Results for 'Q-learning, dynamic structuring of exploration space, reinforcement learning, genetic algorithm, multi legged robot, optimality, fault tolerance'

964 found
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  1.  30
    Qdsega による多足ロボットの歩行運動の獲得.Matsuno Fumitoshi Ito Kazuyuki - 2002 - Transactions of the Japanese Society for Artificial Intelligence 17:363-372.
    Reinforcement learning is very effective for robot learning. Because it does not need priori knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforcement learning algorithm: “Q-learning with Dynamic Structuring of Exploration Space Based on Genetic Algorithm (QDSEGA)”. It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. And we applied it to 50 link manipulator and effective (...)
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  2.  27
    Ga により探索空間の動的生成を行う Q 学習.Matsuno Fumitoshi Ito Kazuyuki - 2001 - Transactions of the Japanese Society for Artificial Intelligence 16:510-520.
    Reinforcement learning has recently received much attention as a learning method for complicated systems, e.g., robot systems. It does not need prior knowledge and has higher capability of reactive and adaptive behaviors. However increase in dimensionality of the action-state space makes it diffcult to accomplish learning. The applicability of the existing reinforcement learning algorithms are effective for simple tasks with relatively small action-state space. In this paper, we propose a new reinforcement learning algorithm: “Q-learning with Dynamic (...)
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  3.  24
    Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning.Xiaoyi Long, Zheng He & Zhongyuan Wang - 2021 - Complexity 2021:1-7.
    This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network -based reinforcement learning method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. (...)
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  4.  12
    A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems.Chen Zhang, Wen Qin, Ming-Can Fan, Ting Wang & Mou-Quan Shen - 2022 - Complexity 2022:1-19.
    This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix (...)
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  5.  46
    Frontiers of Artificial Intelligence, Ethics, and Multidisciplinary Applications: 1st International Conference on Frontiers of AI, Ethics, and Multidisciplinary Applications (FAIEMA), Greece, 2023.Mina Farmanbar, Maria Tzamtzi, Ajit Kumar Verma & Antorweep Chakravorty (eds.) - 2024 - Springer Nature Singapore.
    This groundbreaking proceedings volume explores the integration of Artificial Intelligence (AI) across key domains—healthcare, finance, education, robotics, industrial and other engineering applications —unveiling its transformative potential and practical implications. With a multidisciplinary lens, it transcends technical aspects, fostering a comprehensive understanding while bridging theory and practice. Approaching the subject matter with depth, the book combines theoretical foundations with real-world case studies, empowering researchers, professionals, and enthusiasts with the knowledge and tools to effectively harness AI. Encompassing diverse AI topics—machine learning, natural (...)
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  6. Integrating reinforcement learning, bidding and genetic algorithms.Ron Sun - unknown
    This paper presents a GA-based multi-agent reinforce- ment learning bidding approach (GMARLB) for perform- ing multi-agent reinforcement learning. GMARLB inte- grates reinforcement learning, bidding and genetic algo- rithms. The general idea of our multi-agent systems is as follows: There are a number of individual agents in a team, each agent of the team has two modules: Q module and CQ module. Each agent can select actions to be performed at each step, which are done (...)
     
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  7.  15
    Multi-agent reinforcement learning based algorithm detection of malware-infected nodes in IoT networks.Marcos Severt, Roberto Casado-Vara, Ángel Martín del Rey, Héctor Quintián & Jose Luis Calvo-Rolle - forthcoming - Logic Journal of the IGPL.
    The Internet of Things (IoT) is a fast-growing technology that connects everyday devices to the Internet, enabling wireless, low-consumption and low-cost communication and data exchange. IoT has revolutionized the way devices interact with each other and the internet. The more devices become connected, the greater the risk of security breaches. There is currently a need for new approaches to algorithms that can detect malware regardless of the size of the network and that can adapt to dynamic changes in the (...)
