Pub Date : 2013-10-17DOI: 10.1109/CIG.2013.6633613
E. Powley, D. Whitehouse, P. Cowling
Monte Carlo Tree Search (MCTS) is a family of asymmetric anytime aheuristic game tree search algorithms which have advanced the state-of-the-art in several challenging domains. MCTS learns a playout policy, iteratively building a partial tree to store and further refine the learned portion of the policy. When the playout leaves the existing tree, it falls back to a default simulation policy, which for many variants of MCTS chooses actions uniformly at random. This paper investigates how a simulation policy can be learned during the search, helping the playout policy remain plausible from root to terminal state without the injection of prior knowledge. Since the simulation policy visits states that are previously unseen, its decisions cannot be as context sensitive as those in the tree policy. We consider the well-known Move-Average Sampling Technique (MAST), which learns a value for each move which is independent of context. We also introduce a generalisation of MAST, called N-gram-Average-Sampling-Technique (NAST), which uses as context a fixed-lengthsequence (or N-tuple) of recent moves. We compare several policies for selecting moves during simulation, including the UCB1 policy for multi-armed bandits (as used in the tree policy for the popular UCT variant of MCTS). In addition to the elegance of treating the entire playout as a series of multi-armed bandit problems, we find that UCB1 gives consistently strong performance. We present empirical results for three games of imperfect information, namely the card games Dou Di Zhu and Hearts and the board game Lord Of The Rings: The Confrontation, each of which has its own unique challenges for search-based AI.
{"title":"Bandits all the way down: UCB1 as a simulation policy in Monte Carlo Tree Search","authors":"E. Powley, D. Whitehouse, P. Cowling","doi":"10.1109/CIG.2013.6633613","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633613","url":null,"abstract":"Monte Carlo Tree Search (MCTS) is a family of asymmetric anytime aheuristic game tree search algorithms which have advanced the state-of-the-art in several challenging domains. MCTS learns a playout policy, iteratively building a partial tree to store and further refine the learned portion of the policy. When the playout leaves the existing tree, it falls back to a default simulation policy, which for many variants of MCTS chooses actions uniformly at random. This paper investigates how a simulation policy can be learned during the search, helping the playout policy remain plausible from root to terminal state without the injection of prior knowledge. Since the simulation policy visits states that are previously unseen, its decisions cannot be as context sensitive as those in the tree policy. We consider the well-known Move-Average Sampling Technique (MAST), which learns a value for each move which is independent of context. We also introduce a generalisation of MAST, called N-gram-Average-Sampling-Technique (NAST), which uses as context a fixed-lengthsequence (or N-tuple) of recent moves. We compare several policies for selecting moves during simulation, including the UCB1 policy for multi-armed bandits (as used in the tree policy for the popular UCT variant of MCTS). In addition to the elegance of treating the entire playout as a series of multi-armed bandit problems, we find that UCB1 gives consistently strong performance. We present empirical results for three games of imperfect information, namely the card games Dou Di Zhu and Hearts and the board game Lord Of The Rings: The Confrontation, each of which has its own unique challenges for search-based AI.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128795754","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633619
D. Ashlock, C. McGuinness
This study introduces a new representation landscape automata for encoding heightmaps that may be used for terrain generation or other procedural content generation. Landscape automata are evolvable state-conditioned quadtrees with embedded decay parameters. Landscape automata are used to both match idealized landforms and to generate a heightmap with controllable connectivity for agents using the height map as terrain. Parameter studies on both mutation rate and number of states in the automata are performed. Mutation rate is found to have a modest impact on performance while the number of states used both has a large impact on fitness and a different type of impact for each of two types of fitness functions. Landscape automata are demonstrated to be well able to match idealized landforms, providing a palette of varied approximations with a variety of secondary features. They are also able to generate heightmaps that, viewed as terrain, form challenging mazes.
