This article deals with individuals moving in procession in real and artificial societies. A procession is a minimal form of society in which individual behavior is to go in a given direction and the organization is structured by the knowledge of the one ahead. This simple form of grouping is common in the living world, and, among humans, procession is a very circumscribed social activity whose origins are certainly very remote. This type of organization falls under microsociology, where the focus is on the study of direct interactions between individuals within small groups. In this article, we focus on the particular case of pine tree processionary caterpillars (Thaumetopoea pityocampa). In the first part, we propose a formal definition of the concept of procession and compare field experiments conducted by entomologists with agent-based simulations to study real caterpillars’ processionaries as they are. In the second part, we explore the life of caterpillars as they could be. First, by extending the model beyond reality, we can explain why real processionary caterpillars behave as they do. Then we report on field experiments on the behavior of real caterpillars artificially forced to follow a circular procession; these experiments confirm that each caterpillar can either be the leader of the procession or follow the one in front of it. In the third part, by allowing variations in the speed of movement on an artificial circular procession, computational simulations allow us to observe the emergence of unexpected mobile spatial structures built from regular polygonal shapes where chaotic movements and well-ordered forms are intimately linked. This confirms once again that simple rules can have complex consequences.
{"title":"Processionary Caterpillars at the Edge of Complexity","authors":"Philippe Collard","doi":"10.1162/artl_a_00420","DOIUrl":"10.1162/artl_a_00420","url":null,"abstract":"This article deals with individuals moving in procession in real and artificial societies. A procession is a minimal form of society in which individual behavior is to go in a given direction and the organization is structured by the knowledge of the one ahead. This simple form of grouping is common in the living world, and, among humans, procession is a very circumscribed social activity whose origins are certainly very remote. This type of organization falls under microsociology, where the focus is on the study of direct interactions between individuals within small groups. In this article, we focus on the particular case of pine tree processionary caterpillars (Thaumetopoea pityocampa). In the first part, we propose a formal definition of the concept of procession and compare field experiments conducted by entomologists with agent-based simulations to study real caterpillars’ processionaries as they are. In the second part, we explore the life of caterpillars as they could be. First, by extending the model beyond reality, we can explain why real processionary caterpillars behave as they do. Then we report on field experiments on the behavior of real caterpillars artificially forced to follow a circular procession; these experiments confirm that each caterpillar can either be the leader of the procession or follow the one in front of it. In the third part, by allowing variations in the speed of movement on an artificial circular procession, computational simulations allow us to observe the emergence of unexpected mobile spatial structures built from regular polygonal shapes where chaotic movements and well-ordered forms are intimately linked. This confirms once again that simple rules can have complex consequences.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 2","pages":"171-192"},"PeriodicalIF":2.6,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139479682","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 year 2024 marks the 25th anniversary of the publication of evoloops, an evolutionary variant of Chris Langton’s self-reproducing loops, which proved constructively that Darwinian evolution of self-reproducing organisms by variation and natural selection is possible within deterministic cellular automata. Over the last few decades, this line of Artificial Life research has since undergone several important developments. Although it experienced a relative dormancy of activity for a while, the recent rise of interest in open-ended evolution and the success of continuous cellular automata models have brought researchers’ attention back to how to make spatiotemporal patterns self-reproduce and evolve within spatially distributed computational media. This article provides a review of the relevant literature on this topic over the past 25 years and highlights the major accomplishments made so far, the challenges being faced, and promising future research directions.
{"title":"Self-Reproduction and Evolution in Cellular Automata: 25 Years After Evoloops","authors":"Hiroki Sayama;Chrystopher L. Nehaniv","doi":"10.1162/artl_a_00451","DOIUrl":"10.1162/artl_a_00451","url":null,"abstract":"The year 2024 marks the 25th anniversary of the publication of evoloops, an evolutionary variant of Chris Langton’s self-reproducing loops, which proved constructively that Darwinian evolution of self-reproducing organisms by variation and natural selection is possible within deterministic cellular automata. Over the last few decades, this line of Artificial Life research has since undergone several important developments. Although it experienced a relative dormancy of activity for a while, the recent rise of interest in open-ended evolution and the success of continuous cellular automata models have brought researchers’ attention back to how to make spatiotemporal patterns self-reproduce and evolve within spatially distributed computational media. This article provides a review of the relevant literature on this topic over the past 25 years and highlights the major accomplishments made so far, the challenges being faced, and promising future research directions.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"81-95"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269130","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}
We have discovered a novel transition rule for binary cellular automata (CAs) that yields self-replicating structures across two spatial and temporal scales from sparse random initial conditions. Lower-level, shape-shifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale. This rule, dubbed “Outlier,” is rotationally symmetric and applies to 2-D Moore neighborhoods. It was evolved through genetic programming during an extensive search for rules that foster open-ended evolution in CAs. While self-replicating structures, both crafted and emergent, have been created in CAs with state sets intentionally designed for this purpose, the Outlier may be the first known rule to facilitate nontrivial emergent self-replication across two spatial scales in binary CAs.
