Relevant bibliographies by topics / Action algorithms

Academic literature on the topic 'Action algorithms'

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Published: 30 May 2022
Last updated: 31 May 2022

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Journal articles on the topic "Action algorithms"

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Fathi, Yahya, and Craig Tovey. "Affirmative action algorithms." Mathematical Programming 34, no. 3 (April 1986): 292–301. http://dx.doi.org/10.1007/bf01582232.

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Gite, Shilpa, and Himanshu Agrawal. "Early Prediction of Driver's Action Using Deep Neural Networks." International Journal of Information Retrieval Research 9, no. 2 (April 2019): 11–27. http://dx.doi.org/10.4018/ijirr.2019040102.

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Intelligent transportation systems (ITSs) are one of the most widely-discussed and researched topic across the world. The researchers have focused on the early prediction of a driver's movements before drivers actually perform actions, which might suggest a driver to take a corrective action while driving and thus, avoid the risk of an accident. This article presents an improved deep-learning technique to predict a driver's action before he performs that action, a few seconds in advance. This is considering both the inside context (of the driver) and the outside context (of the road), and fuses them together to anticipate the actions. To predict the driver's action accurately, the proposed work is inspired by recent developments in recurrent neural networks (RNN) with long short term memory (LSTM) algorithms. The performance merit of the proposed algorithm is compared with four other algorithms and the results suggest that the proposed algorithm outperforms the other algorithms using a range of performance metrics.
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Wu, Songjiao. "Image Recognition of Standard Actions in Sports Videos Based on Feature Fusion." Traitement du Signal 38, no. 6 (December 31, 2021): 1801–7. http://dx.doi.org/10.18280/ts.380624.

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Standard actions are crucial to sports training of athletes and daily exercise of ordinary people. There are two key issues in sports action recognition: the extraction of sports action features, and the classification of sports actions. The existing action recognition algorithms cannot work effectively on sports competitions, which feature high complexity, fine class granularity, and fast action speed. To solve the problem, this paper develops an image recognition method of standard actions in sports videos, which merges local and global features. Firstly, the authors combed through the functions and performance required for the recognition of standard actions of sports, and proposed an attention-based local feature extraction algorithm for the frames of sports match videos. Next, a sampling algorithm was developed based on time-space compression, and a standard sports action recognition algorithm was designed based on time-space feature fusion, with the aim to fuse the time-space features of the standard actions in sports match videos, and to overcome the underfitting problem of direct fusion of time-space features extracted by the attention mechanism. The workflow of these algorithms was explained in details. Experimental results confirm the effectiveness of our approach.
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Mudge, Michael E., and J. P. Killingbeck. "Microcomputer Algorithms: Action for Algebra." Mathematical Gazette 76, no. 476 (July 1992): 305. http://dx.doi.org/10.2307/3619164.

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Abdallah, S., and V. Lesser. "A Multiagent Reinforcement Learning Algorithm with Non-linear Dynamics." Journal of Artificial Intelligence Research 33 (December 17, 2008): 521–49. http://dx.doi.org/10.1613/jair.2628.

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Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Due to the complexity of the problem, the majority of the previously developed MARL algorithms assumed agents either had some knowledge of the underlying game (such as Nash equilibria) and/or observed other agents actions and the rewards they received. We introduce a new MARL algorithm called the Weighted Policy Learner (WPL), which allows agents to reach a Nash Equilibrium (NE) in benchmark 2-player-2-action games with minimum knowledge. Using WPL, the only feedback an agent needs is its own local reward (the agent does not observe other agents actions or rewards). Furthermore, WPL does not assume that agents know the underlying game or the corresponding Nash Equilibrium a priori. We experimentally show that our algorithm converges in benchmark two-player-two-action games. We also show that our algorithm converges in the challenging Shapley's game where previous MARL algorithms failed to converge without knowing the underlying game or the NE. Furthermore, we show that WPL outperforms the state-of-the-art algorithms in a more realistic setting of 100 agents interacting and learning concurrently. An important aspect of understanding the behavior of a MARL algorithm is analyzing the dynamics of the algorithm: how the policies of multiple learning agents evolve over time as agents interact with one another. Such an analysis not only verifies whether agents using a given MARL algorithm will eventually converge, but also reveals the behavior of the MARL algorithm prior to convergence. We analyze our algorithm in two-player-two-action games and show that symbolically proving WPL's convergence is difficult, because of the non-linear nature of WPL's dynamics, unlike previous MARL algorithms that had either linear or piece-wise-linear dynamics. Instead, we numerically solve WPL's dynamics differential equations and compare the solution to the dynamics of previous MARL algorithms.
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Yang, Jianhua. "A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports." Computational Intelligence and Neuroscience 2022 (March 24, 2022): 1–9. http://dx.doi.org/10.1155/2022/2663834.

