Pub Date : 2016-06-01DOI: 10.1109/TCIAIG.2015.2505404
Benjamin Eckstein, Jean-Luc Lugrin, Dennis Wiebusch, Marc Erich Latoschik
Today's physics engines mainly simulate classical mechanics and rigid body dynamics, with some late advances also capable of simulating massive particle systems and some approximations of fluid dynamics. An accurate numerical simulation of complex nonmechanical processes in real time is beyond the state of the art in the respective fields. This paper illustrates an alternative approach to a purely numerical solution. It uses a semantic representation of physical properties and processes as well as a reasoning engine to model cause and effect between objects, based on their material properties. Classical collision detection is combined with semantic rules to model various physical processes, for example, in the areas of thermodynamics, electrodynamics, and fluid dynamics as well as chemical processes. Each process is broken down into fine-grained subprocesses capable of approximating continuous transitions with discretized state changes. Our system applies these high-level state descriptions to low-level value changes, which are directly mapped to a graphical representation of the scene. We demonstrate our framework's ability to support multiple complex, causally connected physical and chemical processes by simulating a Goldberg machine. Our performance benchmarks validate its scalability and potential application for entertainment or edutainment purposes.
{"title":"PEARS: Physics extension and representation through semantics","authors":"Benjamin Eckstein, Jean-Luc Lugrin, Dennis Wiebusch, Marc Erich Latoschik","doi":"10.1109/TCIAIG.2015.2505404","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2505404","url":null,"abstract":"Today's physics engines mainly simulate classical mechanics and rigid body dynamics, with some late advances also capable of simulating massive particle systems and some approximations of fluid dynamics. An accurate numerical simulation of complex nonmechanical processes in real time is beyond the state of the art in the respective fields. This paper illustrates an alternative approach to a purely numerical solution. It uses a semantic representation of physical properties and processes as well as a reasoning engine to model cause and effect between objects, based on their material properties. Classical collision detection is combined with semantic rules to model various physical processes, for example, in the areas of thermodynamics, electrodynamics, and fluid dynamics as well as chemical processes. Each process is broken down into fine-grained subprocesses capable of approximating continuous transitions with discretized state changes. Our system applies these high-level state descriptions to low-level value changes, which are directly mapped to a graphical representation of the scene. We demonstrate our framework's ability to support multiple complex, causally connected physical and chemical processes by simulating a Goldberg machine. Our performance benchmarks validate its scalability and potential application for entertainment or edutainment purposes.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"178-189"},"PeriodicalIF":0.0,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2505404","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593291","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 : 2016-05-26DOI: 10.1109/TCIAIG.2016.2573199
Florian Richoux, Alberto Uriarte, Jean-François Baffier
This paper presents GHOST, a combinatorial optimization framework that a real-time strategy (RTS) AI developer can use to model and solve any problem encoded as a constraint satisfaction/optimization problem (CSP/COP). We show a way to model three different problems as a CSP/COP, using instances from the RTS game StarCraft as test beds. Each problem belongs to a specific level of abstraction (the target selection as reactive control problem, the wall-in as a tactics problem, and the build order planning as a strategy problem). In our experiments, GHOST shows good results computed within some tens of milliseconds. We also show that GHOST outperforms state-of-the-art constraint solvers, matching them on the resources allocation problem, a common combinatorial optimization problem.
