Pub Date : 2024-09-23DOI: 10.1109/TAI.2024.3465441
Manaar Alam;Hithem Lamri;Michail Maniatakos
Federated learning (FL) enables collaborative learning across multiple participants without exposing sensitive personal data. However, the distributed nature of FL and unvetted participants’ data makes it vulnerable to backdoor attacks . In these attacks, adversaries selectively inject malicious functionality into the centralized model during training, leading to intentional misclassifications for specific adversary-chosen inputs. While previous research has demonstrated successful injections of persistent backdoors in FL, the persistence also poses a challenge, as their existence in the centralized model can prompt the central aggregation server to take preventive measures for penalizing the adversaries. Therefore, this article proposes a method that enables adversaries to effectively remove backdoors from the centralized model upon achieving their objectives or upon suspicion of possible detection. The proposed approach extends the concept of machine unlearning and presents strategies to preserve the performance of the centralized model and simultaneously prevent over-unlearning of information unrelated to backdoor patterns, making adversaries stealthy while removing backdoors. To the best of our knowledge, this is the first work exploring machine unlearning in FL to remove backdoors to the benefit of adversaries. Exhaustive evaluation considering various image classification scenarios demonstrates the efficacy of the proposed method for efficient backdoor removal from the centralized model, injected by state-of-the-art attacks across multiple configurations.
{"title":"Get Rid of Your Trail: Remotely Erasing Backdoors in Federated Learning","authors":"Manaar Alam;Hithem Lamri;Michail Maniatakos","doi":"10.1109/TAI.2024.3465441","DOIUrl":"https://doi.org/10.1109/TAI.2024.3465441","url":null,"abstract":"Federated learning (FL) enables collaborative learning across multiple participants without exposing sensitive personal data. However, the distributed nature of FL and unvetted participants’ data makes it vulnerable to \u0000<italic>backdoor attacks</i>\u0000. In these attacks, adversaries selectively inject malicious functionality into the centralized model during training, leading to intentional misclassifications for specific adversary-chosen inputs. While previous research has demonstrated successful injections of persistent backdoors in FL, the persistence also poses a challenge, as their existence in the centralized model can prompt the central aggregation server to take preventive measures for penalizing the adversaries. Therefore, this article proposes a method \u0000<italic>that enables adversaries to effectively remove backdoors from the centralized model</i>\u0000 upon achieving their objectives or upon suspicion of possible detection. The proposed approach extends the concept of \u0000<italic>machine unlearning</i>\u0000 and presents strategies to preserve the performance of the centralized model and simultaneously prevent over-unlearning of information unrelated to backdoor patterns, making adversaries stealthy while removing backdoors. To the best of our knowledge, this is the first work exploring machine unlearning in FL to remove backdoors to the benefit of adversaries. Exhaustive evaluation considering various image classification scenarios demonstrates the efficacy of the proposed method for efficient backdoor removal from the centralized model, injected by state-of-the-art attacks across multiple configurations.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6683-6698"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1109/TAI.2024.3465433
Yu-Ming Zhang;Jun-Wei Hsieh;Chun-Chieh Lee;Kuo-Chin Fan
Manually designed convolutional neural networks (CNNs) architectures such as visual geometry group network (VGG), ResNet, DenseNet, and MobileNet have achieved high performance across various tasks, but design them is time-consuming and costly. Neural architecture search (NAS) automates the discovery of effective CNN architectures, reducing the need for experts. However, evaluating candidate architectures requires significant graphics processing unit (GPU) resources, leading to the use of predictor-based NAS, such as graph convolutional networks (GCN), which is the popular option to construct predictors. However, we discover that, even though the ability of GCN mimics the propagation of features of real architectures, the binary nature of the adjacency matrix limits its effectiveness. To address this, we propose redirection of adjacent trails (RATs), which adaptively learns trail weights within the adjacency matrix. Our RATs-GCN outperform other predictors by dynamically adjusting trail weights after each graph convolution layer. Additionally, the proposed divide search sampling (DSS) strategy, based on the observation of cell-based NAS that architectures with similar floating point operations (FLOPs) perform similarly, enhances search efficiency. Our RATs-NAS, which combine RATs-GCN and DSS, shows significant improvements over other predictor-based NAS methods on NASBench-101, NASBench-201, and NASBench-301.
