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Suarez-Gonzalez, Adriana, Christian Lexer, and Quentin C. B. Cronk. "Adaptive introgression: a plant perspective." Biology Letters 14, no. 3 (March 2018): 20170688. http://dx.doi.org/10.1098/rsbl.2017.0688.
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Introgression is emerging as an important source of novel genetic variation, alongside standing variation and mutation. It is adaptive when such introgressed alleles are maintained by natural selection. Recently, there has been an explosion in the number of studies on adaptive introgression. In this review, we take a plant perspective centred on four lines of evidence: (i) introgression, (ii) selection, (iii) phenotype and (iv) fitness. While advances in genomics have contributed to our understanding of introgression and porous species boundaries (task 1), and the detection of signatures of selection in introgression (task 2), the investigation of adaptive introgression critically requires links to phenotypic variation and fitness (tasks 3 and 4). We also discuss the conservation implications of adaptive introgression in the face of climate change. Adaptive introgression is particularly important in rapidly changing environments, when standing genetic variation and mutation alone may only offer limited potential for adaptation. We conclude that clarifying the magnitude and fitness effects of introgression with improved statistical techniques, coupled with phenotypic evidence, has great potential for conservation and management efforts.
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Amirizadeh, Khosrow, and Rajeswari Mandava. "Fast Iterative model for Sequential-Selection-Based Applications." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 7 (February 14, 2014): 3689–96. http://dx.doi.org/10.24297/ijct.v12i7.3092.
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Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of these modifications, number of optimal selections will be maximized and stability of the algorithm under mentioned conditions may be amplified. This adaptive model with automatic step size computation may attractive for on-line applications in which, variance of observations vary with time and re-tuning their step size are unavoidable where, this re-tuning is not a simple task. The proposed model governed by upper confidence bound (UCB) approach in iterative form with automatic step size computation. It called adaptive UCB (AUCB) that may use in industrial robotics, autonomous control and intelligent selection or prediction tasks in the economical engineering applications under lack of information.
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Chen, Yi Mei. "Nonlinear Adaptive Tracking of the Steward Platform in Task Space." Applied Mechanics and Materials 511-512 (February 2014): 1017–21. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.1017.
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This paper presents an adaptive nonlinear controller equipped with a friction estimator for a 6 degree of freedom (DOF) parallel manipulator-steward platform. With the aid of direct adaptive technique and control Lyapunov function method, an adaptive controller is designed to complete the globally adaptive stability of the closed-loop system. The stabilized conditions and corresponding proof are also presented, and the selection method of the controller parameters is proposed. Simulation results are demonstrated in support of the proposed control scheme.
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Hattori, Masasi. "A quantitative model of optimal data selection in Wason's selection task." Quarterly Journal of Experimental Psychology Section A 55, no. 4 (October 2002): 1241–72. http://dx.doi.org/10.1080/02724980244000053.
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The optimal data selection model proposed by Oaksford and Chater (1994) successfully formalized Wason's selection task (Wason, 1966). The model, however, involved some questionable assumptions and was also not sufficient as a model of the task because it could not provide quantitative predictions of the card selection frequencies. In this paper, the model was revised to provide quantitative fits to the data. The model can predict the selection frequencies of cards based on a selection tendency function (STF), or conversely, it enables the estimation of subjective probabilities from data. Past experimental data were first re-analysed based on the model. In Experiment 1, the superiority of the revised model was shown. However, when the relationship between antecedent and consequent was forced to deviate from the biconditional form, the model was not supported. In Experiment 2, it was shown that sufficient emphasis on probabilistic information can affect participants’ performance. A detailed experimental method to sort participants by probabilistic strategies was introduced. Here, the model was supported by a subgroup of participants who used the probabilistic strategy. Finally, the results were discussed from the viewpoint of adaptive rationality.
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tiberghien, Kerensa, Wim Notebaert, Bert De Smedt, and Wim Fias. "Reactive and proactive control in arithmetical strategy selection." Journal of Numerical Cognition 3, no. 3 (January 30, 2018): 598–619. http://dx.doi.org/10.5964/jnc.v3i3.124.
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Individual differences in arithmetic have been explained by differences in cognitive processes and by arithmetic strategy use and selection. In the present study, we investigated the involvement of reactive and proactive control processes. We explored how variation in proactive and reactive control was related to individual differences in strategy selection. We correlated proactive and reactive measures obtained from the AX-CPT and an adjusted N-back task with a measure of strategy adaptiveness during a numerosity judgment task. The results showed that both measures of reactive control (of the AX-CPT and N-back task) correlated positively with strategy adaptiveness, while proactive control was not. This suggests that both cognitive control modes might have a different effect on adaptive strategy selection, where adaptive strategy selection seems to benefit from a transient (late) control mode, reactive control. We discuss these results in the light of the Dual Mechanisms Framework.
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Ma, Tengfei, Patrick Ferber, Siyu Huo, Jie Chen, and Michael Katz. "Online Planner Selection with Graph Neural Networks and Adaptive Scheduling." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5077–84. http://dx.doi.org/10.1609/aaai.v34i04.5949.
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Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference.Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based.The code is available at https://github.com/matenure/GNN_planner.
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Morgan, T. J. H., L. E. Rendell, M. Ehn, W. Hoppitt, and K. N. Laland. "The evolutionary basis of human social learning." Proceedings of the Royal Society B: Biological Sciences 279, no. 1729 (July 27, 2011): 653–62. http://dx.doi.org/10.1098/rspb.2011.1172.
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Humans are characterized by an extreme dependence on culturally transmitted information. Such dependence requires the complex integration of social and asocial information to generate effective learning and decision making. Recent formal theory predicts that natural selection should favour adaptive learning strategies, but relevant empirical work is scarce and rarely examines multiple strategies or tasks. We tested nine hypotheses derived from theoretical models, running a series of experiments investigating factors affecting when and how humans use social information, and whether such behaviour is adaptive, across several computer-based tasks. The number of demonstrators, consensus among demonstrators, confidence of subjects, task difficulty, number of sessions, cost of asocial learning, subject performance and demonstrator performance all influenced subjects' use of social information, and did so adaptively. Our analysis provides strong support for the hypothesis that human social learning is regulated by adaptive learning rules.
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Guo, Wenhua, Jiabao Gao, Yanbin Tian, Fan Yu, and Zuren Feng. "SAFS: Object Tracking Algorithm Based on Self-Adaptive Feature Selection." Sensors 21, no. 12 (June 11, 2021): 4030. http://dx.doi.org/10.3390/s21124030.
