Emma Stensby Norstein;Kotaro Yasui;Takeshi Kano;Akio Ishiguro;Kyrre Glette
Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.
{"title":"Behaviour Diversity in a Walking and Climbing Centipede-Like Virtual Creature","authors":"Emma Stensby Norstein;Kotaro Yasui;Takeshi Kano;Akio Ishiguro;Kyrre Glette","doi":"10.1162/artl_a_00476","DOIUrl":"10.1162/artl_a_00476","url":null,"abstract":"Robot controllers are often optimized for a single robot in a single environment. This approach proves brittle, as such a controller will often fail to produce sensible behavior for a new morphology or environment. In comparison, animal gaits are robust and versatile. By observing animals, and attempting to extract general principles of locomotion from their movement, we aim to design a single, decentralized controller applicable to diverse morphologies and environments. The controller implements the three components of (a) undulation, (b) peristalsis, and (c) leg motion, which we believe are the essential elements in most animal gaits. This work is a first step toward a general controller. Accordingly, the controller has been evaluated on a limited range of simulated centipede-like robot morphologies. The centipede is chosen as inspiration because it moves using both body contractions and legged locomotion. For a controller to work in qualitatively different settings, it must also be able to exhibit qualitatively different behaviors. We find that six different modes of locomotion emerge from our controller in response to environmental and morphological changes. We also find that different parts of the centipede model can exhibit different modes of locomotion, simultaneously, based on local morphological features. This controller can potentially aid in the design or evolution of robots, by quickly testing the potential of a morphology, or be used to get insights about underlying locomotion principles in the centipede.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"321-344"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Word From the Editors.","authors":"Alan Dorin, Susan Stepney","doi":"10.1162/ARTL.e.11","DOIUrl":"https://doi.org/10.1162/ARTL.e.11","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"249"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The scientific fields of complexity, Artificial Life (ALife), and artificial intelligence (AI) share commonalities: historic, conceptual, methodological, and philosophical. Although their origins trace back to the 1940s birth of cybernetics, they were able to develop properly only as modern information technology became available. In this perspective, I offer a personal (and thus biased) account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and AI. Even when the trajectory of the article is influenced by my personal experience, the general questions posed (which outweigh the answers) will, I hope, be useful in aligning efforts in these fields toward overcoming—or accepting—their limits.
{"title":"Complexity, Artificial Life, and Artificial Intelligence","authors":"Carlos Gershenson","doi":"10.1162/artl_a_00462","DOIUrl":"10.1162/artl_a_00462","url":null,"abstract":"The scientific fields of complexity, Artificial Life (ALife), and artificial intelligence (AI) share commonalities: historic, conceptual, methodological, and philosophical. Although their origins trace back to the 1940s birth of cybernetics, they were able to develop properly only as modern information technology became available. In this perspective, I offer a personal (and thus biased) account of the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and AI. Even when the trajectory of the article is influenced by my personal experience, the general questions posed (which outweigh the answers) will, I hope, be useful in aligning efforts in these fields toward overcoming—or accepting—their limits.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"289-303"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11154117","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142689715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents Benefit Game 2.0, a multiscreen Artificial Life gameplay installation. Saccharina latissima, a seaweed species economically beneficial to humans but threatened by overexploitation, motivates the creation of this artwork. Technically, the authors create an underwater virtual ecosystem consisting of a seaweed swarm and symbiotic fungi, created using procedural content generation via machine learning and rule-based methods. Moreover, the work features a unique cybernetic loop structure, incorporating audience observation and game token interactions. This virtual system is also symbolically influenced in real time by indoor carbon dioxide measurements, serving as an artistic metaphor for the broader impacts of climate change. This integration with the physical game machine underscores the fragile relationship between human activities and the environment under severe global climate change and immerses the audience in the challenging balance between sustainability and profit seeking in this context.
