Artificial and Computational Intelligence in Games最新文献

{"title":"General Video Game Playing","authors":"J. Levine, C. Congdon, M. Ebner, G. Kendall, S. Lucas, R. Miikkulainen, T. Schaul, Tommy Thompson","doi":"10.4230/DFU.Vol6.12191.77","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.77","url":null,"abstract":"One of the grand challenges of AI is to create general intelligence: an agent that can excel at many tasks, not just one. In the area of games, this has given rise to the challenge of General Game Playing (GGP). In GGP, the game (typically a turn-taking board game) is defined declaratively in terms of the logic of the game (what happens when a move is made, how the scoring system works, how the winner is declared, and so on). The AI player then has to work out how to play the game and how to win. In this work, we seek to extend the idea of General Game Playing into the realm of video games, thus forming the area of General Video Game Playing (GVGP). In GVGP, computational agents will be asked to play video games that they have not seen before. At the minimum, the agent will be given the current state of the world and told what actions are applicable. Every game tick the agent will have to decide on its action, and the state will be updated, taking into account the actions of the other agents in the game and the game physics. We envisage running a competition based on GVGP playing, using arcadestyle (e.g. similar to Atari 2600) games as our starting point. These games are rich enough to be a formidable challenge to a GVGP agent, without introducing unnecessary complexity. The competition that we envisage could have a number of tracks, based on the form of the state (frame buffer or object model) and whether or not a forward model of action execution is available. We propose that the existing Physical Travelling Salesman (PTSP) software could be extended for our purposes and that a variety of GVGP games could be created in this framework by AI and Games students and other developers. Beyond this, we envisage the development of a Video Game Description Language (VGDL) as a way of concisely specifying video games. For the competition, we see this as being an interesting challenge in terms of deliberative search, machine learning and transfer of existing knowledge into new domains.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134130175","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}

{"title":"Towards a Video Game Description Language","authors":"M. Ebner, J. Levine, S. Lucas, T. Schaul, Tommy Thompson, J. Togelius","doi":"10.4230/DFU.Vol6.12191.85","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.85","url":null,"abstract":"As participants in this Dagstuhl session address the challenge of General Video Game Playing (GVGP), we have recognised the need to create a Video Game Description Language (VGDL). Unlike General Game Playing, we have envisioned GVGP will not require a prescribed language to facilitate understanding of the logic of the game: requiring the computational agent to ascertain these facts for itself. However, we would still require means to define the wide range of problems the GVGP agents may face for the purpose of classification. Not only would such a language provide means to encapsulate the features and mechanics of a game for the purposes of human understanding, but also provide context for the evaluation of GVGP agents having completed playing. Outside of the issues of classification, there is also the opportunity for automatic game generation. Given the intent of the GVGP group to work within a framework akin to the one of the Physical Travelling Salesman Problem (PTSP), we aim to attach a code-base to the VGDL compiler that derives implementations of these games from the definition that can be used in conjunction with GVGP. Implementing such a compiler could provide numerous opportunities; users could modify existing games very quickly, or have a library of existing implementations defined within the language (e.g. an Asteroids ship or a Mario avatar) that have pre-existing, parameterised behaviours that can be customised for the users specific purposes. Provided the language is fit for purpose, automatic game creation could be explored further through experimentation with machine learning algorithms, furthering research in game creation and design. In order for both of these perceived functions to be realised and to ensure it is suitable for a large user base we recognise that the language carries several key requirements. Not only must it be human-readable, but retain the capability to be both expressive and extensible whilst equally simple as it is general. In our preliminary discussions, we sought to define the key requirements and challenges in constructing a new VGDL that will become part of the GVGP process. From this we have proposed an initial design to the semantics of the language and the components required to define a given game. Furthermore, we applied this approach to represent classic games such as Space Invaders, Lunar Lander and Frogger in an attempt to identify potential problems that may come to light. In summary, our group has agreed on a series of preliminary language components and started to experiment with forms of implementation for both the language and the attached framework. In future we aim to realise the potential of the VGDL for the purposes of Procedural Content Generation, Automatic Game Design and Transfer Learning and how the roadmap for GVGP can provide opportunities for these areas.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124796698","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}