我们发现了一种新颖的二元细胞自动机（CA）过渡规则，它能从稀疏的随机初始条件中产生跨越两个空间和时间尺度的自我复制结构。低级的、形状可变的细胞簇经常遵循瞬态吸引子轨迹，产生新的细胞簇，其中一些细胞簇会周期性地自我复制。当活细胞的初始分布足够稀疏时，这些细胞簇会凝聚成更大的形态，这些形态也会自我复制。这些形态可能会进一步形成规模更大的扩展复合体的边界。这种被称为 "离群者 "的规则是旋转对称的，适用于二维摩尔邻域。它是在广泛寻找促进 CA 开放式进化的规则的过程中，通过遗传编程进化而来的。在二元CA中，人们已经通过有意设计的状态集创建了自我复制结构，包括精心制作的和突发的结构，而 "离群者 "可能是第一个在二元CA中促进跨越两个空间尺度的非难突发自我复制的已知规则。
{"title":"Emergence of Self-Replicating Hierarchical Structures in a Binary Cellular Automaton","authors":"Bo Yang","doi":"10.1162/artl_a_00449","DOIUrl":"10.1162/artl_a_00449","url":null,"abstract":"We have discovered a novel transition rule for binary cellular automata (CAs) that yields self-replicating structures across two spatial and temporal scales from sparse random initial conditions. Lower-level, shape-shifting clusters frequently follow a transient attractor trajectory, generating new clusters, some of which periodically self-duplicate. When the initial distribution of live cells is sufficiently sparse, these clusters coalesce into larger formations that also self-replicate. These formations may further form the boundaries of an expanding complex on an even larger scale. This rule, dubbed “Outlier,” is rotationally symmetric and applies to 2-D Moore neighborhoods. It was evolved through genetic programming during an extensive search for rules that foster open-ended evolution in CAs. While self-replicating structures, both crafted and emergent, have been created in CAs with state sets intentionally designed for this purpose, the Outlier may be the first known rule to facilitate nontrivial emergent self-replication across two spatial scales in binary CAs.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"96-105"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10907985","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037834","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}
Biological cells are usually operating in conditions characterized by intercellular signaling and interaction, which are supposed to strongly influence individual cell dynamics. In this work, we study the dynamics of interacting random Boolean networks, focusing on attractor properties and response to perturbations. We observe that the properties of isolated critical Boolean networks are substantially maintained also in interaction settings, while interactions bias the dynamics of chaotic and ordered networks toward that of critical cells. The increase in attractors observed in multicellular scenarios, compared to single cells, allows us to hypothesize that biological processes, such as ontogeny and cell differentiation, leverage interactions to modulate individual and collective cell responses.
{"title":"Cell–Cell Interactions: How Coupled Boolean Networks Tend to Criticality","authors":"Michele Braccini;Paolo Baldini;Andrea Roli","doi":"10.1162/artl_a_00444","DOIUrl":"10.1162/artl_a_00444","url":null,"abstract":"Biological cells are usually operating in conditions characterized by intercellular signaling and interaction, which are supposed to strongly influence individual cell dynamics. In this work, we study the dynamics of interacting random Boolean networks, focusing on attractor properties and response to perturbations. We observe that the properties of isolated critical Boolean networks are substantially maintained also in interaction settings, while interactions bias the dynamics of chaotic and ordered networks toward that of critical cells. The increase in attractors observed in multicellular scenarios, compared to single cells, allows us to hypothesize that biological processes, such as ontogeny and cell differentiation, leverage interactions to modulate individual and collective cell responses.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"68-80"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749767","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}
It is very important to model the behavior of protocells as basic lifelike artificial organisms more and more accurately from the level of genomes to the level of populations. A better understanding of basic protocell communities may help us in describing more complex ecological systems accurately. In this article, we propose a new comprehensive, bilevel mathematical model of a community of three protocell species (one generalist and two specialists). The aim is to achieve a model that is as basic/fundamental as possible while already displaying mutation, selection, and complex population dynamics phenomena (like competitive exclusion and keystone species). At the microlevel of genetic codes, the protocells and their mutations are modeled with Turing machines (TMs). The specialists arise from the generalist by means of mutation. Then the species are put into a common habitat, where, at the macrolevel of populations, they have to compete for the available nutrients, a part of which they themselves can produce. Because of different kinds of mutations, the running times of the species as TMs (algorithms) are different. This feature is passed on to the macrolevel as different reproduction times. At the macrolevel, a discrete-time dynamic model describes the competition. The model displays complex lifelike behavior known from population ecology, including the so-called competitive exclusion principle and the effect of keystone species. In future works, the bilevel model will have a good chance of serving as a simple and useful tool for studying more lifelike phenomena (like evolution) in their pure/abstract form.