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In sports, the essence of a complete technical action is a complete information structure pattern and the athlete’s judgment of the action is actually the identification of the movement information structure pattern. Action recognition refers to the ability of the human brain to distinguish a perceived action from other actions and obtain predictive response information when it identifies and confirms it according to the constantly changing motion information on the field. Action recognition mainly includes two aspects: one is to obtain the required action information based on visual observation and the other is to judge the action based on the obtained action information, but the neuropsychological mechanism of this process is still unknown. In this paper, a new key frame extraction method based on the clustering algorithm and multifeature fusion is proposed for sports videos with complex content, many scenes, and rich actions. First, a variety of features are fused, and then, similarity measurement can be used to describe videos with complex content more completely and comprehensively; second, a clustering algorithm is used to cluster sports video sequences according to scenes, eliminating the need for shots in the case of many scenes. It is difficult and complicated to detect segmentation; third, extracting key frames according to the minimum motion standard can more accurately represent the video content with rich actions. At the same time, the clustering algorithm used in this paper is improved to enhance the offline computing efficiency of the key frame extraction system. Based on the analysis of the advantages and disadvantages of the classical convolutional neural network and recurrent neural network algorithms in deep learning, this paper proposes an improved convolutional network and optimization based on the recognition and analysis of human actions under complex scenes, complex actions, and fast motion compared to post-neural network and hybrid neural network algorithm. Experiments show that the algorithm achieves similar human observation of athletes’ training execution and completion. Compared with other algorithms, it has been verified that it has very high learning rate and accuracy for the athlete’s action recognition.
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Abduljabbar Ali, Mohammed, Abir Jaafar Hussain, and Ahmed T. Sadiq. "Deep Learning Algorithms for Human Fighting Action Recognition." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 02 (February 16, 2022): 71–87. http://dx.doi.org/10.3991/ijoe.v18i02.28019.

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— Human action recognition using skeletons has been employed in various applications, including healthcare robots, human-computer interaction, and surveillance systems. Recently, deep learning systems have been used in various applications, such as object classification. In contrast to conventional techniques, one of the most prominent convolutional neural network deep learning algorithms extracts image features from its operations. Machine learning in computer vision applications faces many challenges, including human action recognition in real time. Despite significant improvements, videos are typically shot with at least 24 frames per second, meaning that the fastest classification technologies take time. Object detection algorithms must correctly identify and locate essential items, but they must also be speedy at prediction time to meet the real-time requirements of video processing. The fundamental goal of this research paper is to recognize the real-time state of human fighting to provide security in organizations by discovering and identifying problems through video surveillance. First, the images in the videos are investigated to locate human fight scenes using the YOLOv3 algorithm, which has been updated in this work. Our improvements to the YOLOv3 algorithm allowed us to accelerate the exploration of a group of humans in the images. The center locator feature in this algorithm was adopted as an essential indicator for measuring the safety distance between two persons. If it is less than a specific value specified in the code, they are tracked. Then, a deep sorting algorithm is used to track people. This framework is filtered to process and classify whether these two people continue to exceed the programmatically defined minimum safety distance. Finally, the content of the filter frame is categorized as combat scenes using the OpenPose technology and a trained VGG-16 algorithm, which classifies the situation as walking, hugging, or fighting. A dataset was created to train these algorithms in the three categories of walking, hugging, and fighting. The proposed methodology proved successful, exhibiting a classification accuracy for walking, hugging, and fighting of 95.0%, 87.4%, and 90.1%, respectively.
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Amir, E., and A. Chang. "Learning Partially Observable Deterministic Action Models." Journal of Artificial Intelligence Research 33 (November 20, 2008): 349–402. http://dx.doi.org/10.1613/jair.2575.

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We present exact algorithms for identifying deterministic-actions' effects and preconditions in dynamic partially observable domains. They apply when one does not know the action model(the way actions affect the world) of a domain and must learn it from partial observations over time. Such scenarios are common in real world applications. They are challenging for AI tasks because traditional domain structures that underly tractability (e.g., conditional independence) fail there (e.g., world features become correlated). Our work departs from traditional assumptions about partial observations and action models. In particular, it focuses on problems in which actions are deterministic of simple logical structure and observation models have all features observed with some frequency. We yield tractable algorithms for the modified problem for such domains. Our algorithms take sequences of partial observations over time as input, and output deterministic action models that could have lead to those observations. The algorithms output all or one of those models (depending on our choice), and are exact in that no model is misclassified given the observations. Our algorithms take polynomial time in the number of time steps and state features for some traditional action classes examined in the AI-planning literature, e.g., STRIPS actions. In contrast, traditional approaches for HMMs and Reinforcement Learning are inexact and exponentially intractable for such domains. Our experiments verify the theoretical tractability guarantees, and show that we identify action models exactly. Several applications in planning, autonomous exploration, and adventure-game playing already use these results. They are also promising for probabilistic settings, partially observable reinforcement learning, and diagnosis.
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Christiansen, Alan D., and Kenneth Y. Goldberg. "Comparing two algorithms for automatic planning by robots in stochastic environments." Robotica 13, no. 6 (November 1995): 565–73. http://dx.doi.org/10.1017/s0263574700018646.