这篇文章介绍了Benefit Game 2.0，一个多屏幕人工生命游戏装置。Saccharina latatissima是一种对人类经济有益但受到过度开发威胁的海藻，它激发了这幅作品的创作动机。从技术上讲，作者创建了一个由海藻群和共生真菌组成的水下虚拟生态系统，通过机器学习和基于规则的方法使用程序内容生成创建。此外，该作品具有独特的控制论循环结构，结合了观众观察和游戏代币互动。这个虚拟系统还象征性地受到室内二氧化碳测量的实时影响，作为气候变化更广泛影响的艺术隐喻。这种与物理游戏机的结合强调了在严重的全球气候变化下，人类活动与环境之间的脆弱关系，并使观众沉浸在可持续发展与追求利润之间的挑战性平衡中。
{"title":"Benefit Game 2.0: Alien Seaweed Swarms—Exploring the Interplay of Human Activity and Environmental Sustainability","authors":"Dan-Lu Fei;Zi-Wei Wu;Kang Zhang","doi":"10.1162/artl_a_00468","DOIUrl":"10.1162/artl_a_00468","url":null,"abstract":"This article presents Benefit Game 2.0, a multiscreen Artificial Life gameplay installation. Saccharina latissima, a seaweed species economically beneficial to humans but threatened by overexploitation, motivates the creation of this artwork. Technically, the authors create an underwater virtual ecosystem consisting of a seaweed swarm and symbiotic fungi, created using procedural content generation via machine learning and rule-based methods. Moreover, the work features a unique cybernetic loop structure, incorporating audience observation and game token interactions. This virtual system is also symbolically influenced in real time by indoor carbon dioxide measurements, serving as an artistic metaphor for the broader impacts of climate change. This integration with the physical game machine underscores the fragile relationship between human activities and the environment under severe global climate change and immerses the audience in the challenging balance between sustainability and profit seeking in this context.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 3","pages":"304-320"},"PeriodicalIF":1.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143652032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Central to the Artificial Life endeavor is the creation of artificial systems that spontaneously generate properties found in the living world, such as autopoiesis, self-replication, evolution, and open-endedness. Though numerous models and paradigms have been proposed, cellular automata (CA) have taken a very important place in the field, notably because they enable the study of phenomena like self-reproduction and autopoiesis. Continuous CA like Lenia have been shown to produce lifelike patterns reminiscent, from both aesthetic and ontological points of view, of biological organisms we call “creatures.” We propose Flow-Lenia, a mass conservative extension of Lenia. We present experiments demonstrating its effectiveness in generating spatially localized patterns with complex behaviors and show that the update rule parameters can be optimized to generate complex creatures showing behaviors of interest. Furthermore, we show that Flow-Lenia allows us to embed the parameters of the model, defining the properties of the emerging patterns, within its own dynamics, thus allowing for multispecies simulation. Using the evolutionary activity framework and other metrics, we shed light on the emergent evolutionary dynamics taking place in this system.
{"title":"Flow-Lenia: Emergent Evolutionary Dynamics in Mass Conservative Continuous Cellular Automata","authors":"Erwan Plantec;Gautier Hamon;Mayalen Etcheverry;Bert Wang-Chak Chan;Pierre-Yves Oudeyer;Clément Moulin-Frier","doi":"10.1162/artl_a_00471","DOIUrl":"10.1162/artl_a_00471","url":null,"abstract":"Central to the Artificial Life endeavor is the creation of artificial systems that spontaneously generate properties found in the living world, such as autopoiesis, self-replication, evolution, and open-endedness. Though numerous models and paradigms have been proposed, cellular automata (CA) have taken a very important place in the field, notably because they enable the study of phenomena like self-reproduction and autopoiesis. Continuous CA like Lenia have been shown to produce lifelike patterns reminiscent, from both aesthetic and ontological points of view, of biological organisms we call “creatures.” We propose Flow-Lenia, a mass conservative extension of Lenia. We present experiments demonstrating its effectiveness in generating spatially localized patterns with complex behaviors and show that the update rule parameters can be optimized to generate complex creatures showing behaviors of interest. Furthermore, we show that Flow-Lenia allows us to embed the parameters of the model, defining the properties of the emerging patterns, within its own dynamics, thus allowing for multispecies simulation. Using the evolutionary activity framework and other metrics, we shed light on the emergent evolutionary dynamics taking place in this system.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"228-248"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PCs are complicated. Yet, being generally more effective, they have replaced typewriters in everyday life. Because of their complications, many of us wonder at PCs as if they were mysterious ghosts in the machine: entities with powers we cannot explain or control, almost supernatural. I analyze how this increase in technological complication may be limiting our society at two levels, one economic and one scientific, and I discuss how the field of Artificial Life (ALife) can attempt to rescue it. At the economic level, there is evidence that computers, being complicated, slow labor productivity rather than increasing it (e.g., maintenance, malware, distractions). Computers are also the subject of debate surrounding technological unemployment and elite overproduction. I advocate for ALife to focus on minimally intrusive developments to our everyday work and to occupy unfilled economic niches, like xenobots or bacterial biofilms. At the scientific level, the surge in artificial intelligence has resulted in many complex algorithms that mimic the cognition happening in brains: Even their creators struggle to make sense of them. I advocate for ALife to focus more on basal forms of cognition, cognition that requires as little “brain” as possible, potentially none—algorithms that think through their bodies, stripped of any superfluous complications, just like typewriters. Ultimately, my goal is for the reader to ask themselves what values should drive ALife.