{"title":"Modeling the Mutation and Competition of Certain Nutrient-Producing Protocells by Means of Specific Turing Machines","authors":"Richárd Kicsiny;Levente Hufnagel;Lajos Lóczi;László Székely;Zoltán Varga","doi":"10.1162/artl_a_00463","DOIUrl":"10.1162/artl_a_00463","url":null,"abstract":"It is very important to model the behavior of protocells as basic lifelike artificial organisms more and more accurately from the level of genomes to the level of populations. A better understanding of basic protocell communities may help us in describing more complex ecological systems accurately. In this article, we propose a new comprehensive, bilevel mathematical model of a community of three protocell species (one generalist and two specialists). The aim is to achieve a model that is as basic/fundamental as possible while already displaying mutation, selection, and complex population dynamics phenomena (like competitive exclusion and keystone species). At the microlevel of genetic codes, the protocells and their mutations are modeled with Turing machines (TMs). The specialists arise from the generalist by means of mutation. Then the species are put into a common habitat, where, at the macrolevel of populations, they have to compete for the available nutrients, a part of which they themselves can produce. Because of different kinds of mutations, the running times of the species as TMs (algorithms) are different. This feature is passed on to the macrolevel as different reproduction times. At the macrolevel, a discrete-time dynamic model describes the competition. The model displays complex lifelike behavior known from population ecology, including the so-called competitive exclusion principle and the effect of keystone species. In future works, the bilevel model will have a good chance of serving as a simple and useful tool for studying more lifelike phenomena (like evolution) in their pure/abstract form.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"2-30"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886515","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 evolution of living beings with continuous and consistent progress toward adaptation and ways to model evolution along principles as close as possible to Darwin’s are important areas of focus in Artificial Life. Though genetic algorithms and evolutionary strategies are good methods for modeling selection, crossover, and mutation, biological systems are undeniably spatially distributed processes in which living organisms interact with locally available individuals rather than with the entire population at once. This work presents a model for the survival of organisms during a change in the environment to a less favorable one, putting them at risk of extinction, such as many organisms experience today under climate change or local habitat loss or fragmentation. Local spatial structure of resources and environmental quality also impacts the capacity of an evolving population to adapt. The problem is considered on a probabilistic cellular automaton with update rules based on the principles of genetic algorithms. To carry out simulations according to the described model, the Darwinian cellular automata are introduced, and the software has been designed with the code available open source. An experimental evaluation of the behavioral characteristics of the model was carried out, completed by a critical evaluation of the results obtained, parametrically describing conditions and thresholds under which extinction or survival of the population may occur.