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  8.  12
    Reinforcement Learning with Probabilistic Boolean Network Models of Smart Grid Devices.Pedro Juan Rivera Torres, Carlos Gershenson García, María Fernanda Sánchez Puig & Samir Kanaan Izquierdo - 2022 - Complexity 2022:1-15.
    The area of smart power grids needs to constantly improve its efficiency and resilience, to provide high quality electrical power in a resilient grid, while managing faults and avoiding failures. Achieving this requires high component reliability, adequate maintenance, and a studied failure occurrence. Correct system operation involves those activities and novel methodologies to detect, classify, and isolate faults and failures and model and simulate processes with predictive algorithms and analytics. In this paper, we showcase the application of a complex-adaptive, self-organizing (...)
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  9. Automatic Partitioning for Multi-Agent Reinforcement Learning.Ron Sun - unknown
    This paper addresses automatic partitioning in complex reinforcement learning tasks with multiple agents, without a priori domain knowledge regarding task structures. Partitioning a state/input space into multiple regions helps to exploit the di erential characteristics of regions and di erential characteristics of agents, thus facilitating learning and reducing the complexity of agents especially when function approximators are used. We develop a method for optimizing the partitioning of the space through experience without the use of a priori domain knowledge. The (...)
     
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  10.  18
    Economic Structure Analysis Based on Neural Network and Bionic Algorithm.Yanjun Dai & Lin Su - 2021 - Complexity 2021:1-11.
    In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm based on elite (...)
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  11.  18
    Multiple Objective Robot Coalition Formation.Naveen Kumar, Lovekesh Vig & Manoj Agarwal - 2011 - Journal of Intelligent Systems 20 (4):395-413.
    In multiple robot systems, the problem of allocation of complex tasks to heterogeneous teams of robots, also known as the multiple robot coalition formation problem, has begun to receive considerable attention. Efforts to address the problem range from heuristics based approaches that search the subspaces of the coalition structure to evolutionary learning approaches. Conventional approaches typically strive to optimize a single objective function such as the number of tasks executed or the time required to execute all tasks, or a weighted (...)
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  12.  12
    Optimal loading method of multi type railway flatcars based on improved genetic algorithm.Zhongliang Yang - 2022 - Journal of Intelligent Systems 31 (1):915-926.
    On the basis of analyzing the complexity of railway flatcar loading optimization problem, according to the characteristics of railway flatcar loading, based on the situation of railway transport loading unit of multiple railway flatcars, this study puts forward the optimal loading optimization method of multimodel railway flatcars based on improved genetic algorithm, constructs the linear programming model of railway flatcar loading optimization problem, and combines with the improved genetic algorithm to solve the problem. The study also analyzes the (...)
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  13.  28
    Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning.Wen-Long Shang, Yanyan Chen, Xingang Li & Washington Y. Ochieng - 2020 - Complexity 2020:1-19.
    Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to (...)
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  14.  7
    Adversarial Dynamics in Centralized Versus Decentralized Intelligent Systems.Levin Brinkmann, Manuel Cebrian & Niccolò Pescetelli - forthcoming - Topics in Cognitive Science.
    Artificial intelligence (AI) is often used to predict human behavior, thus potentially posing limitations to individuals’ and collectives’ freedom to act. AI's most controversial and contested applications range from targeted advertisements to crime prevention, including the suppression of civil disorder. Scholars and civil society watchdogs are discussing the oppressive dangers of AI being used by centralized institutions, like governments or private corporations. Some suggest that AI gives asymmetrical power to governments, compared to their citizens. On the other hand, civil protests (...)
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  15.  46
    Genetically induced communication network fault tolerance.Stephen F. Bush - 2003 - Complexity 9 (2):19-33.
    This paper presents the architecture and initial feasibility results of a proto-type communication network that utilizes genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be preprogrammed within legacy network nodes a priori. A priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in (...)
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  16. Are People Successful at Learning Sequences of Actions on a Perceptual Matching Task?Reiko Yakushijin & Robert A. Jacobs - 2011 - Cognitive Science 35 (5):939-962.