{"title":"ghost: A Combinatorial Optimization Framework for Real-Time Problems","authors":"Florian Richoux, Alberto Uriarte, Jean-François Baffier","doi":"10.1109/TCIAIG.2016.2573199","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2573199","url":null,"abstract":"This paper presents GHOST, a combinatorial optimization framework that a real-time strategy (RTS) AI developer can use to model and solve any problem encoded as a constraint satisfaction/optimization problem (CSP/COP). We show a way to model three different problems as a CSP/COP, using instances from the RTS game StarCraft as test beds. Each problem belongs to a specific level of abstraction (the target selection as reactive control problem, the wall-in as a tactics problem, and the build order planning as a strategy problem). In our experiments, GHOST shows good results computed within some tens of milliseconds. We also show that GHOST outperforms state-of-the-art constraint solvers, matching them on the resources allocation problem, a common combinatorial optimization problem.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"377-388"},"PeriodicalIF":0.0,"publicationDate":"2016-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2573199","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593042","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 : 2016-05-02DOI: 10.1109/TCIAIG.2016.2561080
P. Walega, Michał Zawidzki, Tomasz Lechowski
In this paper, we present a program designed to successfully and autonomously play Angry Birds, which attempts to embrace motives of human players in their choices of targets they want to shoot at in a game play. The program comprises two modules: the representation module and the reasoning module. In the former, we introduce qualitative space representation that utilizes notions such as “to lie on,” “to lie to the right,” “to be a shelter of a target,” etc. The latter investigates how particular blocks of a structure behave once one of them has been hit. It includes two algorithms, namely vertical impact and horizontal impact. The first one is a novel method of investigating the behavior of complex structures after one of their constituent blocks gets hit. Namely, it predicts which elements of a structure fall if a supporting block gets destroyed. Horizontal impact, on the other hand, simulates force propagation between adjacent elements after one of them gets struck. We also describe experimental tests we have conducted in which Vertical Impact correctly predicted which blocks will fall in over 98% of investigated cases.
{"title":"Qualitative Physics in Angry Birds","authors":"P. Walega, Michał Zawidzki, Tomasz Lechowski","doi":"10.1109/TCIAIG.2016.2561080","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2561080","url":null,"abstract":"In this paper, we present a program designed to successfully and autonomously play Angry Birds, which attempts to embrace motives of human players in their choices of targets they want to shoot at in a game play. The program comprises two modules: the representation module and the reasoning module. In the former, we introduce qualitative space representation that utilizes notions such as “to lie on,” “to lie to the right,” “to be a shelter of a target,” etc. The latter investigates how particular blocks of a structure behave once one of them has been hit. It includes two algorithms, namely vertical impact and horizontal impact. The first one is a novel method of investigating the behavior of complex structures after one of their constituent blocks gets hit. Namely, it predicts which elements of a structure fall if a supporting block gets destroyed. Horizontal impact, on the other hand, simulates force propagation between adjacent elements after one of them gets struck. We also describe experimental tests we have conducted in which Vertical Impact correctly predicted which blocks will fall in over 98% of investigated cases.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"152-165"},"PeriodicalIF":0.0,"publicationDate":"2016-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2561080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593004","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}
Angry Birds is a popular video game in which a set of birds has to perform sling shots (bird shots) so as to kill pigs that are protected by a structure composed of different building blocks. The fewer birds we use and the more blocks we destroy, the higher the score we achieve. AIBirds competition is an AI challenge where an intelligent bot has to be developed that plays the game without human intervention. In this paper, we describe the approach implemented in the bot, s-birds Avengers, that participated in ECAI AIBirds 2014. Heuristic techniques were designed to analyze unseen structures using various structural parameters and then to discover their vulnerable points using prior parameter learning training algorithm. The bot then uses this to decide where to hit the structure with the birds.