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SummaryPlanning a sequence of robot actions is especially difficult when the outcome of actions is uncertain, as is inevitable when interacting with the physical environment. In this paper we consider the case of finite state and action spaces where actions can be modeled as Markov transitions. Finding a plan that achieves a desired state with maximum probability is known to be an NP-Complete problem. We consider two algorithms: an exponential-time algorithm that maximizes probability, and a polynomial-time algorithm that maximizes a lower bound on the probability. As these algorithms trade off plan time for plan quality, we compare their performance on a mechanical system for orienting parts. Our results lead us to identify two properties of stochastic actions that can be used to choose between these planning algorithms for other applications.
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Wu, Yuchuan, Shengfeng Qi, Feng Hu, Shuangbao Ma, Wen Mao, and Wei Li. "Recognizing activities of the elderly using wearable sensors: a comparison of ensemble algorithms based on boosting." Sensor Review 39, no. 6 (November 18, 2019): 743–51. http://dx.doi.org/10.1108/sr-11-2018-0309.

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Purpose In human action recognition based on wearable sensors, most previous studies have focused on a single type of sensor and single classifier. This study aims to use a wearable sensor based on flexible sensors and a tri-axial accelerometer to collect action data of elderly people. It uses a statistical modeling approach based on the ensemble algorithm to classify actions and verify its validity. Design/methodology/approach Nine types of daily actions were collected by the wearable sensor device from a group of elderly volunteers, and the time-domain features of the action sequences were extracted. The dimensionality of the feature vectors was reduced by linear discriminant analysis. An ensemble learning method based on XGBoost was used to build a model of elderly action recognition. Its performance was compared with the action recognition rate of other algorithms based on the Boosting algorithm, and with the accuracy of single classifier models. Findings The effectiveness of the method was validated by three experiments. The results show that XGBoost is able to classify nine daily actions of the elderly and achieve an average recognition rate of 94.8 per cent, which is superior to single classifiers and to other ensemble algorithms. Practical implications The research could have important implications for health care, including the treatment and rehabilitation of the elderly, and the prevention of falls. Originality/value Instead of using a single type of sensor, this research used a wearable sensor to obtain daily action data of the elderly. The results show that, by using the appropriate method, the device can obtain detailed data of joint action at a low cost. Comparing differences in performance, it was concluded that XGBoost is the most suitable algorithm for building a model of elderly action recognition. This method, together with a wearable sensor, can provide key data and accurate feedback information to monitor the elderly in their rehabilitation activities.
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Dissertations / Theses on the topic "Action algorithms"

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Cox, Jürgen 1970. "Solution of sign and complex action problems with cluster algorithms." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8646.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2001.
Includes bibliographical references (p. [105]-109) and index.
Two kinds of models are considered which have a Boltzmann weight which is either not real or real but not positive and so standard Monte Carlo methods are not applicable. These sign or complex action problems are solved with the help of cluster algorithms. In each case improved estimators for the Boltzmann weight are constructed which are real and positive. The models considered belong to two classes: fermionic and non-fermionic models. An example for a non-fermionic model is the Potts model approximation to QCD at non-zero baryon density. The three-dimensional three-state Potts model captures the qualitative features of this theory. It has a complex action and so the Boltzmann weight cannot be interpreted as a probability. The complex action problem is solved by using a cluster algorithm. The improved estimator for the complex phase of the Boltzmann factor is real and positive and is used for importance sampling. The first order deconfinement transition line is investigated and the universal behavior at its critical endpoint is studied.
(cont.) An example for a fermionic model with a sign problem are staggered fermions with 2 flavors in 3+1 dimensions. Here the sign is connected to the permutation sign of fermion world lines and is of nonlocal nature. Cluster flips change the topology of the fermion world lines and they have a well defined effect on the permutation sign independent of the other clusters. The sign problem is solved by suppressing those clusters whose contribution to the partition function and observables of interest would be zero. We confirm that the universal critical behavior of the finite temperature chiral phase transition is the one of the three dimensional Ising model. We also study staggered fermions with one flavor in 2+1 dimensions and confirm that the chiral phase transition then belongs to the universality class of the two dimensional Ising model.
by Jürgen Cox.
Ph.D.
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Oppon, Ekow CruickShank. "Synergistic use of promoter prediction algorithms: a choice of small training dataset?" Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8222_1185436339.

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Promoter detection, especially in prokaryotes, has always been an uphill task and may remain so, because of the many varieties of sigma factors employed by various organisms in transcription. The situation is made more complex by the fact, that any seemingly unimportant sequence segment may be turned into a promoter sequence by an activator or repressor (if the actual promoter sequence is made unavailable). Nevertheless, a computational approach to promoter detection has to be performed due to number of reasons. The obvious that comes to mind is the long and tedious process involved in elucidating promoters in the &lsquo
wet&rsquo
laboratories not to mention the financial aspect of such endeavors. Promoter detection/prediction of an organism with few characterized promoters (M.tuberculosis) as envisaged at the beginning of this work was never going to be easy. Even for the few known Mycobacterial promoters, most of the respective sigma factors associated with their transcription were not known. If the information (promoter-sigma) were available, the research would have been focused on categorizing the promoters according to sigma factors and training the methods on the respective categories. That is assuming that, there would be enough training data for the respective categories. Most promoter detection/prediction studies have been carried out on E.coli because of the availability of a number of experimentally characterized promoters (+- 310). Even then, no researcher to date has extended the research to the entire E.coli genome.

