Adaptive Self-organizing System Research Articles

Remote Collaboration and Virtual Team Performance Tools, Generative Artificial Intelligence and Adaptive Self-Organizing Systems, and Wearable Augmented Reality Devices in Immersive Work Environments

Remote Collaboration and Virtual Team Performance Tools, Generative Artificial Intelligence and Adaptive Self-Organizing Systems, and Wearable Augmented Reality Devices in Immersive Work Environments

Adaptive Information Sharing with Ontological Relevance Computation for Decentralized Self-Organization Systems.

Decentralization is a peculiar characteristic of self-organizing systems such as swarm intelligence systems, which function as complex collective responsive systems without central control and operates based on contextual local coordination among relatively simple individual systems. The decentralized particularity of self-organizing systems lies in their capacity to spontaneously respond to accommodate environmental changes in a cooperative manner without external control. However, if members cannot obtain observations of the state of the whole team and environment, they have to share their knowledge and policies with each other through communication in order to adapt to the environment appropriately. In this paper, we propose an information sharing mechanism as an independent decision phase to improve individual members’ joint adaption to the world to fulfill an optimal self-organization in general. We design the information sharing decision analogous to human information sharing mechanisms. In this case, information can be shared among individual members by evaluating the semantic relationship of information based on ontology graph and their local knowledge. That is, if individual member collects more relevant information, the information will be used to update its local knowledge and improve sharing relevant information by measuring the ontological relevance. This will enable more related information to be acquired so that their models will be reinforced for more precise information sharing. Our simulations and experimental results show that this design can share information efficiently to achieve optimal adaptive self-organizing systems.

The interplay of top-down planning and adaptive self-organization in an African floodplain

Natural floodplains are complex social-ecological systems in which human livelihoods are tightly coupled with flooding dynamics. In this paper, we argue that hydraulic planning in such systems, including to mitigate extreme floods, must consider three key features of adaptive self-organizing systems in floodplains - connectivity, learning feedbacks, and rhythms - to support the resilience of floodplain communities to extreme floods. We illustrate this argument with a case study of the Logone Floodplain, Cameroon. Based on hydrological data, ethnographic research and a series of focus group interviews with floodplain villagers, we analyze the interplay of top-down planning and adaptive self-organization in response to two extreme floods in 2012 and 2015. We show that recent top-down strategies of hydraulic control in the floodplain have led to more hydraulic uncertainty for local populations, and curtailed the conditions for sustainable self-organization in the floodplain.

Some neuromyths concerning the effectiveness of cochlear implants: Inferring function from dysfunction

Cochlear implants provide profoundly deaf infants and young children with access to critical temporal and spectral patterns of speech needed for language development. Despite the enormous benefits of cochlear implants, many controversial questions remain. I will consider ten pressing issues that are the focus of current efforts in the field: (1) individual variability in speech and language outcomes, (2) neuroplasticity and early implantation, (3) learning and linguistic experience, (4) linguistic vs indexical channels in speech perception, (5) bilateral vs. unilateral implantation, (6) Contribution of other neural and cognitive systems, (7) early predictors of outcomes and risk factors, (8) workload and mental effort, (9) speech in noise, and (10) acoustic simulations in normal-hearing listeners. These issues raise important theoretical questions about basic processes in speech perception and spoken language processing. Cochlear implant research may be viewed as a “model system” enabling inferences about normal function from the study of dysfunction providing additional converging support for the proposal that the ear does not function in isolation from the rest of the brain; it is an inseparable component of a complex adaptive self-organizing system that evolved to support the perception and production of spoken language. [Work supported by NIH Grants: R01 DC-XYZ and DC-ABC to Indiana University.]

On a Time-Varying Parameter Adaptive Self-Organizing System in the Presence of Large Outliers in Observations

In the previous papers (Pupeikis, 2000; Genov et al., 2006; Atanasov and Pupeikis, 2009), a direct approach for estimating the parameters of a discrete-time linear time-invariant (LTI) dynamic system, acting in a closed-loop in the case of additive noise with contaminating outliers uniformly spread in it, is presented. It is assumed there that the parameters of the LQG (Linear Quadratic Gaussian) controller are unknown, as well as known beforehand, too. The aim of the given paper is development of a minimum variance control (MVC) approach for a closed-loop discrete-time linear dynamic system when slowly or suddenly time-varying coefficients of the transfer function of such a system as well as that of a minimum variance (MV) controller are not known and ought to be estimated. The recursive parametric identification of an open-loop system and determination of the coefficients of the MV controller are performed in each current operation by processing observations in the case of additive noise at the output with contaminating outliers uniformly spread in it. The robust recursive technique, based on the S-algorithm, with a version of Shweppe's GM-estimator and with discounting previous data, used in the estimation, by introducing a constant as well as time-varying forgetting factors in the abovementioned estimator, is applied here in the calculation of estimates of the parameters of a dynamic system. Then, the recursive parameter estimates are used in each current iteration to determine unknown parameters of the MV controller. Afterwards, the current value of the MV control signal is found in each operation, and it is used to generate the output of the system, too. The results of numerical simulation by computer are presented and discussed.

On a parameter adaptive self-organizing system in the presence of large outliers in observations

The aim of the given paper is development of a minimum variance control (MVC) approach for a closed-loopdiscrete-time linear time-invariant (LTI) system when the parameters of a dynamic system as well as that of a controller are not known and ought to be estimated by processing observations in the case of additive Gaussian noise on the output with contaminating outliers uniformly spread in it. Afterwards, the current value of the control signal is found in each operation, and it is used to generate the output of the system. The results of numerical simulation by computer are presented and discussed here, too.

Self-Organization and Adaptation in Intelligent Systems

This special issue is devoted to one of the important topics of current intelligent information systems-their ability to adapt to the environment they operate in, as adaptation is one of the most important features of intelligence. Several milestones in the literature on adaptive systems mark the development in this area. The Hebbian learning rule,1) self-organizing maps,2,3) and adaptive resonance theory4) have influenced the research in this area a great deal. Some current development suggests methods for building adaptive neurofuzzy systems,5) and adaptive self-organizing systems based on principles from biological brains.6) The papers in this issue are organized as follows: The first two papers present material on organization and adaptation in the human brain. The third paper, by Kasabov, presents a novel approach to building open structured adaptive systems for on-line adaptation called evolving connectionist systems. The fourth paper by Kawahara and Saito suggests a method for building virtually connected adaptive cell structures. Papers 5 and 6 discuss the use of genetic algorithms and evolutionary computation for optimizing and adapting the structure of an intelligent system. The last two papers suggest methods for adaptive learning of a sequence of data in a feed-forward neural network that has a fixed structure.

Dynamic optimization by the method of generalized quasigradients

Since the method of generalized quasigradients is fairly general [3, 4], the results obtained with respect to the convergence of the investigated methods of dynamic optimization are applicable to a wide range of extremal problems with perhaps nondifferentiable criteria. Although the proof of the convergence was carried out for deterministic functionals as a particular case, it is easy to obtain from these results the convergence also in the case of minimization of probabilistic, dynamic functionals, when the minimum of a time-dependent regression function is sought. Such problems occur in pattern recognition, and in the development of adaptive self-organizing systems under nonstationary conditions; they can be solved by dynamic methods of stochastic approximation. By analogy with [3], the above theorems on convergence of algorithms of dynamic optimization by the method of generalized quasigradients can be used for proving the convergence in the case of minimization of dynamic problems of large dimension by the method of statistical gradients, the solution of game-type dynamic stochastic problems, etc.