{"title":"s-Birds Avengers: A Dynamic Heuristic Engine-Based Agent for the Angry Birds Problem","authors":"Sourish Dasgupta, Savan Vaghela, Vishwa Modi, Hitarth Kanakia","doi":"10.1109/TCIAIG.2016.2553244","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2553244","url":null,"abstract":"Angry Birds is a popular video game in which a set of birds has to perform sling shots (bird shots) so as to kill pigs that are protected by a structure composed of different building blocks. The fewer birds we use and the more blocks we destroy, the higher the score we achieve. AIBirds competition is an AI challenge where an intelligent bot has to be developed that plays the game without human intervention. In this paper, we describe the approach implemented in the bot, s-birds Avengers, that participated in ECAI AIBirds 2014. Heuristic techniques were designed to analyze unseen structures using various structural parameters and then to discover their vulnerable points using prior parameter learning training algorithm. The bot then uses this to decide where to hit the structure with the birds.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"140-151"},"PeriodicalIF":0.0,"publicationDate":"2016-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2553244","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592995","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 : 2016-04-01DOI: 10.1109/TCIAIG.2015.2506741
X. Ge, Jochen Renz, Peng Zhang
Many current computer vision approaches for object detection can only detect objects that have been learned in advance. In this paper, we present a method that uses qualitative stability analysis to infer the existence of unknown objects in certain areas of the images based on gravity and stability of already detected objects. Our method recursively searches these areas for unknown objects until all detected objects form a stable structure or no new objects can be identified anymore. We evaluate our method using the popular video game Angry Birds. We only start with detecting the green pigs and are able to automatically identify and detect all essential game objects in all 400+ available levels. All objects can be accurately and reliably detected. Our method can be applied to other video games where objects obey gravity and are bound by polygons.
{"title":"Visual Detection of Unknown Objects in Video Games Using Qualitative Stability Analysis","authors":"X. Ge, Jochen Renz, Peng Zhang","doi":"10.1109/TCIAIG.2015.2506741","DOIUrl":"https://doi.org/10.1109/TCIAIG.2015.2506741","url":null,"abstract":"Many current computer vision approaches for object detection can only detect objects that have been learned in advance. In this paper, we present a method that uses qualitative stability analysis to infer the existence of unknown objects in certain areas of the images based on gravity and stability of already detected objects. Our method recursively searches these areas for unknown objects until all detected objects form a stable structure or no new objects can be identified anymore. We evaluate our method using the popular video game Angry Birds. We only start with detecting the green pigs and are able to automatically identify and detect all essential game objects in all 400+ available levels. All objects can be accurately and reliably detected. Our method can be applied to other video games where objects obey gravity and are bound by polygons.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"166-177"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2015.2506741","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593300","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 : 2016-04-01DOI: 10.1109/TCIAIG.2016.2549748
Christopher Archibald, Alon Altman, Y. Shoham
Games with continuous state and action spaces present unique challenges from an artificial intelligence (AI) viewpoint. Billiards, or pool, is one such domain that has been the focus of several research efforts aimed at designing AI agents to play successfully. Due to the continuous nature of the actions, it is natural to believe that the more time an agent has to investigate actions, the better it will perform. This paper gives a thorough description of a successful agent with a novel distributed architecture, designed for being able to grant further time for shot simulation and analysis through the utilization of many CPUs. A brief analysis of the distributed component of the agent is presented, as well as how much the extra time thus obtained contributed to its success, especially when compared to its other novel components. The described agent, CueCard, won the Computer Olympiad computational pool tournament held in 2008.
{"title":"A Distributed Agent for Computational Pool","authors":"Christopher Archibald, Alon Altman, Y. Shoham","doi":"10.1109/TCIAIG.2016.2549748","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2549748","url":null,"abstract":"Games with continuous state and action spaces present unique challenges from an artificial intelligence (AI) viewpoint. Billiards, or pool, is one such domain that has been the focus of several research efforts aimed at designing AI agents to play successfully. Due to the continuous nature of the actions, it is natural to believe that the more time an agent has to investigate actions, the better it will perform. This paper gives a thorough description of a successful agent with a novel distributed architecture, designed for being able to grant further time for shot simulation and analysis through the utilization of many CPUs. A brief analysis of the distributed component of the agent is presented, as well as how much the extra time thus obtained contributed to its success, especially when compared to its other novel components. The described agent, CueCard, won the Computer Olympiad computational pool tournament held in 2008.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"190-202"},"PeriodicalIF":0.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2549748","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593459","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 : 2016-03-22DOI: 10.1109/TCIAIG.2016.2544844
Siming Liu, S. Louis, Christopher A. Ballinger
We investigate heuristic search algorithms to generate high-quality micromanagement in combat scenarios for real-time strategy (RTS) games. Macro- and micromanagement are two key aspects of RTS games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes and battles against equal numbers and types of opponent units or win even when outnumbered. In this paper, we use influence maps and potential fields as a basis representation to evolve short-term positioning and movement tactics. Unit microbehaviors in combat are compactly encoded into 14 parameters. A genetic algorithm evolves good microbehaviors by manipulating these 14 parameters. We compared the performance of our evolved ECSLBot with two other state-of-the-art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular RTS game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. Further experiments show that the parameter values evolved in one scenario work well in other scenarios and that we can switch between preevolved parameter sets to perform well in unseen scenarios containing more than one type of opponent unit. We believe our representation and approach applied to each unit type of interest can result in effective microperformance against melee and ranged opponents and provides a viable approach toward complete RTS bots.