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Wanek, J. F. "Direct action of radiation on mummified cells : modelling of computed tomography by Monte Carlo algorithms." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1432807/.

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X-ray imaging is a non-destructive and preferred method in paleopathology to reconstruct the history of ancient diseases. Sophisticated imaging technologies such as computed tomography (CT) have become common for the investigation of skeletal disorders in human remains. The effects of CT exposure on ancient cells have not been quantitatively examined in the past and may be important for subsequent genetic analysis. To remedy this shortcoming, different Monte Carlo models were developed to simulate X-ray irradiation on ancient cells. Effects of mummification and physical processes were considered by using two sizes of cells and three different phantom tissues that enclosed the investigated cell cluster. This cluster was positioned at the isocentre of a CT scanner model, where the cell hit probabilities P(0,1,…, n) were calculated according to the Poisson distribution. To study the impact of the dominant physical process, CT scans for X-ray spectra of 80 and 120 kVp were simulated. The calculated risk of DNA damage according to the multi-hit, multi-target model revealed that the probability of two DNA hits was pNT = 0.001 with cell size 6x6x10 μm3 (NT6610) and pNT = 0.00033 with cell size 4x4x6 μm3 (NT446) for normal tissue (NT) at 80 kVp. A further decrease in DNA damage was observed with pNT = 0.0006 (NT6610) and pNT = 0.00009 (NT446) at 120 kVp. All values of p are in good agreement with those given by the X-ray risk of cancer. It is concluded that the probability of ancient DNA (aDNA) damage following CT imaging depends on the number and volume of fragments m with paDNA < pNT^m (m ≥ 1). Increasing the number of aDNA fragments m is associated with rapidly decreasing aDNA damage through X-ray imaging.
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Umuhoza, Denise. "Metric of trust for mobile ad hoc networks using source routing algorithms." Thesis, University of the Western Cape, 2006. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_8946_1183465240.

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This thesis proposed and presented technical details of new probabilistic metrics of trust in the links wireless ad hoc networks for unobservable communications. In covert communication networks, only the end nodes are aware of the communication characteristics of the overall path. The most widely used protocols of ad hoc networks was overviewed. The routing protocols of ad hoc networks with trust considerations and select Destination Sequence Routing, a protocol that can be used in distributed ad hoc network settings for path discovery, was also reviewed. It establishes a path through which all packets sent by a source must pass to the destination. The end nodes are responsible for examining the statistics of the received packets and deriving inferences on path feature variations which are used for computing new trust metrics. When a path is judged not trustworthy based on the metrics, the Destination Sequence Routing is informed to undertake a new trusted path discovery between the end points. The thesis added a new feature based on the quality of service parameters of the path to create trust in the links in recognition of attacks.

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Brualla, Barberà Llorenç. "Path integral Monte Carlo. Algorithms and applications to quantum fluids." Doctoral thesis, Universitat Politècnica de Catalunya, 2002. http://hdl.handle.net/10803/6577.

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Path integral Monte Carlo (PIMC) is a method suitable for quantum liquid simulations at finite temperature. We present in this thesis a study of PIMC dealing with the theory and algorithms related to it, and then two applications of PIMC to current research problems of quantum fluids in the Bolzmann regime.
The first part encompasses a study of the different ingredients of a PIMC code: action, sampling and physical property estimators. Particular attention has been paid to Li-Broughton's higher order approximation to the action. Regarding sampling, several collective movement methods have been derived, including the bisection algorithm, that has been thoroughly tested. We also include a study of estimators for different physical properties, such as, the energy (through the thermodynamic and virial estimators), the pair distribution function, the structure factor, and the momentum distribution.
In relation to the momentum distribution, we have developed a novel algorithm for its estimation, the trail method. It surmounts some of the problems exposed by previous approaches, such as the open chain method or McMillan's algorithm.
The Richardson extrapolation used within PIMC simulations, is another contribution of this thesis. Up until now, this extrapolation has not been used in this context. We present studies of the energy dependence on the number of "beads", along with the betterment provide by the Richardson extrapolation.
Inasmuch as our goal is to perform research of quantum liquids at finite temperature, we have produced a library of codes, written from scratch, that implement most of the features theoretically developed. The most elaborated parts of these codes are included in some of the appendixes.
The second part shows two different applications of the algorithms coded. We present results of a PIMC calculation of the momentum distribution of Ne and normal 4He at low temperatures. In the range of temperatures analysed, exchanges can be disregarded and both systems are considered Boltzmann quantum liquids. Their quantum character is well reflected in their momentum distributions witch show clear departures from the classical limit. The PIMC momentum distributions which show clear departures from the classical limit. The PIMC momentum distributions are sampled using the trail method. Kinetic energies of both systems, as a function of temperature and at a fixed density, are also reported.
Finally, the solid-liquid neon phase transition along the 35 K isotherm has been characterized.While thermodynamic properties of the solid phase are well known the behaviour of some properties, such as the energy or the dessity, during the trasition presen6 some uncertainties For example, experimental data for the place diagram, which determines solid and liquid boundaries, present sizeable differences. The temperature chosen is high enough so that Bose or Fermi statistics corrections are small, although the system is strongly quantum mechanical. The results obtained show a discontinuity in the kinetic energy during the transition.
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Празднікова, Маргарита Олександрівна. "Додаток на базі Android «Кіногід» для керування контентом." Bachelor's thesis, КПІ ім. Ігоря Сікорського, 2020. https://ela.kpi.ua/handle/123456789/34968.