    We report the results of an experiment in which human subjects were trained to perform a perceptual matching task. Subjects were asked to manipulate comparison objects until they matched target objects using the fewest manipulations possible. An unusual feature of the experimental task is that efficient performance requires an understanding of the hidden or latent causal structure governing the relationships between actions and perceptual outcomes. We use two benchmarks to evaluate the quality of subjects’ learning. One benchmark is based on (...)
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  17.  26
    Causal Structure Learning in Continuous Systems.Zachary J. Davis, Neil R. Bramley & Bob Rehder - 2020 - Frontiers in Psychology 11.
    Real causal systems are complicated. Despite this, causal learning research has traditionally emphasized how causal relations can be induced on the basis of idealized events, i.e. those that have been mapped to binary variables and abstracted from time. For example, participants may be asked to assess the efficacy of a headache-relief pill on the basis of multiple patients who take the pill (or not) and find their headache relieved (or not). In contrast, the current study examines learning via interactions with (...)
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  18. Q(st at):= (I — o')Q(st at) + o'(r(st+1).Ron Sun - unknown
    Straightforward reinforcement learning for multi-agent co-learning settings often results in poor outcomes. Meta-learning processes beyond straightforward reinforcement learning may be necessary to achieve good (or optimal) outcomes. Algorithmic processes of meta-learning, or "manipulation", will be described, which is a cognitively realistic and effective means for learning cooperation. We will discuss various "manipulation" routines that address the issue of improving multi-agent co-learning. We hope to develop better adaptive means of multi-agent cooperation, without requiring a priori knowledge, (...)
     
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  19.  26
    Enhancement of K-means clustering in big data based on equilibrium optimizer algorithm.Omar Saber Qasim, Zakariya Yahya Algamal & Sarah Ghanim Mahmood Al-Kababchee - 2023 - Journal of Intelligent Systems 32 (1).
    Data mining’s primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K-means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary to (...)
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  20.  23
    Learning the Structure of Bayesian Networks: A Quantitative Assessment of the Effect of Different Algorithmic Schemes.Stefano Beretta, Mauro Castelli, Ivo Gonçalves, Roberto Henriques & Daniele Ramazzotti - 2018 - Complexity 2018:1-12.
    One of the most challenging tasks when adopting Bayesian networks is the one of learning their structure from data. This task is complicated by the huge search space of possible solutions and by the fact that the problem isNP-hard. Hence, a full enumeration of all the possible solutions is not always feasible and approximations are often required. However, to the best of our knowledge, a quantitative analysis of the performance and characteristics of the different heuristics to solve this problem has (...)
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  21.  25
    Κ-確実探査法と動的計画法を用いた mdps 環境の効率的探索法.Kawada Seiichi Tateyama Takeshi - 2001 - Transactions of the Japanese Society for Artificial Intelligence 16:11-19.
    One most common problem in reinforcement learning systems (e.g. Q-learning) is to reduce the number of trials to converge to an optimal policy. As one of the solution to the problem, k-certainty exploration method was proposed. Miyazaki reported that this method could determine an optimal policy faster than Q-learning in Markov decision processes (MDPs). This method is very efficient learning method. But, we propose an improvement plan that makes this method more efficient. In k-certainty exploration method, in (...)
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  22.  44
    Fuzzy Adaptation Algorithms’ Control for Robot Manipulators with Uncertainty Modelling Errors.Yongqing Fan, Keyi Xing & Xiangkui Jiang - 2018 - Complexity 2018:1-8.
    A novel fuzzy control scheme with adaptation algorithms is developed for robot manipulators’ system. At the beginning, one adjustable parameter is introduced in the fuzzy logic system, the robot manipulators system with uncertain nonlinear terms as the master device and a reference model dynamic system as the slave robot system. To overcome the limitations such as online learning computation burden and logic structure in conventional fuzzy logic systems, a parameter should be used in fuzzy logic system, which composes fuzzy (...)
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  23.  13
    Stochasticity, Nonlinear Value Functions, and Update Rules in Learning Aesthetic Biases.Norberto M. Grzywacz - 2021 - Frontiers in Human Neuroscience 15:639081.
