Biologically Motivated Distributed Designs for Adaptive Knowledge Management

We discuss how distributed designs that draw from biological network metaphors can largely improve the current state of information retrieval and knowledge management of distributed information systems. In particular, two adaptive recommendation systems named TalkMine and @ApWeb are discussed in more detail. TalkMine operates at the semantic level of keywords. It leads different databases to learn new and adapt existing keywords to the categories recognized by its communities of users using distributed algorithms. @ApWeb operates at the structural level of information resources, namely citation or hyperlink structure. It relies on collective behavior to adapt such structure to the expectations of users. TalkMine and @ApWeb are currently being implemented for the research library of the Los Alamos National Laboratory under the Active Recommendation Project. Together they define a biologically motivated information retrieval system, recommending simultaneously at the level of user knowledge categories expressed in keywords, and at the level of individual documents and their associations to other documents. Rather than passive information retrieval, with this system, users obtain an active, evolving interaction with information resources.Comment: To appear in Design Principles for the Immune System and Other Distributed Autonomous Systems. i. Cohen and L. Segel (Eds.). Oxford University Pres

NASA JSC neural network survey results

A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc

Medical Statistics: Current Developments in Statistical Methodology for Clinical Trials

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Artificiality in Social Sciences

This text provides with an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the computer power fast increase, gifting social sciences with formalization and experimentation tools previously owned by "hard" sciences alone. It shows that as "a new way of doing social sciences", artificial societies should undoubtedly contribute to a renewed approach in the study of sociality and should play a significant part in the elaboration of original theories of social phenomena.artificial societies; multi-agent systems; distributed artificial intelligence; complexity

Digital Ecosystems: Ecosystem-Oriented Architectures

We view Digital Ecosystems to be the digital counterparts of biological ecosystems. Here, we are concerned with the creation of these Digital Ecosystems, exploiting the self-organising properties of biological ecosystems to evolve high-level software applications. Therefore, we created the Digital Ecosystem, a novel optimisation technique inspired by biological ecosystems, where the optimisation works at two levels: a first optimisation, migration of agents which are distributed in a decentralised peer-to-peer network, operating continuously in time; this process feeds a second optimisation based on evolutionary computing that operates locally on single peers and is aimed at finding solutions to satisfy locally relevant constraints. The Digital Ecosystem was then measured experimentally through simulations, with measures originating from theoretical ecology, evaluating its likeness to biological ecosystems. This included its responsiveness to requests for applications from the user base, as a measure of the ecological succession (ecosystem maturity). Overall, we have advanced the understanding of Digital Ecosystems, creating Ecosystem-Oriented Architectures where the word ecosystem is more than just a metaphor.Comment: 39 pages, 26 figures, journa

On microelectronic self-learning cognitive chip systems

After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory. From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research. And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting conscious phenomena should crucially be restricted to extremely well defined constraints. Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details. In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche

Review and Comparative Analysis of Distributed Knowledge Management Systems

Distributed technologies attract researchers interest as they propose many technological, but as well organizational and end-user benefits. With development of Web 2.0 and Cloud computing, distributed networks are considered as new source of business opportunities. The present research will identify advantages and limitations of distributed knowledge management systems (DKMS). Thus technologies and models of distrubuted KMS will be assessed as an alternative approach to centralized KMS. A review of several theoretical DKMS model will be made in order to outline the common characteristics and alternative approaches to DKMS architecture. At the end will be summarised conclusions for development of new theoretical model of user-centered DKMS

When management encounters complexity

This paper aims at showing how management has come to encounter the sciences of complexity. Therefore the various levels and domains of management are outlined which leverage from the study of complexity. This is not, however, a descriptive study. Rather, we focus on how management can benefit from knowing of the sciences of complexity. New tools and rods, new languages and approaches are sketched that show a radical shift in management leading from a once dependent discipline from physics and engineering, towards a biologically and ecologically permeated new management.Whereas the main concern for complexity consists in understanding complex phenomena and systems, at the end a number of successful applications of complexity to management and entrepreneurial consulting are considered