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Структура та обсяг роботи. Пояснювальна записка дипломного проекту складається з шести розділів, містить 8 рисунків, 5 таблиць, 5 додатка, 45 джерел. Дипломний проект присвячений розробці додатку на базі Android “Кіногід”. У розділі аналіз існуючих рішень для систематизації інформації про кінопокази розглянуть існуючі сервіси їх функції та недоліки, які необхідно вдосконалити. У розділі аналіз мов програмування та технології розроблення Android додатку розглянуті теоретичні аспекти методів виконання завданяя всі можливі ускладнення та недоліки різних систем у розробці Android додатку. Теоретичні відомості щодо теми диплому. Показані методи розробки додатку, алгоритми дій при поставленій задачі, а також вирішення проблеми, які могли виникнути. У розділа особливості реалізаціх програмного додатку для мобільної платформи показані теоретичні відомості про алгоритми, які були використанні. У розділі аналіз додатку розгялнуті методи тестування додатку, аналіз подальшого вдоскналення.
Structure and scope of work. The explanatory note of the diploma project consists of six sections, contains 8 figures, 5 tables, 5 appendices, 45 sources. Thesis project is dedicated to the development of an application based on Android "Kinogid". In the section analysis of existing solutions for systematization of information about film screenings will consider the existing services, their functions and shortcomings that need to be improved. The section analysis of programming languages and technologies of Android application development considers theoretical aspects of execution methods causing all possible complications and shortcomings of different systems in Android application development. Theoretical information on the topic of the diploma. The methods of application development, algorithms of actions at the set task, and also the decision of a problem which could arise are shown. The section features of the implementation of the software application for the mobile platform shows theoretical information about the algorithms that were used. In the section analysis of the application the methods of testing the application, analysis of further improvement are explained.
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Stephanos, Dembe. "Machine Learning Approaches to Dribble Hand-off Action Classification with SportVU NBA Player Coordinate Data." Digital Commons @ East Tennessee State University, 2021. https://dc.etsu.edu/etd/3908.

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Recently, strategies of National Basketball Association teams have evolved with the skillsets of players and the emergence of advanced analytics. One of the most effective actions in dynamic offensive strategies in basketball is the dribble hand-off (DHO). This thesis proposes an architecture for a classification pipeline for detecting DHOs in an accurate and automated manner. This pipeline consists of a combination of player tracking data and event labels, a rule set to identify candidate actions, manually reviewing game recordings to label the candidates, and embedding player trajectories into hexbin cell paths before passing the completed training set to the classification models. This resulting training set is examined using the information gain from extracted and engineered features and the effectiveness of various machine learning algorithms. Finally, we provide a comprehensive accuracy evaluation of the classification models to compare various machine learning algorithms and highlight their subtle differences in this problem domain.
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Gill, Harnavpreet Singh. "Computationally Robust Algorithms for Hypoid Gear Cutting and Contact Line Determination using Ease-Off Methodology." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1587499768039312.

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Chaaraoui, Alexandros Andre. "Vision-based Recognition of Human Behaviour for Intelligent Environments." Doctoral thesis, Universidad de Alicante, 2014. http://hdl.handle.net/10045/36395.

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A critical requirement for achieving ubiquity of artificial intelligence is to provide intelligent environments with the ability to recognize and understand human behaviour. If this is achieved, proactive interaction can occur and, more interestingly, a great variety of services can be developed. In this thesis we aim to support the development of ambient-assisted living services with advances in human behaviour analysis. Specifically, visual data analysis is considered in order to detect and understand human activity at home. As part of an intelligent monitoring system, single- and multi-view recognition of human actions is performed, along several optimizations and extensions. The present work may pave the way for more advanced human behaviour analysis techniques, such as the recognition of activities of daily living, personal routines and abnormal behaviour detection.
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Seita, Marcelo Ruiz. "Simulação multi agente em mercados financeiros artificiais utilizando algoritmos genéticos." reponame:Repositório Institucional do FGV, 2014. http://hdl.handle.net/10438/11936.