{"title":"Landscape automata for search based procedural content generation","authors":"D. Ashlock, C. McGuinness","doi":"10.1109/CIG.2013.6633619","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633619","url":null,"abstract":"This study introduces a new representation landscape automata for encoding heightmaps that may be used for terrain generation or other procedural content generation. Landscape automata are evolvable state-conditioned quadtrees with embedded decay parameters. Landscape automata are used to both match idealized landforms and to generate a heightmap with controllable connectivity for agents using the height map as terrain. Parameter studies on both mutation rate and number of states in the automata are performed. Mutation rate is found to have a modest impact on performance while the number of states used both has a large impact on fitness and a different type of impact for each of two types of fitness functions. Landscape automata are demonstrated to be well able to match idealized landforms, providing a palette of varied approximations with a variety of secondary features. They are also able to generate heightmaps that, viewed as terrain, form challenging mazes.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114511584","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633654
Josep Valls-Vargas, Santiago Ontañón, Jichen Zhu
Some computer game genres require meaningful stories and complex worlds in order to successfully engage players. In this paper we look at a procedural approach to story-based map generation focusing on the tight relationship between stories and the virtual worlds where those stories will unfold. Our long term goal is to develop procedural content generation techniques that can produce maps supporting multiple stories. We present an approach that takes, as input, the specification of a story space as a collection of plot points. Causal relations between these plot points and spatial relationships between locations define different story and spatial structures. Our system generates multiple configurations of a map, determines the stories that are actually supported in each map, and evaluates their quality, in order to find maps that support high quality stories from a storytelling perspective.
{"title":"Towards story-based content generation: From plot-points to maps","authors":"Josep Valls-Vargas, Santiago Ontañón, Jichen Zhu","doi":"10.1109/CIG.2013.6633654","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633654","url":null,"abstract":"Some computer game genres require meaningful stories and complex worlds in order to successfully engage players. In this paper we look at a procedural approach to story-based map generation focusing on the tight relationship between stories and the virtual worlds where those stories will unfold. Our long term goal is to develop procedural content generation techniques that can produce maps supporting multiple stories. We present an approach that takes, as input, the specification of a story space as a collection of plot points. Causal relations between these plot points and spatial relationships between locations define different story and spatial structures. Our system generates multiple configurations of a map, determines the stories that are actually supported in each map, and evaluates their quality, in order to find maps that support high quality stories from a storytelling perspective.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124482843","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633663
Mark O. Riedl, Alexander Zook
A number of changes are occurring in the field of computer game development: persistent online games, digital distribution platforms and portals, social and mobile games, and the emergence of new business models have pushed game development to put heavier emphasis on the live operation of games. Artificial intelligence has long been an important part of game development practices. The forces of change in the industry present an opportunity for Game AI to have new and profound impact on game production practices. Specifically, Game AI agents should act as “producers” responsible for managing a long-running set of live games, their player communities, and real-world context. We characterize a confluence of four major forces at play in the games industry today, together producing a wealth of data that opens unique research opportunities and challenges for Game AI in game production. We enumerate 12 new research areas spawned by these forces and steps toward how they can be addressed by data-driven Game AI Producers.
{"title":"AI for game production","authors":"Mark O. Riedl, Alexander Zook","doi":"10.1109/CIG.2013.6633663","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633663","url":null,"abstract":"A number of changes are occurring in the field of computer game development: persistent online games, digital distribution platforms and portals, social and mobile games, and the emergence of new business models have pushed game development to put heavier emphasis on the live operation of games. Artificial intelligence has long been an important part of game development practices. The forces of change in the industry present an opportunity for Game AI to have new and profound impact on game production practices. Specifically, Game AI agents should act as “producers” responsible for managing a long-running set of live games, their player communities, and real-world context. We characterize a confluence of four major forces at play in the games industry today, together producing a wealth of data that opens unique research opportunities and challenges for Game AI in game production. We enumerate 12 new research areas spawned by these forces and steps toward how they can be addressed by data-driven Game AI Producers.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125843741","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633617
Hashem Alayed, Fotos Frangoudes, C. Neuman
Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game. Several anti-cheating solutions have been developed by gaming companies. However, most of these companies use cheating detection measures that may involve breaches to users' privacy. In our paper, we provide a server-side anti-cheating solution that uses only game logs. Our method is based on defining an honest player's behavior and cheaters' behavior first. After that, using machine learning classifiers to train cheating models, then detect cheaters. We presented our results in different organizations to show different options for developers, and our methods' results gave a very high accuracy in most of the cases. Finally, we provided a detailed analysis of our results with some useful suggestions for online games developers.