{"title":"RATs-NAS: Redirection of Adjacent Trails on Graph Convolutional Networks for Predictor-Based Neural Architecture Search","authors":"Yu-Ming Zhang;Jun-Wei Hsieh;Chun-Chieh Lee;Kuo-Chin Fan","doi":"10.1109/TAI.2024.3465433","DOIUrl":"https://doi.org/10.1109/TAI.2024.3465433","url":null,"abstract":"Manually designed convolutional neural networks (CNNs) architectures such as visual geometry group network (VGG), ResNet, DenseNet, and MobileNet have achieved high performance across various tasks, but design them is time-consuming and costly. Neural architecture search (NAS) automates the discovery of effective CNN architectures, reducing the need for experts. However, evaluating candidate architectures requires significant graphics processing unit (GPU) resources, leading to the use of predictor-based NAS, such as graph convolutional networks (GCN), which is the popular option to construct predictors. However, we discover that, even though the ability of GCN mimics the propagation of features of real architectures, the binary nature of the adjacency matrix limits its effectiveness. To address this, we propose redirection of adjacent trails (RATs), which adaptively learns trail weights within the adjacency matrix. Our RATs-GCN outperform other predictors by dynamically adjusting trail weights after each graph convolution layer. Additionally, the proposed divide search sampling (DSS) strategy, based on the observation of cell-based NAS that architectures with similar floating point operations (FLOPs) perform similarly, enhances search efficiency. Our RATs-NAS, which combine RATs-GCN and DSS, shows significant improvements over other predictor-based NAS methods on NASBench-101, NASBench-201, and NASBench-301.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6672-6682"},"PeriodicalIF":0.0,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%–26% and 2%–5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%–11% higher degradation in DR compared to a random selection; 2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%–13% higher degradation in DR compared to traditional FDIAs; and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5 $-$ 53% compared to benchmark detectors against FDIEAs.
{"title":"Spatio-temporal Graph-Based Generation and Detection of Adversarial False Data Injection Evasion Attacks in Smart Grids","authors":"Abdulrahman Takiddin;Muhammad Ismail;Rachad Atat;Erchin Serpedin","doi":"10.1109/TAI.2024.3464511","DOIUrl":"https://doi.org/10.1109/TAI.2024.3464511","url":null,"abstract":"Smart power grids are vulnerable to security threats due to their cyber-physical nature. Existing data-driven detectors aim to address simple traditional false data injection attacks (FDIAs). However, adversarial false data injection evasion attacks (FDIEAs) present a more serious threat as adversaries, with different levels of knowledge about the system, inject adversarial samples to circumvent the grid's attack detection system. The robustness of state-of-the-art graph-based detectors has not been investigated against sophisticated FDIEAs. Hence, this article answers three research questions. 1) What is the impact of utilizing spatio-temporal features to craft adversarial samples and how to select attack nodes? 2) How can adversaries generate surrogate spatio-temporal data when they lack knowledge about the system topology? 3) What are the required model characteristics for a robust detection against adversarial FDIEAs? To answer the questions, we examine the robustness of several detectors against five attack cases and conclude the following: 1) Attack generation with full knowledge using spatio-temporal features leads to 5%–26% and 2%–5% higher degradation in detection rate (DR) compared to traditional FDIAs and using temporal features, respectively, whereas centrality analysis-based attack node selection leads to 3%–11% higher degradation in DR compared to a random selection; 2) Stochastic geometry-based graph generation to create surrogate adversarial topologies and samples leads to 3%–13% higher degradation in DR compared to traditional FDIAs; and 3) Adopting an unsupervised spatio-temporal graph autoencoder (STGAE)-based detector enhances the DR by 5\u0000<inline-formula><tex-math>$-$</tex-math></inline-formula>\u000053% compared to benchmark detectors against FDIEAs.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6601-6616"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1109/TAI.2024.3464510
Yuhang Zhang;Chao Yan;Jiaping Xiao;Mir Feroskhan
Obstacle avoidance under constrained visual perception presents a significant challenge, requiring rapid detection and decision-making within partially observable environments, particularly for unmanned aerial vehicles (UAVs) maneuvering agilely in 3-D space. Compared with traditional methods, obstacle avoidance algorithms based on deep reinforcement learning (DRL) offer a better comprehension of the uncertain operational environment in an end-to-end manner, reducing computational complexity, and enhancing flexibility and scalability. However, the inherent trial-and-error learning mechanism of DRL necessitates numerous iterations for policy convergence, leading to sample inefficiency issues. Meanwhile, existing sample-efficient obstacle avoidance approaches that leverage imitation learning often heavily rely on offline expert demonstrations, which are not always feasible in hazardous environments. To address these challenges, we propose a novel obstacle avoidance approach based on nonexpert policy enhanced DRL (NPE-DRL). This approach integrates a fundamental DRL framework with prior knowledge derived from a nonexpert policy-guided imitation learning. During the training phase, the agent starts by online imitating the actions generated by the nonexpert policy during interactions and progressively shifts toward autonomously exploring the environment to generate the optimal policy. Both simulation and physical experiments validate that our approach improves sample efficiency and achieves a better exploration–exploitation balance in both virtual and real-world flights. Additionally, our NPE-DRL-based obstacle avoidance approach shows better adaptability in complex environments characterized by larger scales and denser obstacle configurations, demonstrating a significant improvement in UAVs’ obstacle avoidance capability. Code available at https://github.com/zzzzzyh111/NonExpert-Guided-Visual-UAV-Navigation-Gazebo.