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Aiming to establish a methodology capable of segregate market’s moments and identifying investors's characteristics acting on a given financial market, this study employs simulations created by an Agent-based Artificial Financial Market, using a Genetic Algorithm to adjust such simulations to the real observed historic data. For this purpose, a Bovespa's index future contracts application was developed. This methodology could easily be extended to other financial markets by simply changing the model's parameters. Over the foundations established by Toriumi et al. (2011), significant contributions were achieved, promoting knowledge enhancements on the chosen target market, as well as on Artificial Financial Market modelling techniques, and also on the application of Genetic Algorithm into financial markets, resulting on experiments and analysis that suggest the efficacy of the methodology herein proposed.
Com o objetivo de estabelecer uma metodologia capaz segregar momentos de mercado e de identificar as características predominantes dos investidores atuantes em um determinado mercado financeiro, este trabalho emprega simulações geradas em um Mercado Financeiro Artificial baseado em agentes, utilizando um Algoritmo Genético para ajustar tais simulações ao histórico real observado. Para tanto, uma aplicação foi desenvolvida utilizando-se o mercado de contratos futuros de índice Bovespa. Esta metodologia poderia facilmente ser estendida a outros mercados financeiros através da simples parametrização do modelo. Sobre as bases estabelecidas por Toriumi et al. (2011), contribuições significativas foram atingidas, promovendo acréscimo de conhecimento acerca tanto do mercado alvo escolhido, como das técnicas de modelagem em Mercados Financeiros Artificiais e também da aplicação de Algoritmos Genéticos a mercados financeiros, resultando em experimentos e análises que sugerem a eficácia do método ora proposto.
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Books on the topic "Action algorithms"

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Killingbeck, J. P. Microcomputer algorithms: Action from algebra. Bristol, England: Adam Hilger, 1991.

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Pintér, J. Global optimization in action: Continuous and Lipschitz optimization--algorithms, implementations, and applications. Dordrecht: Kluwer Academic Publishers, 1996.

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Quadflieg, Sven, Klaus Neuburg, and Simon Nestler, eds. (Dis)Obedience in Digital Societies. Bielefeld, Germany: transcript Verlag, 2022. http://dx.doi.org/10.14361/9783839457634.

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Algorithms are not to be regarded as a technical structure but as a social phenomenon - they embed themselves, currently still very subtle, into our political and social system. Algorithms shape human behavior on various levels: they influence not only the aesthetic reception of the world but also the well-being and social interaction of their users. They act and intervene in a political and social context. As algorithms influence individual behavior in these social and political situations, their power should be the subject of critical discourse - or even lead to active disobedience and to the need for appropriate tools and methods which can be used to break the algorithmic power.
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Biryulin, Vladimir, Aleksey None, Dar'ya Kudelina, Oleg Larin, and Anton Tancyura. Relay protection and automation systems elektroenergeticheskim. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1058880.

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The manual includes basic information about the element base of relay protection and automation, principles of operation and circuit implementation of various types of relay protection and automation systems, algorithms of calculation of parameters of the action of relay protection and automation. Designed for students in specialty "power and electrical engineering", as well as professionals involved in the design and operation of relay protection and automation of electric power systems.
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Kuo, Sen M. Active noise control systems: Algorithms and DSP implementations. New York: Wiley, 1996.

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Algoritmy preobrazhenii︠a︡: Psikhologii︠a︡ vokalʹno-st︠s︡enicheskogo perevoploshchenii︠a︡. Moskva: Kirillit︠s︡a, 2005.

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Juang, Jer-Nan. Deadbeat predictive controllers. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Juang, Jer-Nan. Recursive deadbeat controller design. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1997.

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Meyer, George. Dynamic forms. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1997.

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Meyer, George. Dynamic forms. Moffett Field, Calif: NASA National Aeronautics and Space Administration, Ames Research Center, 1993.

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Book chapters on the topic "Action algorithms"

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Bergel, Alexandre. "Genetic Algorithms in Action." In Agile Artificial Intelligence in Pharo, 195–208. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5384-7_9.

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Gochet, Paul, Pascal Gribomont, and Didier Rossetto. "Algorithms for Relevant Logic." In Logic, Thought and Action, 479–96. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-3167-x_21.

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Fraczak, Wojciech. "Multi-action process algebra." In Algorithms, Concurrency and Knowledge, 126–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60688-2_40.

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Garcez, Artur S. d’Avila, and Luis C. Lamb. "Cognitive Algorithms and Systems: Reasoning and Knowledge Representation." In Perception-Action Cycle, 573–600. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1452-1_18.

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Mirjalili, Seyedali, and Andrew Lewis. "Benchmark Function Generators for Single-Objective Robust Optimisation Algorithms." In Decision Science in Action, 13–29. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0860-4_2.

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Haque, Shazia, Lars Kulik, and Alexander Klippel. "Algorithms for Reliable Navigation and Wayfinding." In Spatial Cognition V Reasoning, Action, Interaction, 308–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75666-8_18.

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Zhang, Qi. "Cognitive Algorithms and Systems of Episodic Memory, Semantic Memory, and Their Learnings." In Perception-Action Cycle, 417–48. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1452-1_13.

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Lima, Pedro U. "Cognitive Algorithms and Systems of Error Monitoring, Conflict Resolution and Decision Making." In Perception-Action Cycle, 473–96. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-1452-1_15.

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Ahuactzin, Juan Manuel, El-Ghazali Talbi, Pierre Bessière, and Emmanuel Mazer. "Using genetic algorithms for robot motion planning." In Geometric Reasoning for Perception and Action, 84–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-57132-9_6.

