Article
Business
Pieter den Hamer, Koen Frenken
Summary: The proposed model suggests that firms rely on local search for exploitation and on imitation for exploration. Successful imitation generally occurs at an intermediate level of cognitive proximity, and social and cognitive proximity are substitutes. The model also shows that exploration by imitation is more beneficial in highly complex industries and that small-world networks yield the highest benefits for collective learning.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Automation & Control Systems
Wen-Hua Chen, Callum Rhodes, Cunjia Liu
Summary: This paper introduces an optimal autonomous search framework called DCEE for target localization in unknown environments. By using Bayesian inference to estimate target location and taking control actions to minimize error, DCEE can achieve optimal trade-off between exploitation and exploration while reducing estimation uncertainty.
Article
Computer Science, Artificial Intelligence
Kutalmi Coskun, Borahan Tumer
Summary: Modeling and analysis of dynamic systems are crucial for addressing complex real-world problems. This paper proposes a stochastic learning method that can handle non-stationarity and detect changes in stability using a statistical model. Experimental results demonstrate the effectiveness of this method in different types of drifts.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Min Li, Tianyi Huang, William Zhu
Summary: This research proposes an adaptive exploration policy to address the exploration-exploitation tradeoff by adjusting the exploration noise based on training stability. The effectiveness of this policy is demonstrated through theoretical analysis and experiments.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2021)
Article
Robotics
Thomas Lew, Apoorva Sharma, James Harrison, Andrew Bylard, Marco Pavone
Summary: This article introduces a method using Bayesian meta-learning and last-layer adaptation to maintain safety in the presence of dynamic uncertainty. By combining online last-layer adaptation with tight confidence sets, safe trajectory planning is achieved to ensure system safety.
IEEE TRANSACTIONS ON ROBOTICS
(2022)
Article
Automation & Control Systems
Onder Tutsoy, Duygun Erol Barkana, Kemal Balikci
Summary: This article introduces a model-free control approach that achieves learning and control goals by constructing a comprehensive exploration-exploitation policy. It also proposes a completely model-free adaptive law that considers control signal saturation and delay. In practical experiments, the algorithm achieves good results on a challenging benchmark system.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhongguo Li, Wen-Hua Chen, Jun Yang
Summary: A concurrent learning framework is proposed for source search using autonomous platforms equipped with onboard sensors in an unknown environment. The proposed solution is computationally affordable and utilizes multiple parallel estimators to learn the operational environment and quantify estimation uncertainty.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Psychology, Biological
Nadescha Trudel, Jacqueline Scholl, Miriam C. Klein-Flugge, Elsa Fouragnan, Lev Tankelevitch, Marco K. Wittmann, Matthew F. S. Rushworth
Summary: In a study conducted by Trudel et al., it was found that the ventromedial prefrontal cortex carries multiple decision variables with varying strength and polarity depending on the behavioral context. Initially, participants tend to select predictors with higher uncertainty, but as time progresses, they shift towards more accurate predictors and avoid uncertain ones. This transition is accompanied by changes in representations of belief uncertainty in the vmPFC.
NATURE HUMAN BEHAVIOUR
(2021)
Article
Geosciences, Multidisciplinary
Hemin Yuan, Yun Wang, Xiangchun Wang
Summary: This work reviews the roles of seismic and rock physics in gas hydrate exploration and production, summarizing commonly used seismic techniques, rock physics models, and production methods. The study outlines the workflow for identifying gas hydrate formations, linking micro-scale properties to macro-scale seismic velocities, and addresses potential uncertainties. It also discusses problems in gas hydrate production and suggests using geophysical techniques to solve exploration and production challenges.
JOURNAL OF EARTH SCIENCE
(2021)
Editorial Material
Multidisciplinary Sciences
Eddy Dib, Edwin B. Clatworthy, Hugo Cruchade, Izabel C. Medeiros-Costa, Nikolai Nesterenko, Jean-Pierre Gilson, Svetlana Mintova
Summary: The precise location and role of hydroxyls in zeolites are still unknown, but controlling them can lead to tailored catalysts and adsorbents with novel properties, thus increasing efficiency in industrial processes for cleaner energy.
NATIONAL SCIENCE REVIEW
(2022)
Article
Computer Science, Artificial Intelligence
David Wittenberg, Franz Rothlauf, Christian Gagne
Summary: This study investigates the influence of corruption and sampling steps on a neural network-based genetic programming approach. The results show that both corruption strength and the number of sampling steps affect exploration and exploitation in search, and also impact performance.
GENETIC PROGRAMMING AND EVOLVABLE MACHINES
(2023)
Article
Robotics
Hannes Eschmann, Henrik Ebel, Peter Eberhard
Summary: This paper proposes an iterative learning-based procedure for highly accurate tracking, using techniques like Gaussian process regression to tailor a motion model based on a specific recurring reference. The approach enables explorative behavior to automatically explore states around the prescribed trajectory, enriching the dataset and improving robustness and practical training accuracy. An optimization-based reference generator balances the trade-off between accurate tracking and exploration by minimizing the posterior variance of the underlying Gaussian process model. While the study focuses on omnidirectional mobile robots, the scheme can be applied to a wide range of mobile robots. Real-world experiments on a custom-built omnidirectional mobile robot validate the effectiveness of this approach, showing that explorative behavior can outperform purely exploitative approaches.