    A theoretical framework for the reinforcement learning of aesthetic biases was recently proposed based on brain circuitries revealed by neuroimaging. A model grounded on that framework accounted for interesting features of human aesthetic biases. These features included individuality, cultural predispositions, stochastic dynamics of learning and aesthetic biases, and the peak-shift effect. However, despite the success in explaining these features, a potential weakness was the linearity of the value function used to predict reward. This linearity meant that the learning process (...)
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  24.  72
    Novelty and Inductive Generalization in Human Reinforcement Learning.Samuel J. Gershman & Yael Niv - 2015 - Topics in Cognitive Science 7 (3):391-415.
    In reinforcement learning, a decision maker searching for the most rewarding option is often faced with the question: What is the value of an option that has never been tried before? One way to frame this question is as an inductive problem: How can I generalize my previous experience with one set of options to a novel option? We show how hierarchical Bayesian inference can be used to solve this problem, and we describe an equivalence between the Bayesian model (...)
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  25. HCI Model with Learning Mechanism for Cooperative Design in Pervasive Computing Environment.Hong Liu, Bin Hu & Philip Moore - 2015 - Journal of Internet Technology 16.
    This paper presents a human-computer interaction model with a three layers learning mechanism in a pervasive environment. We begin with a discussion around a number of important issues related to human-computer interaction followed by a description of the architecture for a multi-agent cooperative design system for pervasive computing environment. We present our proposed three- layer HCI model and introduce the group formation algorithm, which is predicated on a dynamic sharing niche technology. Finally, we explore the cooperative reinforcement (...)
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  26.  16
    Evolution of Dynamic Reconfigurable Neural Networks: Energy Surface Optimality Using Genetic Algorithms.Robert E. Dorsey & John D. Johnson - 1997 - In Daniel S. Levine & Wesley R. Elsberry (eds.), Optimality in Biological and Artificial Networks? Lawrence Erlbaum. pp. 185.
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  27.  82
    Learning with neighbours: Emergence of convention in a society of learning agents.Roland Mühlenbernd - 2011 - Synthese 183 (S1):87-109.
    I present a game-theoretical multi-agent system to simulate the evolutionary process responsible for the pragmatic phenomenon division of pragmatic labour (DOPL), a linguistic convention emerging from evolutionary forces. Each agent is positioned on a toroid lattice and communicates via signaling games , where the choice of an interlocutor depends on the Manhattan distance between them. In this framework I compare two learning dynamics: reinforcement learning (RL) and belief learning (BL). An agent’s experiences from previous plays influence his communication (...)
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  28. Learning to perceive in the sensorimotor approach: Piaget’s theory of equilibration interpreted dynamically.Ezequiel A. Di Paolo, Xabier E. Barandiaran, Michael Beaton & Thomas Buhrmann - 2014 - Frontiers in Human Neuroscience 8:551.
    Learning to perceive is faced with a classical paradox: if understanding is required for perception, how can we learn to perceive something new, something we do not yet understand? According to the sensorimotor approach, perception involves mastery of regular sensorimotor co-variations that depend on the agent and the environment, also known as the “laws” of sensorimotor contingencies (SMCs). In this sense, perception involves enacting relevant sensorimotor skills in each situation. It is important for this proposal that such skills can be (...)
     
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  29. Toward a dual-learning systems model of speech category learning.Bharath Chandrasekaran, Seth R. Koslov & W. T. Maddox - 2014 - Frontiers in Psychology 5:88645.
    More than two decades of work in vision posits the existence of dual-learning systems of category learning. The reflective system uses working memory to develop and test rules for classifying in an explicit fashion, while the reflexive system operates by implicitly associating perception with actions that lead to reinforcement. Dual-learning systems models hypothesize that in learning natural categories, learners initially use the reflective system and, with practice, transfer control to the reflexive system. The role of reflective and reflexive systems (...)