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Faundez-Zanuy, Marcos, Unto Laine, Gernot Kubin, Stephen McLaughlin, Bastiaan Kleijn, Gerard Chollet, Bojan Petek, and Amir Hussain. "The COST-277 European Action: An Overview." In Nonlinear Analyses and Algorithms for Speech Processing, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11613107_1.

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Conference papers on the topic "Action algorithms"

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Wang, Hanmo, Xiaojun Chang, Lei Shi, Yi Yang, and Yi-Dong Shen. "Uncertainty Sampling for Action Recognition via Maximizing Expected Average Precision." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/134.

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Recognizing human actions in video clips has been an important topic in computer vision. Sufficient labeled data is one of the prerequisites for the good performance of action recognition algorithms. However, while abundant videos can be collected from the Internet, categorizing each video clip is tedious and even time-consuming. Active learning is one way to alleviate the labeling labor by allowing the classifier to choose the most informative unlabeled instances for manual annotation. Among various active learning algorithms, uncertainty sampling is arguably the most widely-used strategy. Conventional uncertainty sampling strategies such as entropy-based methods are usually tested under accuracy. However, in action recognition Average Precision (AP) is an acknowledged evaluation metric, which is somehow ignored in the active learning community. It is defined as the area under the precision-recall curve. In this paper, we propose a novel uncertainty sampling algorithm for action recognition using expected AP. We conduct experiments on three real-world action recognition datasets and show that our algorithm outperforms other uncertainty-based active learning algorithms.
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Patel, Apoorva D. "Wave Algorithms: Optimal Database Search and Catalysis." In QUANTUM COMPUTING: Back Action 2006. AIP, 2006. http://dx.doi.org/10.1063/1.2400898.

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Thomas, Oliver, Miri Zilka, Adrian Weller, and Novi Quadrianto. "An Algorithmic Framework for Positive Action." In EAAMO '21: Equity and Access in Algorithms, Mechanisms, and Optimization. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3465416.3483303.

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Lelis, Levi H. S. "Planning Algorithms for Zero-Sum Games with Exponential Action Spaces: A Unifying Perspective." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/681.

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In this paper we review several planning algorithms developed for zero-sum games with exponential action spaces, i.e., spaces that grow exponentially with the number of game components that can act simultaneously at a given game state. As an example, real-time strategy games have exponential action spaces because the number of actions available grows exponentially with the number of units controlled by the player. We also present a unifying perspective in which several existing algorithms can be described as an instantiation of a variant of NaiveMCTS. In addition to describing several existing planning algorithms for exponential action spaces, we show that other instantiations of this variant of NaiveMCTS represent novel and promising algorithms to be studied in future works.
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Tavares, Anderson Rocha, Sivasubramanian Anbalagan, Leandro Soriano Marcolino, and Luiz Chaimowicz. "Algorithms or Actions? A Study in Large-Scale Reinforcement Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/377.

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Large state and action spaces are very challenging to reinforcement learning. However, in many domains there is a set of algorithms available, which estimate the best action given a state. Hence, agents can either directly learn a performance-maximizing mapping from states to actions, or from states to algorithms. We investigate several aspects of this dilemma, showing sufficient conditions for learning over algorithms to outperform over actions for a finite number of training iterations. We present synthetic experiments to further study such systems. Finally, we propose a function approximation approach, demonstrating the effectiveness of learning over algorithms in real-time strategy games.
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COULBOIS, THIERRY. "PARTIAL ACTION OF GROUPS ON RELATIONAL STRUCTURES: A CONNECTION BETWEEN MODEL THEORY AND PROFINITE TOPOLOGY." In Semigroups, Algorithms, Automata and Languages. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776884_0014.

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Liu, Fang, Feifei Qi, Xiaofen Xing, and Kailing Guo. "Simple to complex dictionary learning for action recognition." In ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3508546.3508630.

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Li, Shuxin, Youzhi Zhang, Xinrun Wang, Wanqi Xue, and Bo An. "CFR-MIX: Solving Imperfect Information Extensive-Form Games with Combinatorial Action Space." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/504.

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In many real-world scenarios, a team of agents must coordinate with each other to compete against an opponent. The challenge of solving this type of game is that the team's joint action space grows exponentially with the number of agents, which results in the inefficiency of the existing algorithms, e.g., Counterfactual Regret Minimization (CFR). To address this problem, we propose a new framework of CFR: CFR-MIX. Firstly, we propose a new strategy representation that represents a joint action strategy using individual strategies of all agents and a consistency relationship to maintain the cooperation between agents. To compute the equilibrium with individual strategies under the CFR framework, we transform the consistency relationship between strategies to the consistency relationship between the cumulative regret values. Furthermore, we propose a novel decomposition method over cumulative regret values to guarantee the consistency relationship between the cumulative regret values. Finally, we introduce our new algorithm CFR-MIX which employs a mixing layer to estimate cumulative regret values of joint actions as a non-linear combination of cumulative regret values of individual actions. Experimental results show that CFR-MIX outperforms existing algorithms on various games significantly.
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Lee, Greg, and Vadim Bulitko. "Genetic algorithms for action set selection across domains." In the 8th annual conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1143997.1144275.