{"title":"Survival and Evolutionary Adaptation of Populations Under Disruptive Habitat Change: A Study With Darwinian Cellular Automata","authors":"Hanna Derets;Chrystopher L. Nehaniv","doi":"10.1162/artl_a_00457","DOIUrl":"10.1162/artl_a_00457","url":null,"abstract":"The evolution of living beings with continuous and consistent progress toward adaptation and ways to model evolution along principles as close as possible to Darwin’s are important areas of focus in Artificial Life. Though genetic algorithms and evolutionary strategies are good methods for modeling selection, crossover, and mutation, biological systems are undeniably spatially distributed processes in which living organisms interact with locally available individuals rather than with the entire population at once. This work presents a model for the survival of organisms during a change in the environment to a less favorable one, putting them at risk of extinction, such as many organisms experience today under climate change or local habitat loss or fragmentation. Local spatial structure of resources and environmental quality also impacts the capacity of an evolving population to adapt. The problem is considered on a probabilistic cellular automaton with update rules based on the principles of genetic algorithms. To carry out simulations according to the described model, the Darwinian cellular automata are introduced, and the software has been designed with the code available open source. An experimental evaluation of the behavioral characteristics of the model was carried out, completed by a critical evaluation of the results obtained, parametrically describing conditions and thresholds under which extinction or survival of the population may occur.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"106-123"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407255","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_00469","DOIUrl":"10.1162/artl_e_00469","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"1-1"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143506270","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}
Reproduction, development, and individual interactions are vital yet complex natural processes. Tierra (an ALife model proposed by Thomas Ray) and cellular automata, which can manage these aspects in a complex manner, are significantly limited in their ability to express morphology and behavior. In contrast, the virtual creatures proposed by Karl Sims have a considerably higher degree of freedom in terms of morphology and behavior. However, they also exhibit a limited capacity for processes like reproduction, development, and individual interactions. In addition, they employ genetic algorithms, which can result in a loss of biological diversity, as their implementation necessitates predefining a fitness function. Contrarily, the evolution of natural life is determined by mutation and natural selection, rather than by a human-defined fitness function. This study carefully extracts the characteristics of these models to propose a new Artificial Life model that can simulate reproduction, development, and individual interactions while exhibiting a high expressive power for morphology and behavior. The model is based on the concept of incorporating Tierra and cellular automata mechanisms into a cell that moves freely in 3-D space. In this model, no predefined fitness function or form that qualifies as a living creature exists. In other words, this approach can be rephrased as searching for persistent patterns, which is similar to the approach of Conway’s Game of Life. The primary objective of this study was to conduct a proof-of-concept demonstration to showcase the capabilities of this model. Guideless simulation by the proposed model using mutation and natural selection resulted in the formation of two types of creatures—dumbbell shaped and reticulated. These creatures exhibit intriguing features, exploiting the degrees of freedom inherent to the proposed model. Particularly noteworthy is their unique method of reproduction, which bears a striking resemblance to that of real organisms. These results reinforce the potential of this approach in modeling intricate processes observed in actual organisms and its ability to generate virtual creatures with intriguing ecologies.
{"title":"Guideless Artificial Life Model for Reproduction, Development, and Interactions","authors":"Keishu Utimula","doi":"10.1162/artl_a_00466","DOIUrl":"10.1162/artl_a_00466","url":null,"abstract":"Reproduction, development, and individual interactions are vital yet complex natural processes. Tierra (an ALife model proposed by Thomas Ray) and cellular automata, which can manage these aspects in a complex manner, are significantly limited in their ability to express morphology and behavior. In contrast, the virtual creatures proposed by Karl Sims have a considerably higher degree of freedom in terms of morphology and behavior. However, they also exhibit a limited capacity for processes like reproduction, development, and individual interactions. In addition, they employ genetic algorithms, which can result in a loss of biological diversity, as their implementation necessitates predefining a fitness function. Contrarily, the evolution of natural life is determined by mutation and natural selection, rather than by a human-defined fitness function. This study carefully extracts the characteristics of these models to propose a new Artificial Life model that can simulate reproduction, development, and individual interactions while exhibiting a high expressive power for morphology and behavior. The model is based on the concept of incorporating Tierra and cellular automata mechanisms into a cell that moves freely in 3-D space. In this model, no predefined fitness function or form that qualifies as a living creature exists. In other words, this approach can be rephrased as searching for persistent patterns, which is similar to the approach of Conway’s Game of Life. The primary objective of this study was to conduct a proof-of-concept demonstration to showcase the capabilities of this model. Guideless simulation by the proposed model using mutation and natural selection resulted in the formation of two types of creatures—dumbbell shaped and reticulated. These creatures exhibit intriguing features, exploiting the degrees of freedom inherent to the proposed model. Particularly noteworthy is their unique method of reproduction, which bears a striking resemblance to that of real organisms. These results reinforce the potential of this approach in modeling intricate processes observed in actual organisms and its ability to generate virtual creatures with intriguing ecologies.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"31-64"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143082331","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}
{"title":"Editorial: Special Issue “The Distributed Ghost”—Cellular Automata, Distributed Dynamical Systems, and Their Applications to Intelligence","authors":"Stefano Nichele;Hiroki Sayama;Eric Medvet;Chrystopher Nehaniv;Mario Pavone","doi":"10.1162/artl_e_00450","DOIUrl":"10.1162/artl_e_00450","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 1","pages":"65-67"},"PeriodicalIF":1.6,"publicationDate":"2024-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037820","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":"What Is Artificial Life Today, and Where Should It Go?","authors":"Alan Dorin;Susan Stepney","doi":"10.1162/artl_e_00435","DOIUrl":"10.1162/artl_e_00435","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 1","pages":"1-15"},"PeriodicalIF":2.6,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140308011","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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