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  30.  11
    Multi-robot inverse reinforcement learning under occlusion with estimation of state transitions.Kenneth Bogert & Prashant Doshi - 2018 - Artificial Intelligence 263 (C):46-73.
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  31.  7
    Source code obfuscation with genetic algorithms using LLVM code optimizations.Juan Carlos de la Torre, Javier Jareño, José Miguel Aragón-Jurado, Sébastien Varrette & Bernabé Dorronsoro - forthcoming - Logic Journal of the IGPL.
    With the advent of the cloud computing model allowing a shared access to massive computing facilities, a surging demand emerges for the protection of the intellectual property tied to the programs executed on these uncontrolled systems. If novel paradigm as confidential computing aims at protecting the data manipulated during the execution, obfuscating techniques (in particular at the source code level) remain a popular solution to conceal the purpose of a program or its logic without altering its functionality, thus preventing reverse-engineering (...)
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  32.  45
    Reinforcing robot perception of multi-modal events through repetition and redundancy and repetition and redundancy.Paul Fitzpatrick, Artur Arsenio & Eduardo R. Torres-Jara - 2006 - Interaction Studies. Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies / Social Behaviour and Communication in Biological and Artificial Systemsinteraction Studies 7 (2):171-196.
    For a robot to be capable of development it must be able to explore its environment and learn from its experiences. It must find opportunities to experience the unfamiliar in ways that reveal properties valid beyond the immediate context. In this paper, we develop a novel method for using the rhythm of everyday actions as a basis for identifying the characteristic appearance and sounds associated with objects, people, and the robot itself. Our approach is to identify and segment groups of (...)
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  33.  21
    Structural-parametric synthesis of deep learning neural networks.Sineglazov V. M. & Chumachenko O. I. - 2020 - Artificial Intelligence Scientific Journal 25 (4):42-51.
    The structural-parametric synthesis of neural networks of deep learning, in particular convolutional neural networks used in image processing, is considered. The classification of modern architectures of convolutional neural networks is given. It is shown that almost every convolutional neural network, depending on its topology, has unique blocks that determine its essential features, Residual block, Inception module, ResNeXt block. It is stated the problem of structural-parametric synthesis of convolutional neural networks, for the solution of which it is proposed to use a (...)
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  34.  20
    Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning.Rui Wang, Xianghua Gan, Qing Li & Xiao Yan - 2021 - Complexity 2021:1-17.
    We study a joint pricing and inventory control problem for perishables with positive lead time in a finite horizon periodic-review system. Unlike most studies considering a continuous density function of demand, in our paper the customer demand depends on the price of current period and arrives according to a homogeneous Poisson process. We consider both backlogging and lost-sales cases, and our goal is to find a simultaneously ordering and pricing policy to maximize the expected discounted profit over the planning horizon. (...)
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  35.  53
    English Grammar Error Correction Algorithm Based on Classification Model.Shanchun Zhou & Wei Liu - 2021 - Complexity 2021:1-11.
    English grammar error correction algorithm refers to the use of computer programming technology to automatically recognize and correct the grammar errors contained in English text written by nonnative language learners. Classification model is the core of machine learning and data mining, which can be applied to extracting information from English text data and constructing a reliable grammar correction method. On the basis of summarizing and analyzing previous research works, this paper expounded the research status and significance of English grammar error (...)
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  36. Beyond bias and discrimination: redefining the AI ethics principle of fairness in healthcare machine-learning algorithms.Benedetta Giovanola & Simona Tiribelli - 2023 - AI and Society 38 (2):549-563.
    The increasing implementation of and reliance on machine-learning (ML) algorithms to perform tasks, deliver services and make decisions in health and healthcare have made the need for fairness in ML, and more specifically in healthcare ML algorithms (HMLA), a very important and urgent task. However, while the debate on fairness in the ethics of artificial intelligence (AI) and in HMLA has grown significantly over the last decade, the very concept of fairness as an ethical value has not yet been sufficiently (...)
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  37. Social Q&A communities: A multi-factor study of the influence of users’ knowledge sharing behaviors.Yi Wen, Xiaofang Yuan & Wenqin Li - 2022 - Frontiers in Psychology 13.