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He, Chun-Lin, and Wei Pan. "Study on human Action Recognition Algorithms in videos." In 2015 International Symposium on Computers and Informatics. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/isci-15.2015.94.

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Reports on the topic "Action algorithms"

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Ozkaya, Yusuf, Erdem Sariyuce, Umit Catalyurek, and Ali Pinar. Active Betweenness Cardinality: Algorithms and Applications. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1733256.

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Bodson, Marc. Adaptive Algorithms for Active Noise and Vibration Control. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada390623.

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Sadek, Fahim, and Bijan Mohraz. Semi-active control algorithms for structures with variable dampers. Gaithersburg, MD: National Institute of Standards and Technology, 1997. http://dx.doi.org/10.6028/nist.ir.6052.

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Mintii, Iryna S., Svitlana V. Shokaliuk, Tetiana A. Vakaliuk, Mykhailo M. Mintii, and Vladimir N. Soloviev. Import test questions into Moodle LMS. [б. в.], September 2019. http://dx.doi.org/10.31812/123456789/3271.

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The purpose of the study is to highlight the theoretical and methodological aspects of preparing the test questions of the most common types in the form of text files for further import into learning management system (LMS) Moodle. The subject of the research is the automated filling of the Moodle LMS test database. The objectives of the study: to analyze the import files of test questions, their advantages and disadvantages; to develop guidelines for the preparation of test questions of common types in the form of text files for further import into Moodle LMS. The action algorithms for importing questions and instructions for submitting question files in such formats as Aiken, GIFT, Moodle XML, “True/False” questions, “Multiple Choice” (one of many and many of many), “Matching”, with an open answer – “Numerical” or “Short answer” and “Essay” are offered in this article. The formats for submitting questions, examples of its designing and developed questions were demonstrated in view mode in Moodle LMS.
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Li, Lingxi, Yaobin Chen, Renren Tian, Feng Li, Howell Li, and James R. Sturdevant. An Integrated Critical Information Delivery Platform for Smart Segment Dissemination to Road Users. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317440.

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An integrated critical information delivery platform for smart segment dissemination to road users was developed. A statewide baseline milepost geodatabase was created at 0.1-mile resolution with tools, protocols, and interfaces that allow other data sources to be efficiently utilized. A variety of data sources (e.g., INRIX, CARS, Doppler, camera images, connected vehicle data, automated vehicle location) were integrated into existing and new dashboards for stakeholders to monitor roadway conditions and after-action reviews. Additionally, based on these data sources, algorithms were developed and an API was created to identify hazardous road conditions when the location of the end-user mobile device was given. Message delivery schemes were successfully implemented to issue alerts to drivers, which were integrated with two in-vehicle smartphone applications. The performance of the integrated platform was evaluated using both the driving simulator and a number of simulated and on-road tests. The results demonstrated the system was able to disseminate data in real-time using the developed platform.
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Sadek, Fahim, and Bijan Mohraz. A modified optimal algorithm for active structural control. Gaithersburg, MD: National Institute of Standards and Technology, 1996. http://dx.doi.org/10.6028/nist.ir.5782.

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Perdigão, Rui A. P. Information physics and quantum space technologies for natural hazard sensing, modelling and prediction. Meteoceanics, September 2021. http://dx.doi.org/10.46337/210930.

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Disruptive socio-natural transformations and climatic change, where system invariants and symmetries break down, defy the traditional complexity paradigms such as machine learning and artificial intelligence. In order to overcome this, we introduced non-ergodic Information Physics, bringing physical meaning to inferential metrics, and a coevolving flexibility to the metrics of information transfer, resulting in new methods for causal discovery and attribution. With this in hand, we develop novel dynamic models and analysis algorithms natively built for quantum information technological platforms, expediting complex system computations and rigour. Moreover, we introduce novel quantum sensing technologies in our Meteoceanics satellite constellation, providing unprecedented spatiotemporal coverage, resolution and lead, whilst using exclusively sustainable materials and processes across the value chain. Our technologies bring out novel information physical fingerprints of extreme events, with recently proven records in capturing early warning signs for extreme hydro-meteorologic events and seismic events, and do so with unprecedented quantum-grade resolution, robustness, security, speed and fidelity in sensing, processing and communication. Our advances, from Earth to Space, further provide crucial predictive edge and added value to early warning systems of natural hazards and long-term predictions supporting climatic security and action.
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Jiang, Ruoyi, Brian Quanz, Hongliang Fei, and Jun Huan. Rail Sensor Testbed Program: Active Agents in Containers for Transport Chain Security: Algorithms. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada539045.

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Hoeiland-Joergensen, T., P. McKenney, D. Taht, J. Gettys, and E. Dumazet. The Flow Queue CoDel Packet Scheduler and Active Queue Management Algorithm. RFC Editor, January 2018. http://dx.doi.org/10.17487/rfc8290.

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Hodgdon, Taylor, Anthony Fuentes, Jason Olivier, Brian Quinn, and Sally Shoop. Automated terrain classification for vehicle mobility in off-road conditions. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40219.

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The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be in-formed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.
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