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Object tracking is one of the most challenging problems in the field of computer vision. In challenging object tracking scenarios such as illumination variation, occlusion, motion blur and fast motion, existing algorithms can present decreased performances. To make better use of the various features of the image, we propose an object tracking method based on the self-adaptive feature selection (SAFS) algorithm, which can select the most distinguishable feature sub-template to guide the tracking task. The similarity of each feature sub-template can be calculated by the histogram of the features. Then, the distinguishability of the feature sub-template can be measured by their similarity matrix based on the maximum a posteriori (MAP). The selection task of the feature sub-template is transformed into the classification task between feature vectors by the above process and adopt modified Jeffreys’ entropy as the discriminant metric for classification, which can complete the update of the sub-template. Experiments with the eight video sequences in the Visual Tracker Benchmark dataset evaluate the comprehensive performance of SAFS and compare them with five baselines. Experimental results demonstrate that SAFS can overcome the difficulties caused by scene changes and achieve robust object tracking.
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Schumacher, Eric H., Erick J. Lauber, Jennifer M. Glass, Eileen L. Zurbriggen, Leon Gmeindl, David E. Kieras, and David E. Meyer. "Concurrent response-selection processes in dual-task performance: Evidence for adaptive executive control of task scheduling." Journal of Experimental Psychology: Human Perception and Performance 25, no. 3 (1999): 791–814. http://dx.doi.org/10.1037/0096-1523.25.3.791.
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Forstmann, Birte U., Marcel Brass, Iring Koch, and D. Yves von Cramon. "Voluntary Selection of Task Sets Revealed by Functional Magnetic Resonance Imaging." Journal of Cognitive Neuroscience 18, no. 3 (March 1, 2006): 388–98. http://dx.doi.org/10.1162/jocn.2006.18.3.388.
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In everyday life, we have to selectively adapt our behavior to different situations and tasks. In cognitive psychology, such adaptive behavior can be investigated with the task-switching paradigm. However, in contrast to everyday life, in experiments participants are unequivocally told which task to perform. The present functional magnetic resonance imaging (fMRI) study was set out to investigate processes that are relevant when participants can decide by their own which task to perform. The number of tasks to choose from was varied between a forced condition (no choice) and two voluntary selection conditions (two or three choices). We expected to find prolonged reaction times as well as higher activations within the midcingulate cortex for the choice conditions compared to the no-choice condition. The fMRI results revealed a significant activation difference for the choice conditions versus the no-choice condition. For the choice contrast, activation was found in the rostral cingulate zone (RCZ) as well as the superior parietal lobule and the posterior part of the intraparietal sulcus. These activations revealed no selection-specific difference between three and two choices. Finally, a post hoc analysis showed that the activation in the RCZ is not associated with higher task-dependent response conflict when participants can select a task set. Taken together, these findings indicate that distinct brain areas are involved in the voluntary selection of abstract task set information.
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Ferreira, Giordano BS, and Matthias Scheutz. "Accidental encounters: can accidents be adaptive?" Adaptive Behavior 26, no. 6 (September 14, 2018): 285–307. http://dx.doi.org/10.1177/1059712318798601.
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Accidents happen in nature, from simple incidents like bumping into obstacles, to erroneously arriving at the wrong location, to mating with an unintended partner. Whether accidents are problematic for an animal depends on their context, frequency, and severity. In this article, we investigate the question of how accidents affect the task performance of agents in an agent-based simulation model for a wide class of tasks called “multi-agent territory exploration” tasks (MATE). In MATE tasks, agents have to visit particular locations of varying quality in partially observable environments within a fixed time window. As such, agents have to balance the quality of the location with how much energy they are willing to expend reaching it. Arriving at the wrong location by accident typically reduces task performance. We model agents based on two location selection strategies that are hypothesized to be widely used in nature: best-of-n and min-threshold. Our results show that the two strategies lead to different accident rates and thus overall different levels of performance based on the degree of competition among agents, as well as the quality, density, visibility, and distribution of target locations in the environment. We also show that in some cases, individual accidents can be advantageous for both the individual and the whole group.
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Pini, Giovanni, Arne Brutschy, Marco Frison, Andrea Roli, Marco Dorigo, and Mauro Birattari. "Task partitioning in swarms of robots: an adaptive method for strategy selection." Swarm Intelligence 5, no. 3-4 (October 5, 2011): 283–304. http://dx.doi.org/10.1007/s11721-011-0060-1.
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Tabaza, Badeea. "An adaptive intelligent framework for assessment & selection process in staffing task." Indian Journal of Science and Technology 14, no. 4 (January 30, 2021): 325–34. http://dx.doi.org/10.17485/ijst/v14i4.2154.
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Maruthi, V., P. Shanthi, and A. Umamakeswari. "Enhanced Adaptive Learning Mechanism for Cloud Selection." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 512. http://dx.doi.org/10.14419/ijet.v7i2.24.12149.
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Estimation of cloud services in a distributed computing environment is taking more interests. There is a wealth of developing cloud benefit assets that makes it difficult for the user to select the best administration related to own applications in an evolving multiple cloud environment, particularly for online processing applications. To make clients to choose their interested cloud adequately, we need a model which holds the cloud profits, and hence dynamic cloud benefit determination procedure named Dynamic Cloud Selection (DCS) is adapted. In this procedure of selected services, every cloud benefit business deals with some group of cloud administrations, and executes the DCS method. This paper studies the cloud selection and proposed a way to improve the cloud selection based on related measures. The measures are reliability, response time, throughput, availability, utilization, resilience, scalability, and elasticity. The system is contrived to enhance the cloud benefit choice powerfully and to restore the best administration result to the client. These measures are used to form best selection strategy. User memory requirement is also considered to improve the preferred task. Experimental results proved that using this new strategy, best cloud selection is made efficiently.
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Fujiki, Tomohisa, Kuniaki Kawabata, and Hajime Asama. "Adaptive Action Selection of Body Expansion Behavior in Multi-Robot System Using Communication." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 2 (February 20, 2007): 142–48. http://dx.doi.org/10.20965/jaciii.2007.p0142.
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In a multi-robot system, cooperation within robots is essential in order to execute tasks efficiently. The purpose of this study is to investigate how robots cooperate with each other using interactive communication. A fundamental role of communication in a multi-robot system is to control other robots by an intension transmission. We believe that a multi-robot system can be more adaptive by treating communication as an action. In this paper, we implemented the action adjustment function to achieve cooperation between two mobile robots. Also we discuss the results of computer simulations of collision avoidance as an example of cooperative task.
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Hu, Min. "Emergency Dynamic Alliance Partner Selection Based on Adaptive Genetic Algorithm." Applied Mechanics and Materials 668-669 (October 2014): 1621–24. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.1621.
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Emergency mobilization alliance partner selection process is longitudinally choice of the value chain to achieve a task. Value of subtasks coefficient has been discussed. Depending on the difficulty of the problem and analytical perspective, the model of emergency mobilization alliance partner selection is given to maximize the overall effectiveness of the emergency mobilization. The choice of partner selection using adaptive genetic algorithm is made and the comparison with other methods has been analyzed.
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Meyer, David E., David E. Kieras, Erick Lauber, Eric H. Schumacher, Jennifer Glass, Eileen Zurbriggen, Leon Gmeindl, and Dana Apfelblat. "Adaptive executive control: Flexible multiple-task performance without pervasive immutable response-selection bottlenecks." Acta Psychologica 90, no. 1-3 (November 1995): 163–90. http://dx.doi.org/10.1016/0001-6918(95)00026-q.