    Recently, social Q&A communities have grown increasingly popular, serving as a primary platform for people to learn and share information. Nonetheless, fewer knowledge producers in these communities are significant than knowledge consumers. Thus, promoting users’ participation in knowledge sharing is a challenge for managers of social Q&A communities. Even though many scholars have studied factors influencing willingness to share knowledge, they tend to start with one theory and ignore the impact of several factors on behaviors. Thus, this manuscript presents a (...)
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  38.  44
    Optimal Formulation of Complex Chemical Systems with a Genetic Algorithm.Mark A. Bedau - unknown
    We demonstrate a method for optimizing desired functionality in real complex chemical systems, using a genetic algorithm. The chemical systems studied here are mixtures of amphiphiles, which spontaneously exhibit a complex variety of self-assembled molecular aggregations, and the property optimized is turbidity. We also experimentally resolve the fitness landscape in some hyper-planes through the space of possible amphiphile formulations, in order to assess the practicality of our optimization method. Our method shows clear and significant progress after testing only 1 (...)
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  39. An Approach to Subjective Computing: a Robot that Learns from Interaction with Humans.Patrick Grüneberg & Kenji Suzuki - 2014 - Ieee Transactions on Autonomous Mental Development 6 (1):5-18.
    We present an approach to subjective computing for the design of future robots that exhibit more adaptive and flexible behavior in terms of subjective intelligence. Instead of encapsulating subjectivity into higher order states, we show by means of a relational approach how subjective intelligence can be implemented in terms of the reciprocity of autonomous self-referentiality and direct world-coupling. Subjectivity concerns the relational arrangement of an agent’s cognitive space. This theoretical concept is narrowed down to the problem of coaching a (...) learning agent by means of binary feedback. Algorithms are presented that implement subjective computing. The relational characteristic of subjectivity is further confirmed by a questionnaire on human perception of robot’s behavior. The results imply that subjective intelligence cannot be externally observed. In sum, we conclude that subjective intelligence in relational terms is fully tractable and therefore implementable in artificial agents. (shrink)
     
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  40.  27
    Hierarchical clustering optimizes the tradeoff between compositionality and expressivity of task structures for flexible reinforcement learning.Rex G. Liu & Michael J. Frank - 2022 - Artificial Intelligence 312 (C):103770.
  41.  16
    Construction of a financial default risk prediction model based on the LightGBM algorithm.Vipin Balyan & Bo Gao - 2022 - Journal of Intelligent Systems 31 (1):767-779.
    The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base LightGBM model along (...)
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  42.  86
    The structure of intrinsic complexity of learning.Sanjay Jain & Arun Sharma - 1997 - Journal of Symbolic Logic 62 (4):1187-1201.
    Limiting identification of r.e. indexes for r.e. languages (from a presentation of elements of the language) and limiting identification of programs for computable functions (from a graph of the function) have served as models for investigating the boundaries of learnability. Recently, a new approach to the study of "intrinsic" complexity of identification in the limit has been proposed. This approach, instead of dealing with the resource requirements of the learning algorithm, uses the notion of reducibility from recursion theory to compare (...)
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  43.  15
    Foundations of algorithms.Richard E. Neapolitan - 2015 - Burlington, MA: Jones & Bartlett Learning.
    Foundations of Algorithms, Fifth Edition offers a well-balanced presentation of algorithm design, complexity analysis of algorithms, and computational complexity. Ideal for any computer science students with a background in college algebra and discrete structures, the text presents mathematical concepts using standard English and simple notation to maximize accessibility and user-friendliness. Concrete examples, appendices reviewing essential mathematical concepts, and a student-focused approach reinforce theoretical explanations and promote learning and retention. C++ and Java pseudocode help students better understand complex algorithms. A chapter (...)
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  44.  19
    Q-Learning Applied to Genetic Algorithm-Fuzzy Approach for On-Line Control in Autonomous Agents.Hengameh Sarmadi - 2009 - Journal of Intelligent Systems 18 (1-2):1-32.