{"title":"NPE-DRL: Enhancing Perception Constrained Obstacle Avoidance With Nonexpert Policy Guided Reinforcement Learning","authors":"Yuhang Zhang;Chao Yan;Jiaping Xiao;Mir Feroskhan","doi":"10.1109/TAI.2024.3464510","DOIUrl":"https://doi.org/10.1109/TAI.2024.3464510","url":null,"abstract":"Obstacle avoidance under constrained visual perception presents a significant challenge, requiring rapid detection and decision-making within partially observable environments, particularly for unmanned aerial vehicles (UAVs) maneuvering agilely in 3-D space. Compared with traditional methods, obstacle avoidance algorithms based on deep reinforcement learning (DRL) offer a better comprehension of the uncertain operational environment in an end-to-end manner, reducing computational complexity, and enhancing flexibility and scalability. However, the inherent trial-and-error learning mechanism of DRL necessitates numerous iterations for policy convergence, leading to sample inefficiency issues. Meanwhile, existing sample-efficient obstacle avoidance approaches that leverage imitation learning often heavily rely on offline expert demonstrations, which are not always feasible in hazardous environments. To address these challenges, we propose a novel obstacle avoidance approach based on nonexpert policy enhanced DRL (NPE-DRL). This approach integrates a fundamental DRL framework with prior knowledge derived from a nonexpert policy-guided imitation learning. During the training phase, the agent starts by online imitating the actions generated by the nonexpert policy during interactions and progressively shifts toward autonomously exploring the environment to generate the optimal policy. Both simulation and physical experiments validate that our approach improves sample efficiency and achieves a better exploration–exploitation balance in both virtual and real-world flights. Additionally, our NPE-DRL-based obstacle avoidance approach shows better adaptability in complex environments characterized by larger scales and denser obstacle configurations, demonstrating a significant improvement in UAVs’ obstacle avoidance capability. Code available at <uri>https://github.com/zzzzzyh111/NonExpert-Guided-Visual-UAV-Navigation-Gazebo</uri>.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"184-198"},"PeriodicalIF":0.0,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/TAI.2024.3461630
Tianyu Zhu;Xinli Shi;Xiangping Xu;Jinde Cao
The utilization of neural network models for solving combinatorial optimization problems (COPs) has gained significant attention in recent years and has demonstrated encouraging outcomes in addressing analogous problems such as the traveling salesman problem (TSP). The multiple TSP (MTSP) has sparked the interest of researchers as a special kind of COPs. The colored TSP (CTSP) is a variation of the MTSP, which utilizes colors to distinguish the accessibility of cities to salesmen. This article proposes a gated entropy-insensitive attention model (GEIAM) to solve CTSP. In specific, the original problem is first modeled as a sequence and preprocessed by the problem feature extraction network of the model, and then solved by the autoregressive solution constructor subsequently. The policy (parameters of the neural network model) is trained via reinforcement learning (RL). The proposed approach is compared with several commercial solvers as well as heuristics and demonstrates superior solving speed with comparable solution quality.