{"title":"Behavioral-based cheating detection in online first person shooters using machine learning techniques","authors":"Hashem Alayed, Fotos Frangoudes, C. Neuman","doi":"10.1109/CIG.2013.6633617","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633617","url":null,"abstract":"Cheating in online games comes with many consequences for both players and companies. Therefore, cheating detection and prevention is an important part of developing a commercial online game. Several anti-cheating solutions have been developed by gaming companies. However, most of these companies use cheating detection measures that may involve breaches to users' privacy. In our paper, we provide a server-side anti-cheating solution that uses only game logs. Our method is based on defining an honest player's behavior and cheaters' behavior first. After that, using machine learning classifiers to train cheating models, then detect cheaters. We presented our results in different organizations to show different options for developers, and our methods' results gave a very high accuracy in most of the cases. Finally, we provided a detailed analysis of our results with some useful suggestions for online games developers.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134478682","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633660
P. Hingston, C. Congdon, G. Kendall
Mobile gaming is an arena full of innovation, with developers exploring new kinds of games, with new kinds of interaction between the mobile device, players, and the connected world that they live in and move through. The mobile gaming world is a perfect playground for AI and CI, generating a maelstrom of data for games that use adaptation, learning and smart content creation. In this paper, we explore this potential killer application for mobile intelligence. We propose combining small, light-weight AI/CI libraries with AI/CI services in the cloud for the heavy lifting. To make our ideas more concrete, we describe a new mobile game that we built that shows how this can work.
{"title":"Mobile games with intelligence: A killer application?","authors":"P. Hingston, C. Congdon, G. Kendall","doi":"10.1109/CIG.2013.6633660","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633660","url":null,"abstract":"Mobile gaming is an arena full of innovation, with developers exploring new kinds of games, with new kinds of interaction between the mobile device, players, and the connected world that they live in and move through. The mobile gaming world is a perfect playground for AI and CI, generating a maelstrom of data for games that use adaptation, learning and smart content creation. In this paper, we explore this potential killer application for mobile intelligence. We propose combining small, light-weight AI/CI libraries with AI/CI services in the cloud for the heavy lifting. To make our ideas more concrete, we describe a new mobile game that we built that shows how this can work.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"13 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124215718","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633638
Jeffrey Tsang
To facilitate systematic and automated analysis of game playing strategies, the fingerprint, a mathematical technique that generates a functional summary independent of representation, was developed. This study attempts to push the boundaries of full state space investigation, looking at 3-state finite transducers as a representation for playing Prisoner's Dilemma. There are a staggering 23,000 unique strategies in this space, which severely limits the choice of analysis methods. These strategies are fingerprinted and pairwise distances computed, then hierarchical clustering reduces them to a manageable size for further experiments with multidimensional scaling and the mutational connectivity network. Results indicate there are no obvious cutoff scales of structure; mutational distance is not correlated with fingerprint distance; and a level of similarity with past results on smaller state spaces.
{"title":"The structure of a 3-state finite transducer representation for Prisoner's Dilemma","authors":"Jeffrey Tsang","doi":"10.1109/CIG.2013.6633638","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633638","url":null,"abstract":"To facilitate systematic and automated analysis of game playing strategies, the fingerprint, a mathematical technique that generates a functional summary independent of representation, was developed. This study attempts to push the boundaries of full state space investigation, looking at 3-state finite transducers as a representation for playing Prisoner's Dilemma. There are a staggering 23,000 unique strategies in this space, which severely limits the choice of analysis methods. These strategies are fingerprinted and pairwise distances computed, then hierarchical clustering reduces them to a manageable size for further experiments with multidimensional scaling and the mutational connectivity network. Results indicate there are no obvious cutoff scales of structure; mutational distance is not correlated with fingerprint distance; and a level of similarity with past results on smaller state spaces.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114727101","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633610
T. Schaul
We propose a powerful new tool for conducting research on computational intelligence and games. `PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL. The main objective of these generated games is to serve as diverse benchmark problems for learning and planning algorithms; so we provide a collection of interfaces for different types of learning agents, with visual or abstract observations, from a global or first-person viewpoint. To demonstrate the library's usefulness in a broad range of learning scenarios, we show how to learn competent behaviors when a model of the game dynamics is available or when it is not, when full state information is given to the agent or just subjective observations, when learning is interactive or in batch-mode, and for a number of different learning algorithms, including reinforcement learning and evolutionary search.