{"title":"Of Typewriters and PCs: How the Complication of Computers Limits Us and What to Do About It","authors":"Federico Pigozzi","doi":"10.1162/artl_a_00472","DOIUrl":"10.1162/artl_a_00472","url":null,"abstract":"PCs are complicated. Yet, being generally more effective, they have replaced typewriters in everyday life. Because of their complications, many of us wonder at PCs as if they were mysterious ghosts in the machine: entities with powers we cannot explain or control, almost supernatural. I analyze how this increase in technological complication may be limiting our society at two levels, one economic and one scientific, and I discuss how the field of Artificial Life (ALife) can attempt to rescue it. At the economic level, there is evidence that computers, being complicated, slow labor productivity rather than increasing it (e.g., maintenance, malware, distractions). Computers are also the subject of debate surrounding technological unemployment and elite overproduction. I advocate for ALife to focus on minimally intrusive developments to our everyday work and to occupy unfilled economic niches, like xenobots or bacterial biofilms. At the scientific level, the surge in artificial intelligence has resulted in many complex algorithms that mimic the cognition happening in brains: Even their creators struggle to make sense of them. I advocate for ALife to focus more on basal forms of cognition, cognition that requires as little “brain” as possible, potentially none—algorithms that think through their bodies, stripped of any superfluous complications, just like typewriters. Ultimately, my goal is for the reader to ask themselves what values should drive ALife.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"195-210"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hiroyuki Iizuka;Keisuke Suzuki;Reiji Suzuki;Eduardo J. Izquierdo;Manuel Baltieri
{"title":"Editorial Introduction to the 2023 Conference on Artificial Life Special Issue","authors":"Hiroyuki Iizuka;Keisuke Suzuki;Reiji Suzuki;Eduardo J. Izquierdo;Manuel Baltieri","doi":"10.1162/artl_e_00474","DOIUrl":"10.1162/artl_e_00474","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"125-128"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task. We show that unconstrained meta-learning leads to the emergence of diverse plasticity rules. However, regularization and bottlenecks in the model help reduce this variability, resulting in interpretable rules. Our findings indicate that the meta-learning of plasticity rules is very sensitive to various parameters, with this sensitivity possibly reflected in the learning rules found in biological networks. When included in models, these dependencies can be used to discover potential objective functions and details of biological learning via comparisons with experimental observations.
