Learning the optimum as a Nash equilibrium

Cataloged from PDF version of article.This paper shows the computational benefits of a game theoretic approach to optimization of high dimensional control problems. A dynamic noncooperative game framework is adopted to partition the control space and to search the optimum as the equilibrium of a k-person dynamic game played by k-parallel genetic algorithms. When there are multiple inputs, we delegate control authority over a set of control variables exclusively to one player so that k artificially intelligent players explore and communicate to learn the global optimum as the Nash equilibrium. In the case of a single input, each player's decision authority becomes active on exclusive sets of dates-so that k GAs construct the optimal control trajectory as the equilibrium of evolving best-to-date responses. Sample problems are provided to demonstrate the gains in computational speed and accuracy. (C) 2000 Elsevier Science B.V. All rights reserved

On-line Computation of Stackelberg Equilibria with Synchronous Parallel Genetic Algorithms

Cataloged from PDF version of article.This paper develops a method to compute the Stackelberg equilibria in sequential games. We construct a normal form game which is interactively played by an artificially intelligent leader, GAL, and a follower, GA(F). The leader is a genetic algorithm breeding a population of potential actions to better anticipate the follower's reaction. The follower is also a genetic algorithm training on-line a suitable neural network to evolve a population of rules to respond to any move in the leader's action space. When GAs repeatedly play this game updating each other synchronously, populations converge to the Stackelberg equilibrium of the sequential game. We provide numerical examples attesting to the efficiency of the algorithm. (C) 2002 Elsevier Science B.V. All rights reserved

Economic growth, trade and environmental quality in a two-region world economy

Cataloged from PDF version of article.This paper examines the linkages between international trade, environmental degradation, and economic growth, in a dynamic North-South trade game. The North produces manufactured goods by employing capital, labor, and a natural resource that it imports from the South, using a neoclassical production function subject to an endogenously improving technology. The South extracts the resource using raw labor, in the process generating local pollution. Genetic algorithms (GA) are used to search for optimal policies in the presence of local pollution and technology spillovers from North to South. In the GA search for optimal regional policies, both noncooperative and cooperative modes of trade are considered. Noncooperative trade results in inefficiencies stemming from externalities. Though cooperative trade policies are efficient, they lack credibility. A joint maximization of the global welfare shows that transfer of technology is a viable route to improve world welfare

A genetic game of trade, growth and externalities

A genetic algorithm is introduced to search for optimal policies in the presence of knowledge spillovers and local pollution in a dynamic North/South trade game. Non-cooperative trade compounds inefficiencies stemming from externalities. Cooperative trade policies are efficient and yet not credible. Short of a joint maximization of the global welfare, transfer of knowledge remains as a viable route to improve world welfare. © 1998 Elsevier Science B.V. All rights reserved

Feedback approximation of the stochastic growth model by genetic neural networks

A direct numerical optimization method is developed to approximate the one-sector stochastic growth model. The feedback investment policy is parameterized as a neural network and trained by a genetic algorithm to maximize the utility functional over the space of time-invariant investment policies. To eliminate the dependence of training on the initial conditions, at any generation, the same stationary investment policy (the same network) is used to repeatedly solve the problem from differing initial conditions. The fitness of a given policy rule is then computed as the sum of payoffs over all initial conditions. The algorithm performs quite well under a wide set of parameters. Given the general purpose nature of the method, the flexibility of neural network parametrization and the global nature of the genetic algorithm search, it can be easily extended to tackle problems with higher dimensional nonlinearities, state spaces and/or discontinuities. © Springer Science+Business Media, Inc. 2006

Influence of recycled basalt-aramid fibres integration on the mechanical and thermal properties of brake friction composites

In the brake friction composites(BFCs), fibres take part in significant attention as reinforcement in governing mechanical and thermal-mechanical properties. The current investigation aims to develop hybrid brake friction composites using recycled basalt- aramid fibre integration and to characterise for its mechanical and thermal properties. The experiments related to thermal (heat swell, loss of ignition and thermal conductivity) and mechanical (tensile, compression, flexural and impact) properties were conducted as per industrial standards. From the experimental investigations, it was concluded that fibre inclusion in the BFCs enhanced the mechanical and thermal properties considerably. Further, with the aid of scanning electron microscope (SEM), fracture interfaces of the tested friction composites were analyzed for various characteristics like pullout, void, fibre-matrix bonding etc

Brain function assessment in different conscious states

Background: The study of brain functioning is a major challenge in neuroscience fields as human brain has a dynamic and ever changing information processing. Case is worsened with conditions where brain undergoes major changes in so-called different conscious states. Even though the exact definition of consciousness is a hard one, there are certain conditions where the descriptions have reached a consensus. The sleep and the anesthesia are different conditions which are separable from each other and also from wakefulness. The aim of our group has been to tackle the issue of brain functioning with setting up similar research conditions for these three conscious states.Methods: In order to achieve this goal we have designed an auditory stimulation battery with changing conditions to be recorded during a 40 channel EEG polygraph (Nuamps) session. The stimuli (modified mismatch, auditory evoked etc.) have been administered both in the operation room and the sleep lab via Embedded Interactive Stimulus Unit which was developed in our lab. The overall study has provided some results for three domains of consciousness. In order to be able to monitor the changes we have incorporated Bispectral Index Monitoring to both sleep and anesthesia conditions.Results: The first stage results have provided a basic understanding in these altered states such that auditory stimuli have been successfully processed in both light and deep sleep stages. The anesthesia provides a sudden change in brain responsiveness; therefore a dosage dependent anesthetic administration has proved to be useful. The auditory processing was exemplified targeting N1 wave, with a thorough analysis from spectrogram to sLORETA. The frequency components were observed to be shifting throughout the stages. The propofol administration and the deeper sleep stages both resulted in the decreasing of N1 component. The sLORETA revealed similar activity at BA7 in sleep (BIS 70) and target propofol concentration of 1.2 μg/mL.Conclusions: The current study utilized similar stimulation and recording system and incorporated BIS dependent values to validate a common approach to sleep and anesthesia. Accordingly the brain has a complex behavior pattern, dynamically changing its responsiveness in accordance with stimulations and states. © 2010 Ozgoren et al; licensee BioMed Central Ltd

Preparation of cellulose nanofibers with hydrophobic surface characteristics.

The aim of this study was to develop cellulose nanofibers with hydrophobic surface characteristics using chemical modification. Kenaf fibers were modified using acetic anhydride and cellulose nanofibers were isolated from the acetylated kenaf using mechanical isolation methods. Fourier transform infrared spectroscopy (FTIR) indicated acetylation of the hydroxyl groups of cellulose. The study of the dispersion demonstrated that acetylated cellulose nanofibers formed stable, well-dispersed suspensions in both acetone and ethanol. The contact angle measurements showed that the surface characteristics of nanofibers were changed from hydrophilic to more hydrophobic when acetylated. The microscopy study showed that the acetylation caused a swelling of the kenaf fiber cell wall and that the diameters of isolated nanofibers were between 5 and 50 nm. X-ray analysis showed that the acetylation process reduced the crystallinity of the fibers, whereas mechanical isolation increased it. The method used provides a novel processing route for producing cellulose nanofibers with hydrophobic surfaces