{"title":"A video game description language for model-based or interactive learning","authors":"T. Schaul","doi":"10.1109/CIG.2013.6633610","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633610","url":null,"abstract":"We propose a powerful new tool for conducting research on computational intelligence and games. `PyVGDL' is a simple, high-level description language for 2D video games, and the accompanying software library permits parsing and instantly playing those games. The streamlined design of the language is based on defining locations and dynamics for simple building blocks, and the interaction effects when such objects collide, all of which are provided in a rich ontology. It can be used to quickly design games, without needing to deal with control structures, and the concise language is also accessible to generative approaches. We show how the dynamics of many classical games can be generated from a few lines of PyVGDL. The main objective of these generated games is to serve as diverse benchmark problems for learning and planning algorithms; so we provide a collection of interfaces for different types of learning agents, with visual or abstract observations, from a global or first-person viewpoint. To demonstrate the library's usefulness in a broad range of learning scenarios, we show how to learn competent behaviors when a model of the game dynamics is available or when it is not, when full state information is given to the agent or just subjective observations, when learning is interactive or in batch-mode, and for a number of different learning algorithms, including reinforcement learning and evolutionary search.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062154","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633644
Alberto Uriarte, Santiago Ontañón
Designing a well balanced map for a real-time strategy game might be time consuming. This paper presents an algorithm, called PSMAGE, for generating balanced maps for the popular real-time strategy (RTS) game StarCraft. Our approach uses Voronoi diagrams to generate an initial map layout, and then assigns different properties to each of the regions in the diagram. Additionally, PSMAGE includes a collection of evaluation metrics, aimed at measuring how balanced a map is.
{"title":"PSMAGE: Balanced map generation for StarCraft","authors":"Alberto Uriarte, Santiago Ontañón","doi":"10.1109/CIG.2013.6633644","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633644","url":null,"abstract":"Designing a well balanced map for a real-time strategy game might be time consuming. This paper presents an algorithm, called PSMAGE, for generating balanced maps for the popular real-time strategy (RTS) game StarCraft. Our approach uses Voronoi diagrams to generate an initial map layout, and then assigns different properties to each of the regions in the diagram. Additionally, PSMAGE includes a collection of evaluation metrics, aimed at measuring how balanced a map is.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"47 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121025063","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 : 2013-10-17DOI: 10.1109/CIG.2013.6633649
C. Browne
Deductive search (DS) is a breadth-first, depth-limited propagation scheme for the constraint-based solution of deduction puzzles, using simple logic operations found in standard constraint satisfaction solvers. It attempts to emulate the processing limits experienced by human solvers, and, to some extent, the process by which they solve such problems. Any solution deduced by DS is guaranteed to be correct and unique. Further, it provides an estimate of the deducibility of a given problem for human solvers and offers new ways of understanding deduction puzzles. Its performance is tested on a number of problem domains including Japanese logic puzzles, a traditional logic puzzle, and a geometric placement puzzle.
{"title":"Deductive search for logic puzzles","authors":"C. Browne","doi":"10.1109/CIG.2013.6633649","DOIUrl":"https://doi.org/10.1109/CIG.2013.6633649","url":null,"abstract":"Deductive search (DS) is a breadth-first, depth-limited propagation scheme for the constraint-based solution of deduction puzzles, using simple logic operations found in standard constraint satisfaction solvers. It attempts to emulate the processing limits experienced by human solvers, and, to some extent, the process by which they solve such problems. Any solution deduced by DS is guaranteed to be correct and unique. Further, it provides an estimate of the deducibility of a given problem for human solvers and offers new ways of understanding deduction puzzles. Its performance is tested on a number of problem domains including Japanese logic puzzles, a traditional logic puzzle, and a geometric placement puzzle.","PeriodicalId":158902,"journal":{"name":"2013 IEEE Conference on Computational Inteligence in Games (CIG)","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132357050","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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