend on a set of specific environmental factors to exhibit behaviour, then these controls will be more vulnerable to perturbations on that set, and another for which these factors are equally sufficient for behaviours. Vulnerability to perturbations depends on the particular distribution. In contrast to most existing approaches to the study of robustness, this thesis argues that behavioural robustness is better understood in the context of agent-environment dynamical couplings, not in terms of internal mechanisms. Such couplings, however, are not always the full determinants of robustness. Challenges and limitations of our approach are also identified for future studies

Echo state model of non-Markovian reinforcement learning, An

Department Head: Dale H. Grit.2008 Spring.Includes bibliographical references (pages 137-142).There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to domains where the state-action space is approximately Markovian, a requirement for the overwhelming majority of real-world domains. These domains, termed non-Markovian reinforcement learning domains, raise a unique set of practical challenges. The reconstruction dimension required to approximate a Markovian state-space is unknown a priori and can potentially be large. Further, spatial complexity of local function approximation of the reinforcement learning domain grows exponentially with the reconstruction dimension. Parameterized dynamic systems alleviate both embedding length and state-space dimensionality concerns by reconstructing an approximate Markovian state-space via a compact, recurrent representation. Yet this representation extracts a cost; modeling reinforcement learning domains via adaptive, parameterized dynamic systems is characterized by instability, slow-convergence, and high computational or spatial training complexity. The objectives of this research are to demonstrate a stable, convergent, accurate, and scalable model of non-Markovian reinforcement learning domains. These objectives are fulfilled via fixed point analysis of the dynamics underlying the reinforcement learning domain and the Echo State Network, a class of parameterized dynamic system. Understanding models of non-Markovian reinforcement learning domains requires understanding the interactions between learning domains and their models. Fixed point analysis of the Mountain Car Problem reinforcement learning domain, for both local and nonlocal function approximations, suggests a close relationship between the locality of the approximation and the number and severity of bifurcations of the fixed point structure. This research suggests the likely cause of this relationship: reinforcement learning domains exist within a dynamic feature space in which trajectories are analogous to states. The fixed point structure maps dynamic space onto state-space. This explanation suggests two testable hypotheses. Reinforcement learning is sensitive to state-space locality because states cluster as trajectories in time rather than space. Second, models using trajectory-based features should exhibit good modeling performance and few changes in fixed point structure. Analysis of performance of lookup table, feedforward neural network, and Echo State Network (ESN) on the Mountain Car Problem reinforcement learning domain confirm these hypotheses. The ESN is a large, sparse, randomly-generated, unadapted recurrent neural network, which adapts a linear projection of the target domain onto the hidden layer. ESN modeling results on reinforcement learning domains show it achieves performance comparable to lookup table and neural network architectures on the Mountain Car Problem with minimal changes to fixed point structure. Also, the ESN achieves lookup table caliber performance when modeling Acrobot, a four-dimensional control problem, but is less successful modeling the lower dimensional Modified Mountain Car Problem. These performance discrepancies are attributed to the ESN’s excellent ability to represent complex short term dynamics, and its inability to consolidate long temporal dependencies into a static memory. Without memory consolidation, reinforcement learning domains exhibiting attractors with multiple dynamic scales are unlikely to be well-modeled via ESN. To mediate this problem, a simple ESN memory consolidation method is presented and tested for stationary dynamic systems. These results indicate the potential to improve modeling performance in reinforcement learning domains via memory consolidation

Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots' recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control