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Yang, Jian, Xiaojuan Ban, and Chunxiao Xing. "Using Greedy Random Adaptive Procedure to Solve the User Selection Problem in Mobile Crowdsourcing." Sensors 19, no. 14 (July 18, 2019): 3158. http://dx.doi.org/10.3390/s19143158.
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With the rapid development of mobile networks and smart terminals, mobile crowdsourcing has aroused the interest of relevant scholars and industries. In this paper, we propose a new solution to the problem of user selection in mobile crowdsourcing system. The existing user selection schemes mainly include: (1) find a subset of users to maximize crowdsourcing quality under a given budget constraint; (2) find a subset of users to minimize cost while meeting minimum crowdsourcing quality requirement. However, these solutions have deficiencies in selecting users to maximize the quality of service of the task and minimize costs. Inspired by the marginalism principle in economics, we wish to select a new user only when the marginal gain of the newly joined user is higher than the cost of payment and the marginal cost associated with integration. We modeled the scheme as a marginalism problem of mobile crowdsourcing user selection (MCUS-marginalism). We rigorously prove the MCUS-marginalism problem to be NP-hard, and propose a greedy random adaptive procedure with annealing randomness (GRASP-AR) to achieve maximize the gain and minimize the cost of the task. The effectiveness and efficiency of our proposed approaches are clearly verified by a large scale of experimental evaluations on both real-world and synthetic data sets.
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Zink, Nicolas, Wiebke Bensmann, Larissa Arning, Lorenza S. Colzato, Ann-Kathrin Stock, and Christian Beste. "The Role of DRD1 and DRD2 Receptors for Response Selection Under Varying Complexity Levels: Implications for Metacontrol Processes." International Journal of Neuropsychopharmacology 22, no. 12 (May 23, 2019): 747–53. http://dx.doi.org/10.1093/ijnp/pyz024.
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Abstract Background Highly complex tasks generally benefit from increases in cognitive control, which has been linked to dopamine. Yet, the same amount of control may actually be detrimental in tasks with low complexity so that the task-dependent allocation of cognitive control resources (also known as “metacontrol”) is key to expedient and adaptive behavior in various contexts. Methods Given that dopamine D1 and D2 receptors have been suggested to exert opposing effects on cognitive control, we investigated the impact of 2 single nucleotide polymorphisms in the DRD1 (rs4532) and DRD2 (rs6277) genes on metacontrol in 195 healthy young adults. Subjects performed 2 consecutive tasks that differed in their demand for control (starting with the less complex task and then performing a more complex task rule). Results We found carriers of the DRD1 rs4532 G allele to outperform noncarriers in case of high control requirements (i.e., reveal a better response accuracy), but not in case of low control requirements. This was confirmed by Bayesian analyses. No effects of DRD2 rs6277 genotype on either task were evident, again confirmed by Bayesian analyses. Conclusions Our findings suggest that higher DRD1 receptor efficiency improves performance during high, but not low, control requirements, probably by promoting a “D1 state,” which is characterized by highly stable task set representations. The null findings for DRD2 signaling might be explained by the fact that the “D2 state” is thought to enhance flexible switching between task set representations when our task only featured 1 task set at any given time.
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Yan, Jiangqiao, Yue Zhang, Zhonghan Chang, Tengfei Zhang, Menglong Yan, Wenhui Diao, Hongqi Wang, and Xian Sun. "FAS-Net: Construct Effective Features Adaptively for Multi-Scale Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 12573–80. http://dx.doi.org/10.1609/aaai.v34i07.6947.
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Feature pyramid is the mainstream method for multi-scale object detection. In most detectors with feature pyramid, each proposal is predicted based on feature grids pooled from only one feature level, which is assigned heuristically. Recent studies report that the feature representation extracted using this method is sub-optimal, since they ignore the valid information exists on other unselected layers of the feature pyramid. To address this issue, researchers present to fuse valid information across all feature levels. However, these methods can be further improved: the feature fusion strategies, which use common operation (element-wise max or sum) in most detectors, should be replaced by a more flexible way. In this work, a novel method called feature adaptive selection subnetwork (FAS-Net) is proposed to construct effective features for detecting objects of different scales. Particularly, its adaption consists of two level: global attention and local adaptive selection. First, we model the global context of each feature map with global attention based feature selection module (GAFSM), which can strengthen the effective features across each layer adaptively. Then we extract the features of each region of interest (RoI) on the entire feature pyramid to construct a RoI feature pyramid. Finally, the RoI feature pyramid is sent to the feature adaptive selection module (FASM) to integrate the strengthened features according to the input adaptively. Our FAS-Net can be easily extended to other two-stage object detectors with feature pyramid, and supports to analyze the importance of different feature levels for multi-scale objects quantitatively. Besides, FAS-Net can also be further applied to instance segmentation task and get consistent improvements. Experiments on PASCAL07/12 and MSCOCO17 demonstrate the effectiveness and generalization of the proposed method.
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Hu, Chunyang, Meng Xu, and Kao-Shing Hwang. "An adaptive cooperation with reinforcement learning for robot soccer games." International Journal of Advanced Robotic Systems 17, no. 3 (May 1, 2020): 172988142092132. http://dx.doi.org/10.1177/1729881420921324.
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A strategy system with self-improvement and self-learning abilities for robot soccer system has been developed in this study. This work focuses on the cooperation strategy for the task assignment and develops an adaptive cooperation method for this system. This method was inspired by reinforcement learning (RL) and game theory. The developed system includes two subsystems: the task assignment system and the RL system. The task assignment system assigns one of the four roles, Attacker, Helper, Defender, and Goalkeeper, to each separate robot with the same physical and mechanical conditions to achieve cooperation. The assigned role to robots considers the situation in the game field. Each role has its own behaviors and tasks. The RL helps the Helper and Defender to improve the ability of their policy selection on the real-time confrontation. The RL system can not only learn to figure up how Helper helps its teammates to form an attack or a defense type but also learn to stand a proper defensive strategy. Some experiments on FIRE simulator and standard platform have been demonstrated that the proposed method performs better than the competitors.
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Wang, Juan, and Di Li. "Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing." Sensors 18, no. 8 (August 1, 2018): 2509. http://dx.doi.org/10.3390/s18082509.
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In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system. A method based on Computing Mode Selection (CMS) and execution sequences based on the task priority (ASTP) is proposed in this paper. First, a CMS module is designed in the SDN controller and then, after operating the CMS algorithm, each task obtains an optimal computing mode. Second, the task priorities can be calculated according to their real-time performance and calculated amount. According to the task priority, the SDN controller sends a flow table to the SDN switch to complete the task transmission. In other words, the higher the task priority is, the earlier the fog computing service is obtained. Finally, a series of experiments and simulations are performed to evaluate the performance of the proposed method. The results show that our method can achieve real-time performance and high reliability in IIoT.