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  45.  38
    You Mean It’s Not My Fault: Learning about Lipedema, a Fat Disorder.Catherine A. Seo - 2014 - Narrative Inquiry in Bioethics 4 (2):6-9.
    In lieu of an abstract, here is a brief excerpt of the content:You Mean It’s Not My Fault:Learning about Lipedema, a Fat DisorderCatherine A. Seo“As a surgeon there is nothing more I can do for you. You need to lose 75 pounds before I can even consider repairing the damage done.” Implied and not directly stated, “… Because it’s your fault.” I sat listening, dumbfounded. I was at one of the top teaching hospitals in the country, face to (...)
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  46.  61
    Neogenomic events challenge current models of heritability, neuronal plasticity dynamics, and machine learning.Cláudio Eduardo Corrêa Teixeira, Nelson Monte de Carvalho-Filho & Luiz Carlos de Lima Silveira - 2012 - Behavioral and Brain Sciences 35 (5):379-380.
    We address current needs for neogenomics-based theoretical and computational approaches for several neuroscience research fields, from investigations of heritability properties, passing by investigations of spatiotemporal dynamics in the neuromodulatory microcircuits involved in perceptual learning and attentional shifts, to the application of genetic algorithms to create robots exhibiting ongoing emergence.
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  47.  37
    Controle da diversidade da população em algoritmos genéticos aplicados na predição de estruturas de proteínas.Vinicius Tragante do Ó & Renato Tinos - 2009 - Scientia (Brazil) 20 (2):83-93.
    Control of the population diversity in genetic algorithms applied to the protein structure prediction problem. Genetic Algorithms (GAs), a successful approach for optimization problems, usually fail when employed in the standard configuration in the protein structure prediction problem, since the solution space is very large and the population converges before a reasonable percentage of the possible solutions is explored. Thus, this work investigates the effect of increasing the diversity of the population on this problem by using Hypermutation and (...)
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  48. The structure of egocentric space.Adrian J. T. Alsmith - 2020 - In Frédérique de Vignemont (ed.), The World at Our Fingertips: A Multidisciplinary Exploration of Peripersonal Space. Oxford: Oxford University Press.
    This chapter offers an indirect defence of the Evansian conception of egocentric space, by showing how it resolves a puzzle concerning the unity of egocentric spatial perception. The chapter outlines several common assumptions about egocentric perspectival structure and argues that a subject’s experience, both within and across her sensory modalities, may involve multiple structures of this kind. This raises the question of how perspectival unity is achieved, such that these perspectival structures form a complex whole, rather than merely disunified set (...)
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  49. Multi-Agent Reinforcement Learning: Weighting and Partitioning.Ron Sun & Todd Peterson - unknown
    This paper addresses weighting and partitioning in complex reinforcement learning tasks, with the aim of facilitating learning. The paper presents some ideas regarding weighting of multiple agents and extends them into partitioning an input/state space into multiple regions with di erential weighting in these regions, to exploit di erential characteristics of regions and di erential characteristics of agents to reduce the learning complexity of agents (and their function approximators) and thus to facilitate the learning overall. It analyzes, in (...) learning tasks, di erent ways of partitioning a task and using agents selectively based on partitioning. Based on the analysis, some heuristic methods are described and experimentally tested. We nd that some o -line heuristic methods performed the best, signi cantly better than single-agent models. (shrink)
     
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  50.  49
    Decolonization Projects.Cornelius Ewuoso - 2023 - Voices in Bioethics 9.
    Photo ID 279661800 © Sidewaypics|Dreamstime.com ABSTRACT Decolonization is complex, vast, and the subject of an ongoing academic debate. While the many efforts to decolonize or dismantle the vestiges of colonialism that remain are laudable, they can also reinforce what they seek to end. For decolonization to be impactful, it must be done with epistemic and cultural humility, requiring decolonial scholars, project leaders, and well-meaning people to be more sensitive to those impacted by colonization and not regularly included in the discourse. (...)
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