Can Self-Organization Emerge through Dynamic Neural Fields Computation?

International audienceIn this paper, dynamic neural fields are used to develop key features of a cortically-inspired computational module. Under the perspective of designing computational systems that can exhibit the flexibility and genericity of the cortical substrate, using neural field as the competition layer for self-organizing modules has to be considered. However, despite the fact that they serve as a biologically-inspired model, applying dynamic neural fields to drive self-organization is not straightforward. In order to address that issue, an original method for evaluating neural field equations is proposed, based on statistical measurements of the field behavior in some scenarios. Limitations of classical neural field equations are then quantified, and an original field equation is proposed to overcome these difficulties. The performance of the proposed field model is discussed in comparison with some previously considered models, leading to the promotion of the proposed model as a suitable mean for processing competition in cortex-like computation for cognitive systems

Intelligent robust control of redun-dant smart robotic arm Pt II: Quantum computing KB optimizer

In the first part of the article, two ways of fuzzy controller’s implementation showed. First way applied one controller for all links of the manipulator and showed the best performance. However, such an implementation is not possible in complex control objects, such as a planar redundant manipulator with seven degrees of freedom (DoF). The second way use of separated control when an independent fuzzy controller controls each link. The decomposition control due to a slight decrease in the quality of management has greatly simplified the processes of creating and placing knowledge bases. In this paper (Part II), the advantages and limitations of intelligent control systems based on soft computing technology described. To eliminate the mismatch of the work of separate independent fuzzy controllers, methods for self-organizing coordination control based on quantum computing technologies to create and design robust intelligent control systems for robotic manipulators with 3DOF and 7DOF described. Quantum fuzzy inference as quantum self-organization algorithm of imperfect KBs introduced. Quantum computational intelligence smart toolkit QCOptKBTMbased on quantum fuzzy inference applied. QCOptKBTM toolkit include quantum deep machine learning in on line. Successful engineering application of end-to-end quantum computing information technologies (as quantum sophisticated algorithms and quantum programming) in searching of solutions of algorithmic unsolved problems in classical dynamic intelligent control systems, artificial intelligence (AI) and intelligent cognitive robotics discussed. Quantum computing supremacy in efficient solution of intractable classical tasks as global robustness of redundant robotic manipulator in unpredicted control situations demonstrated. As result, the new synergetic self-organization information effect of robust KB design from responses of imperfect KBs (partial KB robustness cretead on toolkit SCOptKBTM in Pat I) fined

Emergent Hierarchy Through Conductance-based Degree Constraints

The presence of hierarchy in many real-world networks is not yet fully understood. We observe that complex interaction networks are often coarse-grain models of vast modular networks, where tightly connected subgraphs are agglomerated into nodes for simplicity of representation and computational feasibility. The emergence of hierarchy in such growing complex networks may stem from one particular property of these ignored subgraphs: their graph conductance. Being a quantification of the main bottleneck of flow through the coarse-grain node, this scalar quantity implies a structural limitation and supports the consideration of heterogeneous degree constraints. The internal conductance values of the subgraphs are mapped onto integer degree restrictions, which we call internal bottlenecks, by using inverse cumulative density functions. This leads to a hidden variable model based on the rich-get-richer scheme. It is shown that imposing such restrictions generally leads to an increased measure of hierarchy and alters the tail of the degree distribution in a predictable way. Thus, a mechanism is provided whereby inherent limitations on network flow leads to hierarchical self-organization

Coordination approaches and systems - part I : a strategic perspective

This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective

Digital Tectonics as a Morphogenetic Process

p. 938-948Tectonics is a seminal concept that defines the nature of the relationship between architecture and its structural properties. The changing definition of the symbiotic relationship between structural engineering and architectural design may be considered one of the formative influences on the conceptual evolution of tectonics in different historical periods. Recent developments in the field of morphogenesis, digital media, theories techniques and methods of digital design have contributed a new models of integration between structure, material and form in digital tectonics. The objective of this paper is to propose and define tectonics as a model of morphogenetic process. The paper identifies and presents the manner in which theory and emerging concepts of morphogenesis as well as digital models of design are contributing to this new model. The paper first analyzes the historical evolution of tectonics as a concept and characterizes the emergence of theoretical framework reflected in concepts and terms related to morphogenesis.Oxman, R. (2010). Digital Tectonics as a Morphogenetic Process. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/695

Closure in artificial cell signalling networks - investigating the emergence of cognition in collectively autocatalytic reaction networks

Cell Signalling Networks (CSNs) are complex biochemical networks responsible for the coordination of cellular activities in response to internal and external stimuli. We hypothesize that CSNs are subsets of collectively autocatalytic reaction networks. The signal processing or cognitive abilities of CSNs would originate from the closure properties of these systems. We investigate how Artificial CSNs, regarded as minimal cognitive systems, could emerge and evolve under this condition where closure may interact with evolution. To assist this research, we employ a multi-level concurrent Artificial Chemistry based on the Molecular Classifier Systems and the Holland broadcast language. A critical issue for the evolvability of such undirected and autonomous evolutionary systems is to identify the conditions that would ensure evolutionary stability. In this paper we present some key features of our system which permitted stable cooperation to occur between the different molecular species through evolution. Following this, we present an experiment in which we evolved a simple closed reaction network to accomplish a pre-specified task. In this experiment we show that the signal-processing ability (signal amplification) directly resulted from the evolved systems closure properties