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Atanasova, Margarita, and Anna Malinova. "TRANSFORMING CONCUR TASK TREES MODEL INTO AN ABSTRACT USER INTERFACE." CBU International Conference Proceedings 5 (September 24, 2017): 1036–41. http://dx.doi.org/10.12955/cbup.v5.1067.
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Business applications are difficult to use for the average user. An adaptive user interface improves employees’ productivity and is presented as a solution to this problem. However, developing user interfaces that are adapted to the needs and culture of the enterprise is time-consuming and expensive. We developed a software prototype for generating adaptive user interfaces that makes this process less time-consuming and more efficient. We propose an extension to the Cameleon Reference Framework project by Information Society Technologies, on the implementation level by adding an additional step for defining the Area of Business Operations. That way the prototype can extract business tasks for the selected industry therefore, presenting to the developer a more intelligent selection of predefined tasks. In this article, we also present a programming approach for transforming a task model, as defined by the ConcurTaskTrees notation, into an abstract user interface.
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Salden, Ron J. C. M., Fred Paas, and Jeroen J. G. van Merriënboer. "Personalised adaptive task selection in air traffic control: Effects on training efficiency and transfer." Learning and Instruction 16, no. 4 (August 2006): 350–62. http://dx.doi.org/10.1016/j.learninstruc.2006.07.007.
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Mihajlov Carević, Miroslava, Milena Petrović, Nebojša Denić, and Aleksandra Mitrović. "Computing Support in Statistical Evaluation of Mathematics Teaching Effectiveness: Development of Students’ Constructive Thinking." Technium: Romanian Journal of Applied Sciences and Technology 2, no. 4 (June 15, 2020): 109–15. http://dx.doi.org/10.47577/technium.v2i4.996.
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One of the main tasks in teaching mathematics is to develop students’ constructive thinking. In order to effectively accomplish this task, it is necessary to make a good selection of instructional materials and teaching aids. In order to make good selection and improve the teaching of mathematics, it is, also, necessary to include a statistical analysis of the certain factors’ impact that affect mathematics curriculum. For the purpose of this research, we used the software computational approach ANFIS (adaptive neuro fuzzy inference system) to determine the qualitative impact of several factors on improving students’ ability to create constructive thinking.
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Mückschel, Moritz, Elena Eggert, Astrid Prochnow, and Christian Beste. "Learning Experience Reverses Catecholaminergic Effects on Adaptive Behavior." International Journal of Neuropsychopharmacology 23, no. 1 (November 8, 2019): 12–19. http://dx.doi.org/10.1093/ijnp/pyz058.
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Abstract Background Catecholamines are important for cognitive control and the ability to adapt behavior (e.g., after response errors). A prominent drug that modulates the catecholaminergic system is methylphenidate. On the basis of theoretical consideration, we propose that the effects of methylphenidate on behavioral adaptation depend on prior learning experience. Methods In a double-blind, randomized, placebo-controlled crossover study design, we examined the effect of methylphenidate (0.25 mg/kg) on post error behavioral adaptation processes in a group of n = 43 healthy young adults. Behavioral adaptation processes were examined in a working memory, modulated response selection task. The focus of the analysis was on order effects within the crossover study design to evaluate effects of prior learning/task experience. Results The effect of methylphenidate/placebo on post-error behavioral adaptation processes reverses depending on prior task experience. When there was no prior experience with the task, methylphenidate increased post-error slowing and thus intensified behavioral adaptation processes. However, when there was prior task experience, (i.e., when the placebo session was conducted first in the crossover design), methylphenidate even decreased post-error slowing and behavioral adaptation. Effect sizes were large and the power of the observed effects was higher than 95%. Conclusions The data suggest that catecholaminergic effects on cognitive control functions vary as a function of prior learning/task experience. The data establish a close link between learning/task familiarization and catecholaminergic effects for executive functions, which has not yet been studied, to our knowledge, but is of considerable clinical relevance. Theoretical implications are discussed.
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Ding, Lizhong, Yong Liu, Shizhong Liao, Yu Li, Peng Yang, Yijie Pan, Chao Huang, Ling Shao, and Xin Gao. "Approximate Kernel Selection with Strong Approximate Consistency." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3462–69. http://dx.doi.org/10.1609/aaai.v33i01.33013462.
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Kernel selection is fundamental to the generalization performance of kernel-based learning algorithms. Approximate kernel selection is an efficient kernel selection approach that exploits the convergence property of the kernel selection criteria and the computational virtue of kernel matrix approximation. The convergence property is measured by the notion of approximate consistency. For the existing Nyström approximations, whose sampling distributions are independent of the specific learning task at hand, it is difficult to establish the strong approximate consistency. They mainly focus on the quality of the low-rank matrix approximation, rather than the performance of the kernel selection criterion used in conjunction with the approximate matrix. In this paper, we propose a novel Nyström approximate kernel selection algorithm by customizing a criterion-driven adaptive sampling distribution for the Nyström approximation, which adaptively reduces the error between the approximate and accurate criteria. We theoretically derive the strong approximate consistency of the proposed Nyström approximate kernel selection algorithm. Finally, we empirically evaluate the approximate consistency of our algorithm as compared to state-of-the-art methods.
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Cheng, Fang Qi, and Fei Fan Ye. "Collaborative Manufacturing Unit Selection Using a Sorting Adaptive Genetic Algorithm in Networked Environment." Key Engineering Materials 407-408 (February 2009): 230–33. http://dx.doi.org/10.4028/www.scientific.net/kem.407-408.230.
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In order to solve the collaborative manufacturing unit (CMU) selection problem in the networked cooperative manufacturing environment, a sorting adaptive genetic algorithm is proposed. To obtain the optimal executive manufacturing process, the objective function is constructed considering manufacturing cost and product load rate of candidate CMUs under time-sequence constraint. The embedded subtask scheduling procedure in sorting adaptive genetic algorithm is used to ascertain the penalty cost for the tardiness of the task. Finally, a case study is implemented to verify the feasibility of the proposed approach. The results show that the proposed model and algorithm can obtain satisfactory solutions.
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Shubin, I. Yu, G. G. Chetverykov, V. A. Liashyk, and N. A. Shanidze. "Adaptive Testing Of Knowledge by Methods of Logical Networks." Bionics of Intelligence 2, no. 95 (December 2, 2020): 82–89. http://dx.doi.org/10.30837/bi.2020.2(95).11.