Article
Computer Science, Interdisciplinary Applications
Giuseppe Brunetti, Christine Stumpp, Jiri Simunek
Summary: This paper proposes a hybrid strategy, G-CLPSO, which combines global search properties with exploitation capability for solving optimization problems in hydrological modeling. The results from benchmark tests and synthetic modeling scenarios demonstrate that G-CLPSO outperforms other methods in terms of accuracy and convergence.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Statistics & Probability
Soren Christensen, Claudia Strauch
Summary: The paper aims to combine techniques from stochastic control with methods from statistics for stochastic processes to learn the dynamics of the underlying process and control it in a reasonable manner. By studying a long-term average impulse control problem, the authors propose a solution to the exploration-exploitation dilemma and find that it can be based on the convergence rates of estimators for the invariant density.
ANNALS OF APPLIED PROBABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Qihang Chen, Qiwei Zhang, Yunlong Liu
Summary: One of the major challenges in reinforcement learning is the sparse and delayed rewards in episodic tasks. The existing techniques have difficulties in assigning credits to explored transitions or are misled by behavioral policies, leading to sluggish learning efficiency. To address this, we propose an approach called EMR, which combines intrinsic rewards of exploration mechanisms with reward redistribution to balance exploration and exploitation in such tasks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Tomaz Kosar, Saso Gaberc, Jeffrey C. Carver, Marjan Mernik
EMPIRICAL SOFTWARE ENGINEERING
(2018)
Article
Computer Science, Software Engineering
Tomaz Kosar, Sudev Bohra, Marjan Mernik
JOURNAL OF SYSTEMS AND SOFTWARE
(2018)
Article
Mathematics
Matej Crepinsek, Miha Ravber, Marjan Mernik, Tomaz Kosar
Article
Chemistry, Multidisciplinary
Tomaz Kos, Marjan Mernik, Tomaz Kosar
APPLIED SCIENCES-BASEL
(2019)
Article
Mathematics
Matej Crepinsek, Shih-Hsi Liu, Marjan Mernik, Miha Ravber
Article
Mathematics
Marko Jesenik, Marjan Mernik, Mladen Trlep
Article
Mathematics
Zeljko Kovacevic, Marjan Mernik, Miha Ravber, Matej Crepinsek
Article
Chemistry, Multidisciplinary
Tomaz Kosar, Zhenli Lu, Marjan Mernik, Marjan Horvat, Matej Crepinsek
Summary: Rehabilitation aids are crucial for people with disabilities, but they are often expensive. Advances in software, IoT, robotics, and additive manufacturing have made affordable rehabilitation solutions possible. The paper introduces a comprehensive rehabilitation platform concept and demonstrates it with a unique platform called RehabHand, supporting custom rehabilitation exercises and hardware integration.
APPLIED SCIENCES-BASEL
(2021)
Article
Mathematics
Mihael Baketaric, Marjan Mernik, Tomaz Kosar
Summary: Attraction basins in discrete domains are better understood than those in continuous domains. Research on attraction basins in dynamic problems is scarce. Multi-objective problems are poorly investigated in both domains, with slightly more focus on continuous domains. There is a lack of parallel and scalable algorithms for computing attraction basins, as well as a general framework to unify different definitions/implementations.
Article
Computer Science, Artificial Intelligence
Miha Ravber, Shih-Hsi Liu, Marjan Mernik, Matej Crepinsek
Summary: Evolutionary algorithms are effective in solving complex optimization problems, leading to the development of more efficient algorithms. Comparing these algorithms is a complex task, and stopping criteria play a vital role in ensuring fair and unbiased comparisons. This paper focuses on the impact of stopping criteria and shows that they can significantly affect the rankings of evolutionary algorithms.
APPLIED SOFT COMPUTING
(2022)
Article
Mathematics
Ziga Leber, Matej Crepinsek, Marjan Mernik, Tomaz Kosar
Summary: The limitations of traditional parsing architecture are well known. This paper presents a novel scanner-based architecture that overcomes these limitations and allows for parsing of all character-level context-free languages. The architecture is based on right-nulled generalized LR parsing and has unbounded parser and scanner lookahead, making it suitable for streaming parsing.
Article
Mathematics
Bostjan Slivnik, Zeljko Kovacevic, Marjan Mernik, Tomaz Kosar
Summary: The research found that code generated by Genetic Programming is significantly harder to comprehend than manually written ones, indicating a difficulty in automatic program generation.
Article
Chemistry, Multidisciplinary
Tomaz Kos, Marjan Mernik, Tomaz Kosar
Summary: This study introduces a new DSML language RT-Sequencer to support modeling of Real-Time Control systems, which evolved by introducing advanced end-users and general-purpose programming language. By allowing the insertion of GPL code into the model, the DSML was extended to adapt and optimize execution code for specific tasks.
APPLIED SCIENCES-BASEL
(2022)
Article
Mathematics
Jeewaka Perera, Shih-Hsi Liu, Marjan Mernik, Matej Crepinsek, Miha Ravber
Summary: This paper introduces a multi-objective deep graph pointer network-based reinforcement learning (MODGRL) algorithm for solving multi-objective TSPs. MODGRL improves an earlier deep reinforcement learning algorithm, called DRL-MOA, by utilizing a graph pointer network to learn the graphical structures of TSPs. The results show that MODGRL outperforms the competitors on convergence and diversity measured by the hypervolume indicator.
Article
Mathematics
Bostjan Slivnik, Marjan Mernik
Summary: This is a description of a new parsing method based on the semi-Thue system, which is more efficient than traditional algorithms and can be used for parsing any recursively enumerable language. It can simulate various existing parsing algorithms and is primarily used for programming and domain-specific languages.
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)