{"title":"Network Bottlenecks and Task Structure Control the Evolution of Interpretable Learning Rules in a Foraging Agent","authors":"Emmanouil Giannakakis;Sina Khajehabdollahi;Anna Levina","doi":"10.1162/artl_a_00458","DOIUrl":"10.1162/artl_a_00458","url":null,"abstract":"Developing reliable mechanisms for continuous local learning is a central challenge faced by biological and artificial systems. Yet, how the environmental factors and structural constraints on the learning network influence the optimal plasticity mechanisms remains obscure even for simple settings. To elucidate these dependencies, we study meta-learning via evolutionary optimization of simple reward-modulated plasticity rules in embodied agents solving a foraging task. We show that unconstrained meta-learning leads to the emergence of diverse plasticity rules. However, regularization and bottlenecks in the model help reduce this variability, resulting in interpretable rules. Our findings indicate that the meta-learning of plasticity rules is very sensitive to various parameters, with this sensitivity possibly reflected in the learning rules found in biological networks. When included in models, these dependencies can be used to discover potential objective functions and details of biological learning via comparisons with experimental observations.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"177-194"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Insect-inspired navigation strategies have the potential to unlock robotic navigation in power-constrained scenarios, as they can function effectively with limited computational resources. One such strategy, familiarity-based navigation, has successfully navigated a robot along routes of up to 60 m using a single-layer neural network trained with an Infomax learning rule. Given the small size of the network that effectively encodes the route, here we investigate the limits of this method, challenging it to navigate longer routes, investigating the relationship between performance, view acquisition rate and dimension, network size, and robustness to noise. Our goal is both to determine the parameters at which this method operates effectively and to explore the profile with which it fails, both to inform theories of insect navigation and to improve robotic deployments. We show that effective memorization of familiar views is possible for longer routes than previously attempted, but that this length decreases for reduced input view dimensions. We also show that the ideal view acquisition rate must be increased with route length for consistent performance. We further demonstrate that computational and memory savings may be made with equivalent performance by reducing the network size—an important consideration for applicability to small, lower-power robots—and investigate the profile of memory failure, demonstrating increased confusion across the route as it extends in length. In this extension to previous work, we also investigate the form taken by the network weights as training extends and the areas of the image on which visual familiarity–based navigation most relies. Additionally, we investigate the robustness of familiarity-based navigation to view variation caused by noise.
{"title":"Investigating the Limits of Familiarity-Based Navigation","authors":"Amany Azevedo Amin;Efstathios Kagioulis;Norbert Domcsek;Thomas Nowotny;Paul Graham;Andrew Philippides","doi":"10.1162/artl_a_00459","DOIUrl":"10.1162/artl_a_00459","url":null,"abstract":"Insect-inspired navigation strategies have the potential to unlock robotic navigation in power-constrained scenarios, as they can function effectively with limited computational resources. One such strategy, familiarity-based navigation, has successfully navigated a robot along routes of up to 60 m using a single-layer neural network trained with an Infomax learning rule. Given the small size of the network that effectively encodes the route, here we investigate the limits of this method, challenging it to navigate longer routes, investigating the relationship between performance, view acquisition rate and dimension, network size, and robustness to noise. Our goal is both to determine the parameters at which this method operates effectively and to explore the profile with which it fails, both to inform theories of insect navigation and to improve robotic deployments. We show that effective memorization of familiar views is possible for longer routes than previously attempted, but that this length decreases for reduced input view dimensions. We also show that the ideal view acquisition rate must be increased with route length for consistent performance. We further demonstrate that computational and memory savings may be made with equivalent performance by reducing the network size—an important consideration for applicability to small, lower-power robots—and investigate the profile of memory failure, demonstrating increased confusion across the route as it extends in length. In this extension to previous work, we also investigate the form taken by the network weights as training extends and the areas of the image on which visual familiarity–based navigation most relies. Additionally, we investigate the robustness of familiarity-based navigation to view variation caused by noise.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"211-227"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142559567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, while prey must avoid being found. A simulation model of that adversarial relationship is presented here. Camouflage patterns of prey coevolve in competition with visual perception of predators. During their lifetimes, predators learn to better locate the camouflaged prey they encounter. The environment for this 2-D simulation is provided by photographs of natural scenes. The model consists of two evolving populations, one of prey and another of predators. Conflict between these populations produces both effective prey camouflage and predators able to “break” camouflage. The resulting open-source Artificial Life model can help the study of camouflage in nature and the perceptual phenomenon of camouflage more generally.
{"title":"Camouflage From Coevolution of Predator and Prey","authors":"Craig Reynolds","doi":"10.1162/artl_a_00473","DOIUrl":"10.1162/artl_a_00473","url":null,"abstract":"Camouflage in nature seems to arise from competition between predator and prey. To survive, predators must find prey, while prey must avoid being found. A simulation model of that adversarial relationship is presented here. Camouflage patterns of prey coevolve in competition with visual perception of predators. During their lifetimes, predators learn to better locate the camouflaged prey they encounter. The environment for this 2-D simulation is provided by photographs of natural scenes. The model consists of two evolving populations, one of prey and another of predators. Conflict between these populations produces both effective prey camouflage and predators able to “break” camouflage. The resulting open-source Artificial Life model can help the study of camouflage in nature and the perceptual phenomenon of camouflage more generally.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"153-176"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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