Réseaux ad hoc : système d'adressage et méthodes d'accessibilité aux données

RÉSUMÉ Au cours de la dernière décennie, un nouveau type de réseaux sans fil a suscité un grand intérêt dans la communauté scientifique: ce sont les réseaux ad hoc. Ils existent sous la variante des réseaux mobiles ad hoc (MANET), et des réseaux de capteurs sans fil (RCSF). Les réseaux mobiles ad hoc sont constitués de noeuds mobiles qui communiquent les uns avec les autres sans l‘aide d‘une d'infrastructure centralisée. Les noeuds se déplacent librement et sont soumis à des déconnexions fréquentes en raison de l'instabilité des liens. Cela a pour conséquence de diminuer l'accessibilité aux données, et de modifier la façon dont les données sont partagées dans le réseau. Comparable aux réseaux MANET, un RCSF est composé d'un ensemble d'unités de traitements embarquées, appelées capteurs, communiquant via des liens sans fil et dont la fonction principale est la collecte de paramètres relatifs à l'environnement qui les entoure, telles que la température, la pression, ou la présence d'objets. Les RCSF diffèrent des MANET de par le déploiement à grande échelle des noeuds, et trouvent leur application dans diverses activités de la société, tels les processus industriels, les applications militaires de surveillance, l'observation et le suivi d'habitat, etc. Lorsqu‘un grand nombre de capteurs sont déployés avec des dispositifs d'actionnement appelés acteurs, le RCSF devient un réseau de capteurs et d‘acteurs sans fil (RCASF). Dans une telle situation, les capteurs collaborent pour la détection des phénomènes physiques et rapportent les données afférentes aux acteurs qui les traitent et initient les actions appropriées. De nombreux travaux dans les RCSF supposent l'existence d'adresses et d'infrastructures de routage pour valider leurs propositions. Cependant, l‘allocation d‘adresses et le routage des données liées aux événements détectés dans ces réseaux restent des défis entiers, en particulier à cause du nombre élevé de capteurs et des ressources limitées dont ils disposent. Dans cette thèse, nous abordons le problème de l'accessibilité aux données dans les MANET, et les mécanismes d‘adressage et de routage dans les RCSF de grande taille.----------ABSTRACT During the last decade, a new type of wireless networks has stirred up great interest within the scientific community: there are ad hoc networks. They exist as mobile ad hoc networks (MANET), and wireless sensor (WSN). The mobile ad hoc networks consist of mobile nodes that communicate with each other without using a centralized infrastructure. The nodes move freely and are subject to frequent disconnections due to links instability. This has the effect of reducing data accessibility, and change the way data are shared across the network. Similar MANET networks, a WSN consists of a set of embedded processing units called sensors that communicate with each other via wireless links. Their main function is the collection of parameters relating to the environment around them, such as temperature, pressure, motion, video, etc. WSNs differ from the MANETs due to the large scale deployment of nodes, and are expected to have many applications in various fields, such as industrial processes, military surveillance, observation and monitoring of habitat, etc. When a large number of sensors which are resource-impoverished nodes are deployed with powerful actuation devices, the WSN becomes a Wireless Sensor and Actor Network (WSAN). In such a situation, the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data to perform appropriate action. Numerous works in WSN assumes the existence of addresses and routing infrastructure to validate their proposals. However, assigning addresses and delivering detected events remains highly challenging, specifically due to the sheer number of nodes. In this thesis, we address the problem of data accessibility in MANET, and that of addressing and routing in large scale WSN. This involves techniques such as data caching and replication to prevent the deterioration of data accessibility. The addressing system in WSN includes a distributed address allocation scheme and a routing infrastructure for both actors and sensors. Moreover, with the birth of the multimedia sensors, the traffic may be mixed with time sensitive packets and reliability-demanding packets. For that purpose, we also address the problem of providing quality of service (QoS) in the routing infrastructure for WSN

Proceedings of the 9th Arab Society for Computer Aided Architectural Design (ASCAAD) international conference 2021 (ASCAAD 2021): architecture in the age of disruptive technologies: transformation and challenges.

The ASCAAD 2021 conference theme is Architecture in the age of disruptive technologies: transformation and challenges. The theme addresses the gradual shift in computational design from prototypical morphogenetic-centered associations in the architectural discourse. This imminent shift of focus is increasingly stirring a debate in the architectural community and is provoking a much needed critical questioning of the role of computation in architecture as a sole embodiment and enactment of technical dimensions, into one that rather deliberately pursues and embraces the humanities as an ultimate aspiration

NASA/MSFC FY-85 Atmospheric Processes Research Review

The two main areas of focus for the research program are global scale processes and mesoscale processes. Geophysical fluid processes, satellite doppler lidar, satellite data analysis, atmospheric electricity, doppler lidar wind research, and mesoscale modeling are among the topics covered

Internet Traffic Engineering : An Artificial Intelligence Approach

Dissertação de Mestrado em Ciência de Computadores, apresentada à Faculdade de Ciências da Universidade do Port