{"title":"Reinforcement Learning for Solving Colored Traveling Salesman Problems: An Entropy-Insensitive Attention Approach","authors":"Tianyu Zhu;Xinli Shi;Xiangping Xu;Jinde Cao","doi":"10.1109/TAI.2024.3461630","DOIUrl":"https://doi.org/10.1109/TAI.2024.3461630","url":null,"abstract":"The utilization of neural network models for solving combinatorial optimization problems (COPs) has gained significant attention in recent years and has demonstrated encouraging outcomes in addressing analogous problems such as the traveling salesman problem (TSP). The multiple TSP (MTSP) has sparked the interest of researchers as a special kind of COPs. The colored TSP (CTSP) is a variation of the MTSP, which utilizes colors to distinguish the accessibility of cities to salesmen. This article proposes a gated entropy-insensitive attention model (GEIAM) to solve CTSP. In specific, the original problem is first modeled as a sequence and preprocessed by the problem feature extraction network of the model, and then solved by the autoregressive solution constructor subsequently. The policy (parameters of the neural network model) is trained via reinforcement learning (RL). The proposed approach is compared with several commercial solvers as well as heuristics and demonstrates superior solving speed with comparable solution quality.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6699-6708"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/TAI.2024.3433614
Prachi Pratyusha Sahoo;Kyriakos G. Vamvoudakis
In this article, we provide a model-free reinforcement learning (RL) framework that relies on internal reinforcement signals, called self-model-free RL, for learning agents that experience loss of the reinforcement signals in the form of packet drops and/or jamming attacks by malicious agents. The framework embeds a correcting mechanism in the form of a goal network to compensate for information loss and produce optimal and stabilizing policies. It also provides a trade-off scheme that reconstructs the reward using a goal network whenever the reinforcement signals are lost but utilizes true reinforcement signals when they are available. The stability of the equilibrium point is guaranteed despite fractional information loss in the reinforcement signals. Finally, simulation results validate the efficacy of the proposed work.
{"title":"Self-Model-Free Learning Versus Learning With External Rewards in Information Constrained Environments","authors":"Prachi Pratyusha Sahoo;Kyriakos G. Vamvoudakis","doi":"10.1109/TAI.2024.3433614","DOIUrl":"https://doi.org/10.1109/TAI.2024.3433614","url":null,"abstract":"In this article, we provide a model-free reinforcement learning (RL) framework that relies on internal reinforcement signals, called self-model-free RL, for learning agents that experience loss of the reinforcement signals in the form of packet drops and/or jamming attacks by malicious agents. The framework embeds a correcting mechanism in the form of a goal network to compensate for information loss and produce optimal and stabilizing policies. It also provides a trade-off scheme that reconstructs the reward using a goal network whenever the reinforcement signals are lost but utilizes true reinforcement signals when they are available. The stability of the equilibrium point is guaranteed despite fractional information loss in the reinforcement signals. Finally, simulation results validate the efficacy of the proposed work.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6566-6579"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.1109/TAI.2024.3457931
Fei Teng;Jiaming Zhang;Kunyu Peng;Yaonan Wang;Rainer Stiefelhagen;Kailun Yang
Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an omni-aperture fusion model (OAFuser) that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective subaperture fusion module (SAFM) . This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only ( ${sim}1rm GFlops$ ). Furthermore, to address the mismatched spatial information across viewpoints, we present a center angular rectification module (CARM) to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of all evaluation metrics and sets a new record of $84.93%$ in mIoU on the UrbanLF-Real Extended dataset, with a gain of ${+}3.69%$ . The source code for OAFuser is available at https://github.com/FeiBryantkit/OAFuser .
{"title":"OAFuser: Toward Omni-Aperture Fusion for Light Field Semantic Segmentation","authors":"Fei Teng;Jiaming Zhang;Kunyu Peng;Yaonan Wang;Rainer Stiefelhagen;Kailun Yang","doi":"10.1109/TAI.2024.3457931","DOIUrl":"https://doi.org/10.1109/TAI.2024.3457931","url":null,"abstract":"Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: 1) The extensive angular information of light field cameras contains a large amount of redundant data, which is overwhelming for the limited hardware resources of intelligent agents. 2) A relative displacement difference exists in the data collected by different microlenses. To address these issues, we propose an \u0000<italic>omni-aperture fusion model (OAFuser)</i>\u0000 that leverages dense context from the central view and extracts the angular information from subaperture images to generate semantically consistent results. To simultaneously streamline the redundant information from the light field cameras and avoid feature loss during network propagation, we present a simple yet very effective \u0000<italic>subaperture fusion module (SAFM)</i>\u0000. This module efficiently embeds subaperture images in angular features, allowing the network to process each subaperture image with a minimal computational demand of only (\u0000<inline-formula><tex-math>${sim}1rm GFlops$</tex-math></inline-formula>\u0000). Furthermore, to address the mismatched spatial information across viewpoints, we present a \u0000<italic>center angular rectification module (CARM)</i>\u0000 to realize feature resorting and prevent feature occlusion caused by misalignment. The proposed OAFuser achieves state-of-the-art performance on four UrbanLF datasets in terms of \u0000<italic>all evaluation metrics</i>\u0000 and sets a new record of \u0000<inline-formula><tex-math>$84.93%$</tex-math></inline-formula>\u0000 in mIoU on the UrbanLF-Real Extended dataset, with a gain of \u0000<inline-formula><tex-math>${+}3.69%$</tex-math></inline-formula>\u0000. The source code for OAFuser is available at \u0000<uri>https://github.com/FeiBryantkit/OAFuser</uri>\u0000.