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Adaptive test control is a computerized system of scientifically based verification and evaluation of learning outcomes, which is highly effective by optimizing the procedures for generating, presenting and evaluating the results of adaptive tests, based on methods of building and optimizing logical networks. Algorithms for selection and presentation of tasks are based on the principle of feedback, when the correct answer of the subject of training is the next difficult task, and the wrong answer causes the presentation of the next easier task than that to which the subject of training the wrong answer was given. It is also possible to ask additional questions on topics that the subject does not know very well to clarify the level of knowledge in these areas. The choice of testing algorithms is currently actually limited by the forms of presentation of test tasks and algorithms for evaluating test results. Achieving higher results and increasing the motivation to learn is ultimately the main goal of testing knowledge. To determine the basic algorithm, it is necessary to provide a scenario of the system. It is based on the model of taking the exam by a teacher as a model of adaptive testing. This choice of the scenario of the system is due to the fact that, firstly, this procedure is historically well formalized, and secondly, when designing tests, their developer must rely on common, known and used methods with minimal modification.
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Nawrocki, Piotr, and Bartlomiej Sniezynski. "Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning." Wireless Personal Communications 115, no. 3 (August 13, 2020): 1839–67. http://dx.doi.org/10.1007/s11277-020-07657-9.
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AbstractIn this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.
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van der Werf, Rintse, Geke Hootsen, and Anne Vermeer. "Automated User-Centred Task Selection and Input Modification in Language Learning." ITL - International Journal of Applied Linguistics 155 (2008): 1–21. http://dx.doi.org/10.2143/itl.155.0.2032362.
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Abstract This article presents the results of a CALL-experiment in which reading material is selected dynamically, based on the “fit” between the vocabulary proficiency of individual students and the relative difficulty of texts. The texts were analysed, selected and presented online, together with a personalized electronic dictionary with words that were assumed to be unknown. In a pre-test – treatment – post-test design in which 32 Dutch L2-students took part, the vocabulary learned implicitly while reading the texts was measured. The relation between user-initiated noticing and word retention was also examined. We found an average word-learning improvement of 10.8%. On the basis of the non-significant differences between various proficiency groups, we concluded that the method we propose for the automated adaptive selection of reading texts ensures that learners of different proficiency levels receive linguistic input that is best fitted to their abilities. Using frequency information for both automated analysis of texts and the compilation of a personalized dictionary has great potential for more user-centred task selection and guidance. We also found a relation between user-initiated dictionary use and word retention, which was strong for the lowest proficiency level. With respect to the words looked up in the dictionary, a strong correlation was found between noticing and retention.
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van der Werf, Rintse, Geke Hootsen, and Anne Vermeer. "Automated User-Centred Task Selection and Input Modification in Language Learning." ITL - International Journal of Applied Linguistics 155 (January 1, 2008): 1–21. http://dx.doi.org/10.1075/itl.155.01wer.
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This article presents the results of a CALL-experiment in which reading material is selected dynamically, based on the “fit” between the vocabulary proficiency of individual students and the relative difficulty of texts. The texts were analysed, selected and presented online, together with a personalized electronic dictionary with words that were assumed to be unknown. In a pre-test – treatment – post-test design in which 32 Dutch L2-students took part, the vocabulary learned implicitly while reading the texts was measured. The relation between user-initiated noticing and word retention was also examined. We found an average word-learning improvement of 10.8%. On the basis of the non-significant differences between various proficiency groups, we concluded that the method we propose for the automated adaptive selection of reading texts ensures that learners of different proficiency levels receive linguistic input that is best fitted to their abilities. Using frequency information for both automated analysis of texts and the compilation of a personalized dictionary has great potential for more user-centred task selection and guidance. We also found a relation between user-initiated dictionary use and word retention, which was strong for the lowest proficiency level. With respect to the words looked up in the dictionary, a strong correlation was found between noticing and retention.
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Remington, Roger W., Joyce M. G. Vromen, Stefanie I. Becker, Oliver Baumann, and Jason B. Mattingley. "The Role of Frontoparietal Cortex across the Functional Stages of Visual Search." Journal of Cognitive Neuroscience 33, no. 1 (January 2021): 63–76. http://dx.doi.org/10.1162/jocn_a_01632.
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Areas in frontoparietal cortex have been shown to be active in a range of cognitive tasks and have been proposed to play a key role in goal-driven activities (Dosenbach, N. U. F., Fair, D. A., Miezin, F. M., Cohen, A. L., Wenger, K. K., Dosenbach, R. A. T., et al. Distinct brain networks for adaptive and stable task control in humans. Proceedings of the National Academy of Sciences, U.S.A., 104, 11073–11078, 2007; Duncan, J. The multiple-demand (MD) system of the primate brain: Mental programs for intelligent behavior. Trends in Cognitive Sciences, 14, 172–179, 2010). Here, we examine the role this frontoparietal system plays in visual search. Visual search, like many complex tasks, consists of a sequence of operations: target selection, stimulus–response (SR) mapping, and response execution. We independently manipulated the difficulty of target selection and SR mapping in a novel visual search task that involved identical stimulus displays. Enhanced activity was observed in areas of frontal and parietal cortex during both difficult target selection and SR mapping. In addition, anterior insula and ACC showed preferential representation of SR-stage information, whereas the medial frontal gyrus, precuneus, and inferior parietal sulcus showed preferential representation of target selection-stage information. A connectivity analysis revealed dissociable neural circuits underlying visual search. We hypothesize that these circuits regulate distinct mental operations associated with the allocation of spatial attention, stimulus decisions, shifts of task set from selection to SR mapping, and SR mapping. Taken together, the results show frontoparietal involvement in all stages of visual search and a specialization with respect to cognitive operations.
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Alavash, Mohsen, Sarah Tune, and Jonas Obleser. "Modular reconfiguration of an auditory control brain network supports adaptive listening behavior." Proceedings of the National Academy of Sciences 116, no. 2 (December 26, 2018): 660–69. http://dx.doi.org/10.1073/pnas.1815321116.
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Speech comprehension in noisy, multitalker situations poses a challenge. Successful behavioral adaptation to a listening challenge often requires stronger engagement of auditory spatial attention and context-dependent semantic predictions. Human listeners differ substantially in the degree to which they adapt behaviorally and can listen successfully under such circumstances. How cortical networks embody this adaptation, particularly at the individual level, is currently unknown. We here explain this adaptation from reconfiguration of brain networks for a challenging listening task (i.e., a linguistic variant of the Posner paradigm with concurrent speech) in an age-varying sample of n = 49 healthy adults undergoing resting-state and task fMRI. We here provide evidence for the hypothesis that more successful listeners exhibit stronger task-specific reconfiguration (hence, better adaptation) of brain networks. From rest to task, brain networks become reconfigured toward more localized cortical processing characterized by higher topological segregation. This reconfiguration is dominated by the functional division of an auditory and a cingulo-opercular module and the emergence of a conjoined auditory and ventral attention module along bilateral middle and posterior temporal cortices. Supporting our hypothesis, the degree to which modularity of this frontotemporal auditory control network is increased relative to resting state predicts individuals’ listening success in states of divided and selective attention. Our findings elucidate how fine-tuned cortical communication dynamics shape selection and comprehension of speech. Our results highlight modularity of the auditory control network as a key organizational principle in cortical implementation of auditory spatial attention in challenging listening situations.