{"title":"Evolving Effective Microbehaviors in Real-Time Strategy Games","authors":"Siming Liu, S. Louis, Christopher A. Ballinger","doi":"10.1109/TCIAIG.2016.2544844","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2544844","url":null,"abstract":"We investigate heuristic search algorithms to generate high-quality micromanagement in combat scenarios for real-time strategy (RTS) games. Macro- and micromanagement are two key aspects of RTS games. While good macro helps a player collect more resources and build more units, good micro helps a player win skirmishes and battles against equal numbers and types of opponent units or win even when outnumbered. In this paper, we use influence maps and potential fields as a basis representation to evolve short-term positioning and movement tactics. Unit microbehaviors in combat are compactly encoded into 14 parameters. A genetic algorithm evolves good microbehaviors by manipulating these 14 parameters. We compared the performance of our evolved ECSLBot with two other state-of-the-art bots, UAlbertaBot and Nova, on several skirmish scenarios in a popular RTS game StarCraft. The results show that the ECSLBot tuned by genetic algorithms outperforms UAlbertaBot and Nova in kiting efficiency, target selection, and fleeing. Further experiments show that the parameter values evolved in one scenario work well in other scenarios and that we can switch between preevolved parameter sets to perform well in unseen scenarios containing more than one type of opponent unit. We believe our representation and approach applied to each unit type of interest can result in effective microperformance against melee and ranged opponents and provides a viable approach toward complete RTS bots.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"351-362"},"PeriodicalIF":0.0,"publicationDate":"2016-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2544844","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593446","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 : 2016-03-22DOI: 10.1109/TCIAIG.2016.2544817
Christopher A. Ballinger, S. Louis, Siming Liu
We investigate and develop a coevolutionary approach to finding strong, robust build orders for real-time strategy games. Which units to produce and the order in which to produce them is one important aspect of real-time strategy gameplay. In real-time strategy games, creating plans to address unit production problems are called “build orders.” Our research compares build orders produced from a coevolutionary algorithm, genetic algorithm (GA), and hill climber (HC) to exhaustive search. GAs find the strongest build orders, while coevolution produces more robust build orders than a genetic algorithm or HC. Case injection into the coevolutionary teachset and population can be used to bias coevolution into producing build orders that beat specific opponents or play like specific players, while maintaining robustness. Finally, in this paper, we extend our representation by adding branching and iteration to the build-action sequence and show that this more complex representation enables coevolution to find stronger build orders. We believe this study is a start toward a promising approach for creating strong, robust build orders for RTS games.