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 12","pages":"6225-6239"},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142810372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1109/TAI.2024.3439048
Hussein Abbass;Keeley Crockett;Jonathan Garibaldi;Alexander Gegov;Uzay Kaymak;Joao Miguel C. Sousa
{"title":"Editorial: From Explainable Artificial Intelligence (xAI) to Understandable Artificial Intelligence (uAI)","authors":"Hussein Abbass;Keeley Crockett;Jonathan Garibaldi;Alexander Gegov;Uzay Kaymak;Joao Miguel C. Sousa","doi":"10.1109/TAI.2024.3439048","DOIUrl":"https://doi.org/10.1109/TAI.2024.3439048","url":null,"abstract":"","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"5 9","pages":"4310-4314"},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10673750","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-09DOI: 10.1109/TAI.2024.3456086
Fangfang Zhang;Gaofeng Shi;Yi Mei;Mengjie Zhang
Dynamic flexible job shop scheduling is an important combinatorial optimization problem that has rich real-world applications such as product processing in manufacturing. Genetic programming has been successfully used to learn scheduling heuristics for dynamic flexible job shop scheduling. Intuitively, users prefer small and effective scheduling heuristics that can not only generate promising schedules but also are computationally efficient and easy to be understood. However, a scheduling heuristic with better effectiveness tends to have a larger size, and the effectiveness of rules and rule size are potentially conflicting objectives. With the traditional dominance relation-based multiobjective algorithms, there is a search bias toward rule size, since rule size is much easier to optimized than effectiveness, and larger rules are easily abandoned, resulting in the loss of effectiveness. To address this issue, this article develops a novel multiobjective genetic programming algorithm that takes size and effectiveness of scheduling heuristics for optimization via multitask learning mechanism. Specifically, we construct two tasks for the multiobjective optimization with biased objectives using different search mechanisms for each task. The focus of the proposed algorithm is to improve the effectiveness of learned small rules by knowledge sharing between constructed tasks which is implemented with the crossover operator. The results show that our proposed algorithm performs significantly better, i.e., with smaller and more effective scheduling heuristics, than the state-of-the-art algorithms in the examined scenarios. By analyzing the population diversity, we find that the proposed algorithm has a good balance between exploration and exploitation during the evolutionary process.
{"title":"Multiobjective Dynamic Flexible Job Shop Scheduling With Biased Objectives via Multitask Genetic Programming","authors":"Fangfang Zhang;Gaofeng Shi;Yi Mei;Mengjie Zhang","doi":"10.1109/TAI.2024.3456086","DOIUrl":"https://doi.org/10.1109/TAI.2024.3456086","url":null,"abstract":"Dynamic flexible job shop scheduling is an important combinatorial optimization problem that has rich real-world applications such as product processing in manufacturing. Genetic programming has been successfully used to learn scheduling heuristics for dynamic flexible job shop scheduling. Intuitively, users prefer small and effective scheduling heuristics that can not only generate promising schedules but also are computationally efficient and easy to be understood. However, a scheduling heuristic with better effectiveness tends to have a larger size, and the effectiveness of rules and rule size are potentially conflicting objectives. With the traditional dominance relation-based multiobjective algorithms, there is a search bias toward rule size, since rule size is much easier to optimized than effectiveness, and larger rules are easily abandoned, resulting in the loss of effectiveness. To address this issue, this article develops a novel multiobjective genetic programming algorithm that takes size and effectiveness of scheduling heuristics for optimization via multitask learning mechanism. Specifically, we construct two tasks for the multiobjective optimization with biased objectives using different search mechanisms for each task. The focus of the proposed algorithm is to improve the effectiveness of learned small rules by knowledge sharing between constructed tasks which is implemented with the crossover operator. The results show that our proposed algorithm performs significantly better, i.e., with smaller and more effective scheduling heuristics, than the state-of-the-art algorithms in the examined scenarios. By analyzing the population diversity, we find that the proposed algorithm has a good balance between exploration and exploitation during the evolutionary process.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"169-183"},"PeriodicalIF":0.0,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142976034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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