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Stafford, Tom, and Kevin N. Gurney. "Biologically constrained action selection improves cognitive control in a model of the Stroop task." Philosophical Transactions of the Royal Society B: Biological Sciences 362, no. 1485 (April 11, 2007): 1671–84. http://dx.doi.org/10.1098/rstb.2007.2060.
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The Stroop task is a paradigmatic psychological task for investigating stimulus conflict and the effect this has on response selection. The model of Cohen et al. (Cohen et al . 1990 Psychol. Rev. 97 , 332–361) has hitherto provided the best account of performance in the Stroop task, but there remains certain key data that it fails to match. We show that this failure is due to the mechanism used to perform final response selection—one based on the diffusion model of choice behaviour (Ratcliff 1978 Psychol. Rev. 85 , 59–108). We adapt the model to use a selection mechanism which is based on the putative human locus of final response selection, the basal ganglia/thalamo-cortical complex (Redgrave et al. 1999 Neuroscience 89 , 1009–1023). This improves the match to the core human data and, additionally, makes it possible for the model to accommodate, in a principled way, additional mechanisms of cognitive control that enable better fits to the data. This work prompts a critique of the diffusion model as a mechanism of response selection, and the features that any response mechanism must possess to provide adaptive action selection. We conclude that the consideration of biologically constrained solutions to the action selection problem is vital to the understanding and improvement of cognitive models of response selection.
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Norrie, Lauren, and Roderick Murray-Smith. "Investigating UI Displacements in an Adaptive Mobile Homescreen." International Journal of Mobile Human Computer Interaction 8, no. 3 (July 2016): 1–17. http://dx.doi.org/10.4018/ijmhci.2016070101.oa.
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The authors present a system that adapts application shortcuts (apps) on the homescreen of an Android smartphone, and investigate the effect of UI displacements that are caused by the choice of adaptive model and the order of apps in the homescreen layout. They define UI displacements to be the distance that items move between adaptations, and they use this as a measure of stability. An experiment with 12 participants is performed to evaluate the impact of UI displacements on the homescreen. To make the distribution of apps in the experiment task less contrived, naturally generated data from a pilot study is used. The authors’ results show that selection time is correlated to the magnitude of the previous UI displacement. Additionally, selection time and subjective rating improve significantly when the model is easy to understand and an alphabetical order is used, conditions that increase stability. However, rank order is preferred when the model updates frequently and is less easy to understand. The authors present their approach to adapting apps on the homescreen, and initial insights into UI displacements.
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Asante, Curtis Oware, Amy Chu, Mark Fisher, Leora Benson, Asim Beg, Peter Scheiffele, and John Martin. "Cortical Control of Adaptive Locomotion in Wild-Type Mice and Mutant Mice Lacking the Ephrin-Eph Effector Protein α2-Chimaerin." Journal of Neurophysiology 104, no. 6 (December 2010): 3189–202. http://dx.doi.org/10.1152/jn.00671.2010.
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In voluntary control, supraspinal motor systems select the appropriate response and plan movement mechanics to match task constraints. Spinal circuits translate supraspinal drive into action. We studied the interplay between motor cortex (M1) and spinal circuits during voluntary movements in wild-type (WT) mice and mice lacking the α2-chimaerin gene (Chn1−/−), necessary for ephrinB3-EphA4 signaling. Chn1−/− mice have aberrant bilateral corticospinal systems, aberrant bilateral-projecting spinal interneurons, and disordered voluntary control because they express a hopping gait, which may be akin to mirror movements. We addressed three issues. First, we determined the role of the corticospinal system in adaptive control. We trained mice to step over obstacles during treadmill locomotion. We compared performance before and after bilateral M1 ablation. WT mice adaptively modified their trajectory to step over obstacles, and M1 ablation increased substantially the incidence of errant steps over the obstacle. Chn1−/− mice randomly stepped or hopped during unobstructed locomotion but hopped over the obstacle. Bilateral M1 ablation eliminated this obstacle-dependent hop selection and increased forelimb obstacle contact errors. Second, we characterized the laterality of corticospinal action in Chn1−/− mice using pseudorabies virus retrograde transneuronal transport and intracortical microstimulation. We showed bilateral connections between M1 and forelimb muscles in Chn1−/− and unilateral connections in WT mice. Third, in Chn1−/− mice, we studied adaptive responses before and after unilateral M1 ablation. We identified a more important role for contralateral than ipsilateral M1 in hopping over the obstacle. Our findings suggest an important role for M1 in the mouse in moment-to-moment adaptive control, and further, using Chn1−/− mice, a role in mediating task-dependent selection of mirror-like hopping movements over the obstacle. Our findings also stress the importance of subcortical control during adaptive locomotion because key features of the trajectory remained largely intact after M1 ablation.
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Stasiak-Cieślak, Beata. "Selection procedure for adaptation devices helping car by driver with disability." WUT Journal of Transportation Engineering 121 (June 1, 2018): 363–72. http://dx.doi.org/10.5604/01.3001.0014.4618.
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The vehicle's equipment in specialized adaptations for drivers with disabilities requires knowledge of many issues, both anatomical, technical, procedural and psychological. In Poland, there are not many experts who have experience in comprehensive customer service with specific needs while driving. The article presents assumptions used to develop an algorithm for the selection of adaptive devices. The algorithm has the form of an expert database and contains: a description of possible dysfunctions, rules for the selection of adaptive devices and offers of European companies regarding specific adaptive solutions. The algorithm developed the knowledge and practice in the field of functional tests carried out in the Automotive Service Centre for the Disabled at the Institute of Motor Transport and serving people with difficult motor dysfunctions. The continuous development of adaptive instrumentation makes it convenient conditions for realizing the important need of mobility of people with disabilities. However, the process by which a disabled person has to pass to get a converted car is often complicated and time-consuming. Adaptation of a car is a very difficult task that requires specialist knowledge and experience. Presentation of existing procedures: from a simple Adaptation Selection Module to a more complicated algorithm, it should pay attention to the need to implement such solutions, among others for the needs of driving instructors or certifying doctors.
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Schumacher, Eric H., Puni A. Elston, and Mark D'Esposito. "Neural Evidence for Representation-Specific Response Selection." Journal of Cognitive Neuroscience 15, no. 8 (November 1, 2003): 1111–21. http://dx.doi.org/10.1162/089892903322598085.
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Response selection is the mental process of choosing representations for appropriate motor behaviors given particular environmental stimuli and one's current task situation and goals. Many cognitive theories of response selection postulate a unitary process. That is, one central response-selection mechanism chooses appropriate responses in most, if not all, task situations. However, neuroscience research shows that neural processing is often localized based on the type of information processed. Our current experiments investigate whether response selection is unitary or stimulus specific by manipulating response-selection difficulty in two functional magnetic resonance imaging experiments using spatial and nonspatial stimuli. The same participants were used in both experiments. We found spatial response selection involves the right prefrontal cortex, the bilateral premotor cortex, and the dorsal parietal cortical regions (precuneus and superior parietal lobule). Nonspatial response selection, conversely, involves the left prefrontal cortex and the more ventral posterior cortical regions (left middle temporal gyrus, left inferior parietal lobule, and right extrastriate cortex). Our brain activation data suggest a cognitive model for response selection in which different brain networks mediate the choice of appropriate responses for different types of stimuli. This model is consistent with behavioral research suggesting that responseselection processing may be more flexible and adaptive than originally proposed.
