Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

A theoretical and computational basis for CATNETS

The main content of this report is the identification and definition of market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. These build the theoretical foundation for the work within the following two years of the CATNETS project. --Grid Computing

Authority in the Context of Distributed Knowledge

The notion of distributed knowledge is increasingly often invoked in discussions of economic organization. In particular, the claim that authority is inefficient as a means of coordination in the context of distributed knowledge has become widespread. However, very little analysis has been dedicated to the relation between economic organization and distributed knowledge. In this paper, we concentrate on the role of authority as a coordination mechanism under conditions of distributed knowledge, and also briefly discuss other issues of economic organization. We clarify the meanings of authority and distributed knowledge, and criticize the above claim by arguing that authority may be a superior mechanism of coordination under distributed knowledge. We also discuss how distributed knowledge influences the boundaries of firms. Our arguments rely on insights in problem-solving and on ideas from organizational economics.Distributed knowledge, existence of authority, problem-solving, the boundaries of the firm

Production Networks Linkages, Innovation Processes and Social Management Technologies. A Methodological Approach Applied to the Volkswagen case in Argentina

The purpose of this paper -as a part of a wider research project - is to analyze the concept of production network from a methodological and theoretical viewpoint based on a three-plane perspective. These dimensions are the linkages among agents, the innovation activities, and the social management technology, including work process organization and the social agreement generation model in force. It is an experimentally methodological approach that tries to go from a theoretical conceptualization of the phenomenon to its empirical evaluation. The questions guiding this research are as follows: What are the variables and dimensions to be observed in the analysis of a group of interconnected firms in order to define a production network? Is it a unique definition or, on the contrary, does it involve a range of alternatives? What are the externalities generated by the agents who belong to one network? What is the relationship between the network’s firms’ technological behavior and their organizational counterpart? How are learning processes in the business firms linked to their own training systems? Has the social management technology some differential role in the learning process and in the development of skills? How do knowledge transmission processes manifest themselves within the “network”? What indicators are useful for the empirical identification of the different means of manifestation of the network according to the theoretical viewpoint adopted? How can those indicators be articulated in order to elaborate typologies intended for the identification of “hybrid” models? How can a complex indicator be built in order to show the different levels of circulation of intangible assets, development of learning processes and work process organization? In the first section, the conceptualization of the production “network” used in this paper is discussed. In the second section, most relevant variables and indicators are presented in order to feature the business firms and the network in terms of: a) type, quantity and quality of tangible and intangible exchanges among the agents; b) innovative capacity and learning; c) social management technology. Then we elaborate a typology of networks based on the consideration of the previous parameters. Lastly, in the fourth section, we discuss how the three dimensions interact in the case of Volkswagen and his forty main local suppliers.Innovation, production process, case study

MyoDex: A Generalizable Prior for Dexterous Manipulation

Human dexterity is a hallmark of motor control. Our hands can rapidly synthesize new behaviors despite the complexity (multi-articular and multi-joints, with 23 joints controlled by more than 40 muscles) of musculoskeletal sensory-motor circuits. In this work, we take inspiration from how human dexterity builds on a diversity of prior experiences, instead of being acquired through a single task. Motivated by this observation, we set out to develop agents that can build upon their previous experience to quickly acquire new (previously unattainable) behaviors. Specifically, our approach leverages multi-task learning to implicitly capture task-agnostic behavioral priors (MyoDex) for human-like dexterity, using a physiologically realistic human hand model - MyoHand. We demonstrate MyoDex's effectiveness in few-shot generalization as well as positive transfer to a large repertoire of unseen dexterous manipulation tasks. Agents leveraging MyoDex can solve approximately 3x more tasks, and 4x faster in comparison to a distillation baseline. While prior work has synthesized single musculoskeletal control behaviors, MyoDex is the first generalizable manipulation prior that catalyzes the learning of dexterous physiological control across a large variety of contact-rich behaviors. We also demonstrate the effectiveness of our paradigms beyond musculoskeletal control towards the acquisition of dexterity in 24 DoF Adroit Hand. Website: https://sites.google.com/view/myodexComment: Accepted to the 40th International Conference on Machine Learning (2023

Theoretical and Computational Basis for Economical Ressource Allocation in Application Layer Networks - Annual Report Year 1

This paper identifies and defines suitable market mechanisms for Application Layer Networks (ALNs). On basis of the structured Market Engineering process, the work comprises the identification of requirements which adequate market mechanisms for ALNs have to fulfill. Subsequently, two mechanisms for each, the centralized and the decentralized case are described in this document. --Grid Computing