{"title":"Coevolving Robust Build-Order Iterative Lists for Real-Time Strategy Games","authors":"Christopher A. Ballinger, S. Louis, Siming Liu","doi":"10.1109/TCIAIG.2016.2544817","DOIUrl":"https://doi.org/10.1109/TCIAIG.2016.2544817","url":null,"abstract":"We investigate and develop a coevolutionary approach to finding strong, robust build orders for real-time strategy games. Which units to produce and the order in which to produce them is one important aspect of real-time strategy gameplay. In real-time strategy games, creating plans to address unit production problems are called “build orders.” Our research compares build orders produced from a coevolutionary algorithm, genetic algorithm (GA), and hill climber (HC) to exhaustive search. GAs find the strongest build orders, while coevolution produces more robust build orders than a genetic algorithm or HC. Case injection into the coevolutionary teachset and population can be used to bias coevolution into producing build orders that beat specific opponents or play like specific players, while maintaining robustness. Finally, in this paper, we extend our representation by adding branching and iteration to the build-action sequence and show that this more complex representation enables coevolution to find stronger build orders. We believe this study is a start toward a promising approach for creating strong, robust build orders for RTS games.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"363-376"},"PeriodicalIF":0.0,"publicationDate":"2016-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2016.2544817","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62593438","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 : 2016-03-01DOI: 10.1109/TCIAIG.2014.2355859
Pierpaolo Basile, M. Degemmis, P. Lops, G. Semeraro
“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.
{"title":"Solving a Complex Language Game by Using Knowledge-Based Word Associations Discovery","authors":"Pierpaolo Basile, M. Degemmis, P. Lops, G. Semeraro","doi":"10.1109/TCIAIG.2014.2355859","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2355859","url":null,"abstract":"“The Guillotine” is a language game whose goal is to predict the unique word that is linked in some way to five words given as clues, generally unrelated to each other. The ability of the human player to find the solution depends on the richness of her cultural background. We designed an artificial player for that game, based on a large knowledge repository built by exploiting several sources available on the web, such as Wikipedia, that provide the system with the cultural and linguistic background needed to understand clues. The “brain” of the system is a spreading activation algorithm that starts processing clues, finds associations between them and words within the knowledge repository, and computes a list of candidate solutions. In this paper we focus on the problem of finding the most promising candidate solution to be provided as the final answer. We improved the spreading algorithm by means of two strategies for finding associations also between candidate solutions and clues. Those strategies allow bidirectional reasoning and select the candidate solution which is the most connected with the clues. Experiments show that the performance of the system is comparable to that of average human players.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"13-26"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2355859","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592516","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 : 2016-03-01DOI: 10.1109/TCIAIG.2014.2365414
Marius Stanescu, Michal Čertický
The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent's actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player's partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 min seems to be surprisingly high, making the method useful in practice. Root-mean-square error grows only slowly with increasing prediction intervals-almost in a linear fashion.
{"title":"Predicting Opponent's Production in Real-Time Strategy Games With Answer Set Programming","authors":"Marius Stanescu, Michal Čertický","doi":"10.1109/TCIAIG.2014.2365414","DOIUrl":"https://doi.org/10.1109/TCIAIG.2014.2365414","url":null,"abstract":"The adversarial character of real-time strategy (RTS) games is one of the main sources of uncertainty within this domain. Since players lack exact knowledge about their opponent's actions, they need a reasonable representation of alternative possibilities and their likelihood. In this article we propose a method of predicting the most probable combination of units produced by the opponent during a certain time period. We employ a logic programming paradigm called Answer Set Programming, since its semantics is well suited for reasoning with uncertainty and incomplete knowledge. In contrast with typical, purely probabilistic approaches, the presented method takes into account the background knowledge about the game and only considers the combinations that are consistent with the game mechanics and with the player's partial observations. Experiments, conducted during different phases of StarCraft: Brood War and Warcraft III: The Frozen Throne games, show that the prediction accuracy for time intervals of 1-3 min seems to be surprisingly high, making the method useful in practice. Root-mean-square error grows only slowly with increasing prediction intervals-almost in a linear fashion.","PeriodicalId":49192,"journal":{"name":"IEEE Transactions on Computational Intelligence and AI in Games","volume":"8 1","pages":"89-94"},"PeriodicalIF":0.0,"publicationDate":"2016-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TCIAIG.2014.2365414","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62592720","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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