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van Ede, Freek, Alexander G. Board, and Anna C. Nobre. "Goal-directed and stimulus-driven selection of internal representations." Proceedings of the National Academy of Sciences 117, no. 39 (September 14, 2020): 24590–98. http://dx.doi.org/10.1073/pnas.2013432117.
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Adaptive behavior relies on the selection of relevant sensory information from both the external environment and internal memory representations. In understanding external selection, a classic distinction is made between voluntary (goal-directed) and involuntary (stimulus-driven) guidance of attention. We have developed a task—the anti-retrocue task—to separate and examine voluntary and involuntary guidance of attention to internal representations in visual working memory. We show that both voluntary and involuntary factors influence memory performance but do so in distinct ways. Moreover, by tracking gaze biases linked to attentional focusing in memory, we provide direct evidence for an involuntary “retro-capture” effect whereby external stimuli involuntarily trigger the selection of feature-matching internal representations. We show that stimulus-driven and goal-directed influences compete for selection in memory, and that the balance of this competition—as reflected in oculomotor signatures of internal attention—predicts the quality of ensuing memory-guided behavior. Thus, goal-directed and stimulus-driven factors together determine the fate not only of perception, but also of internal representations in working memory.
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Shereen A. El-aal, Rabie A. Ramadan, and Neveen I. Ghali. "Classification of EEG Signals for Motor Imagery based on Mutual Information and Adaptive Neuro Fuzzy Inference System." International Journal of System Dynamics Applications 5, no. 4 (October 2016): 64–82. http://dx.doi.org/10.4018/ijsda.2016100104.
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Electroencephalogram (EEG) signals based Brain Computer Interface (BCI) is employed to help disabled people to interact better with the environment. EEG signals are recorded through BCI system to translate it to control commands. There are a large body of literature targeting EEG feature extraction and classification for Motor Imagery tasks. Motor imagery task have several features can be extracted to use in classification. However, using more features consume running time and using irrelevant and redundant features affect the performance of the used classifier. This paper is dedicated to extracting the best feature vector for motor imagery task. This work suggests two feature selection methods based on Mutual Information (MI) including Minimum Redundancy Maximal Relevance (MRMR) and maximal Relevance (MaxRel). Adaptive Neuro Fuzzy Inference System (ANFIS) classifier with Subtractive clustering method is utilized for EEG signals classifications. The suggested methods are applied to BCI Competition III dataset IVa and IVb and BCI Competition II dataset III.
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Hinault, Thomas, and Patrick Lemaire. "Adaptive Strategic Variations in Human Cognition Across the Life Span." Journal of Education and Training 3, no. 1 (February 2, 2016): 189. http://dx.doi.org/10.5296/jet.v3i1.8967.
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This paper provides an overview of how a strategy perspective fruitfully contributes to our understanding of psychological adaptation in problem-solving tasks, as well as how strategic adaptation develops across lifespan. Indeed, people do not use a single strategy to solve various problems, nor do their strategies remain the same across their lifespan. Problem-solving performance is determined by efficient strategy selection and execution, and strategy effectiveness is modulated by characteristics of problems, strategies, situations, and participants. Multiple strategy use help participants to obtain better performance through strategic adaptations. Strategic adaptations can be defined as participants’ calibrations of how they accomplish cognitive tasks as a function of different task parameters. Moreover, this review consider how strategic adaptation mechanisms are implemented during childhood, as well as aging effects on the ability to select and execute strategies adaptively given environmental constraints. Third, the role of working memory capacity and executive processes in strategy use and in age-related changes in strategy adaptativeness are discussed. This review illustrates developmental changes of strategic adaptation during childhood and adulthood with findings from a variety of cognitive domains, including decision making and arithmetic problem solving.
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McGarry, Kathleen, Ericka Rovira, and Raja Parasuraman. "Adaptive Change in the Type of Automation Support Reduces the Cost of Imperfect Decision Aids in a Simulated Battlefield Engagement Task." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 3 (September 2005): 307–11. http://dx.doi.org/10.1177/154193120504900320.
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Automation that is meant to aid the human operator may actually be detrimental to performance, particularly if faulty decision recommendations are provided (decision automation), as opposed to prioritized or integrated information advisories that are incorrect (information automation). Because automation can be imperfect, operator over-reliance on decision automation can degrade performance. The present study examined whether temporary adaptive changes in the type and level of automation—-between decision and information automation, or between decision automation and manual performance—–could mitigate the cost of automation imperfection in a combat engagement selection task. Twelve participants were provided with two types of automation (decision and information) and also performed the task manually. In three conditions, the type of automation was alternated during performance of the task over three blocks of trials. In all three conditions, decision automation was provided in the first and third blocks of the task, with the middle block requiring the use of decision automation, information automation, or manual performance. The accuracy of engagement decisions improved in the third block with decision automation when it was preceded by a temporary adaptive change to information automation. No such improvement occurred when decision automation was used throughout the task or when the adaptive change involved a temporary return to manual performance. This suggests that providing the user with short periods of information automation can help mitigate some of the costs of imperfect decision automation by keeping the operator in the decision-making loop.
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NGUYEN, Thanh-Tam, Son-Thai LE, and Van-Thuy LE. "Adaptive Hyperparameter for Face Recognition." International Journal of Innovative Technology and Exploring Engineering 10, no. 2 (January 10, 2021): 116–19. http://dx.doi.org/10.35940/ijitee.c8409.0110321.
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One of the widely used prominent biometric techniques for identity authentication is Face Recognition. It plays an essential role in many areas, such as daily life, public security, finance, the military, and the smart school. The facial recognition task is identifying or verifying the identity of a person base on their face. The first step is face detection, which detects and locates human faces in images and videos. The face match process then finds an identity of the detected face. In recent years there have been many face recognition systems improving the performance based on deep learning models. Deep learning learns representations of the face based on multiple processing layers with multiple levels of feature extraction. This approach has made sufficient improvement in face recognition since 2014, launched by the breakthroughs of DeepFace and DeepID. However, finding a way to choose the best hyperparameters remains an open question. In this paper, we introduce a method for adaptive hyperparameters selection to improve recognition accuracy. The proposed method achieves improvements on three datasets.
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Lefebvre, Louis, and Pascal Carlier. "Differences in Individual Learning Between Group-Foraging and Territorial Zenaida Doves." Behaviour 133, no. 15-16 (1996): 1197–207. http://dx.doi.org/10.1163/156853996x00369.
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AbstractAdaptive views of learning predict that natural selection should lead to differences in specialized learning abilities between animals that face different ecological pressures. Group-living is thought to favour social learning, but previous comparative work suggests that differences between gregarious feral pigeons (Columba livia) and territorial Zenaida doves (Zenaida aurita) exceed the specialized effect on social tasks predicted by the adaptive hypothesis. In this paper, we show that group-foraging Zenaida doves from Barbados learn an individual shaping task more quickly than territorial Zenaida doves from a site 9 km away. These results suggest that the scramble competition associated with group-foraging favours several types of leaming, both social and non-social, and that its effects are more wide-ranging than previously thought. Since genetic isolation between Zenaida dove populations is highly unlikely, the results also suggest that differences in foraging ecology may lead to different learned responses to local reward contingencies as well as natural selection for different genotypes affecting learning. In some cases, the standard comparative prediction of ecologically-correlated learning differences may therefore not distinguish between adaptive specialization and general process theories.
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Chadderdon, George L., and Olaf Sporns. "A Large-scale Neurocomputational Model of Task-oriented Behavior Selection and Working Memory in Prefrontal Cortex." Journal of Cognitive Neuroscience 18, no. 2 (February 1, 2006): 242–57. http://dx.doi.org/10.1162/jocn.2006.18.2.242.
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The prefrontal cortex (PFC) is crucially involved in the executive component of working memory, representation of task state, and behavior selection. This article presents a large-scale computational model of the PFC and associated brain regions designed to investigate the mechanisms by which working memory and task state interact to select adaptive behaviors from a behavioral repertoire. The model consists of multiple brain regions containing neuronal populations with realistic physiological and anatomical properties, including extrastriate visual cortical regions, the inferotemporal cortex, the PFC, the striatum, and midbrain dopamine (DA) neurons. The onset of a delayed match-to-sample or delayed nonmatch-to-sample task triggers tonic DA release in the PFC causing a switch into a persistent, stimulus-insensitive dynamic state that promotes the maintenance of stimulus representations within prefrontal networks. Other modeled prefrontal and striatal units select cognitive acceptance or rejection behaviors according to which task is active and whether prefrontal working memory representations match the current stimulus. Working memory task performance and memory fields of prefrontal delay units are degraded by extreme elevation or depletion of tonic DA levels. Analyses of cellular and synaptic activity suggest that hyponormal DA levels result in increased prefrontal activation, whereas hypernormal DA levels lead to decreased activation. Our simulation results suggest a range of predictions for behavioral, single-cell, and neuroimaging response data under the proposed task set and under manipulations of DA concentration.
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Wang, Yin-Tien, Zhi-Jun You, and Chia-Hsing Chen. "AIN-Based Action Selection Mechanism for Soccer Robot Systems." Journal of Control Science and Engineering 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/896310.
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Role and action selections are two major procedures of the game strategy for multiple robots playing the soccer game. In role-select procedure, a formation is planned for the soccer team, and a role is assigned to each individual robot. In action-select procedure, each robot executes an action provided by an action selection mechanism to fulfill its role playing. The role-select procedure was often designed efficiently by using the geometry approach. However, the action-select procedure developed based on geometry approach will become a very complex task. In this paper, a novel action-select algorithm for soccer robots is proposed by using the concepts of artificial immune network (AIN). This AIN-based action-select provides an efficient and robust algorithm for robot role selection. Meanwhile, a reinforcement learning mechanism is applied in the proposed algorithm to enhance the response of the adaptive immune system. Simulation and experiment are carried out to verify the proposed AIN-based algorithm, and the results show that the proposed algorithm provides an efficient and applicable algorithm for mobile robots to play soccer game.
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LIU, YABO, JIANHUA YANG, and ZHAOHUI WU. "UBIQUITOUS AND COOPERATIVE NETWORK ROBOT SYSTEM WITHIN A SERVICE FRAMEWORK." International Journal of Humanoid Robotics 08, no. 01 (March 2011): 147–67. http://dx.doi.org/10.1142/s021984361100237x.
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Network robot system (NRS) is a new concept that integrates physical autonomous robots, environmental sensors, and human–robot interactions through network-based cooperation. The aim of this paper is to provide a ubiquitous and cooperative service framework for NRS. We first present foundational concepts of semantic map and service definition for the framework. Then, in order to generate feasible service configurations to fulfill tasks, we propose service configuration and reconfiguration algorithms, which dynamically search the appropriate service configurations for different tasks. Additionally, we put forward a service reasoning and enabling process to tackle the service unavailable problems. A cost evaluation function for service configuration is also proposed to facilitate the selection of suitable configurations. We tested and evaluated the framework in both simulation system and physical environment. Specifically, by separately varying the parameter settings, system performance was measured in three aspects: the success rate of tasks, the average waiting time per task, and the average cost per task. The experiment results indicate that the versatile service framework provides self-adaptive capability and utilizes available resources efficiently under a range of different scenarios.
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Lv, Li. "Research on Optimization Selection Algorithm of Track and Field Based on Adaptive Wireless Sensor Network Node Speed." Applied Mechanics and Materials 556-562 (May 2014): 5690–94. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5690.
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By using the adaptive search algorithm of particle swarm layout, we optimize 3D space nodes of wireless network, and obtain the optimum mathematical model of particle swarm optimization distribution of nodes in wireless network. Using the tree structure and the wireless network hardware equipment we design algorithm the optimization platform of track and field technique. In order to verify the stability and reliability of platform running, we use the wireless network node division method and transmission time groups of control network to control athlete’s technical movement. And we use Java technology to calculate the athlete’s action to verify the stability. Through calculation, for the same optimization task, the time of adaptive search algorithm is short, and the search is less, which is a rapid and effective optimization algorithm.
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Chen, Shaopei, Ji Yang, Yong Li, and Jingfeng Yang. "Multiconstrained Network Intensive Vehicle Routing Adaptive Ant Colony Algorithm in the Context of Neural Network Analysis." Complexity 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8594792.
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Neural network models have recently made significant achievements in solving vehicle scheduling problems. Adaptive ant colony algorithm provides a new idea for neural networks to solve complex system problems of multiconstrained network intensive vehicle routing models. The pheromone in the path is changed by adjusting the volatile factors in the operation process adaptively. It effectively overcomes the tendency of the traditional ant colony algorithm to fall easily into the local optimal solution and slow convergence speed to search for the global optimal solution. The multiconstrained network intensive vehicle routing algorithm based on adaptive ant colony algorithm in this paper refers to the interaction between groups. Adaptive transfer and pheromone update strategies are introduced based on the traditional ant colony algorithm to optimize the selection, update, and coordination mechanisms of the algorithm further. Thus, the search task of the objective function for a feasible solution is completed by the search ants. Through the division and collaboration of different kinds of ants, pheromone adaptive strategy is combined with polymorphic ant colony algorithm. It can effectively overcome some disadvantages, such as premature stagnation, and has a theoretical significance to the study of large-scale multiconstrained vehicle routing problems in complex traffic network systems.