{"title":"Search in Real-Time Video Games","authors":"P. Cowling, M. Buro, M. Bída, A. Botea, B. Bouzy, Martin Volker Butz, P. Hingston, Hector Muñoz-Avila, Dana S. Nau, M. Sipper","doi":"10.4230/DFU.Vol6.12191.1","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.1","url":null,"abstract":"This chapter arises from the discussions of an experienced international group of researchers interested in the potential for creative application of algorithms for searching finite discrete graphs, which have been highly successful in a wide range of application areas, to address a broad range of problems arising in video games. The chapter first summarises the state of the art in search algorithms for games. It then considers the challenges in implementing these algorithms in video games (particularly real time strategy and first-person games) and ways of creating searchable discrete representations of video game decisions (for example as state-action graphs). Finally the chapter looks forward to promising techniques which might bring some of the success achieved in games such as Go and Chess, to real-time video games. For simplicity, we will consider primarily the objective of maximising playing strength, and consider games where this is a challenging task, which results in interesting gameplay.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115722107","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}

{"title":"Player Modeling","authors":"Georgios N. Yannakakis, P. Spronck, D. Loiacono, E. André","doi":"10.4230/DFU.Vol6.12191.45","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.45","url":null,"abstract":"Player modeling is the study of computational models of players in games. This includes the detection, modeling, prediction and expression of human player characteristics which are manifested through cognitive, affective and behavioral patterns. This chapter introduces a holistic view of player modeling and provides a high level taxonomy and discussion of the key components of a player’s model. The discussion focuses on a taxonomy of approaches for constructing a player model, the available types of data for the model’s input and a proposed classification for the model’s output. The chapter provides also a brief overview of some promising applications and a discussion of the key challenges player modeling is currently facing which are linked to the input, the output and the computational model.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124222057","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}

{"title":"Pathfinding in Games","authors":"A. Botea, B. Bouzy, M. Buro, C. Bauckhage, Dana S. Nau","doi":"10.4230/DFU.Vol6.12191.21","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.21","url":null,"abstract":"Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"174 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123070652","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}

{"title":"Learning and Game AI","authors":"Hector Muñoz-Avila, C. Bauckhage, M. Bída, C. Congdon, G. Kendall","doi":"10.4230/DFU.Vol6.12191.33","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.33","url":null,"abstract":"The incorporation of learning into commercial games can enrich the player experience, but may concern developers in terms of issues such as losing control of their game world. We explore a number of applied research and some fielded applications that point to the tremendous possibilities of machine learning research including game genres such as real-time strategy games, flight simulation games, car and motorcycle racing games, board games such as Go, an even traditional \u0000game-theoretic problems such as the prisoners dilemma. A common trait of these works is the potential of machine learning to reduce the burden of game developers. However a number of challenges exists that hinder the use of machine learning more broadly. We discuss some of these challenges while at the same time exploring opportunities for a wide use of machine learning in games.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124119707","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}

{"title":"Artificial and Computational Intelligence for Games on Mobile Platforms","authors":"C. Congdon, P. Hingston, G. Kendall","doi":"10.4230/DFU.Vol6.12191.101","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.101","url":null,"abstract":"In this chapter, we consider the possibilities of creating new and innovative games that are targeted for mobile devices, such as smart phones and tablets, and that showcase AI (Artificial Intelligence) and CI (Computational Intelligence) approaches. Such games might take advantage of the sensors and facilities that are not available on other platforms, or might simply rely on the \"app culture\" to facilitate getting the games into users' hands. While these games might be profitable in themselves, our focus is on the benefits and challenges of developing AI and CI games for mobile devices.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125865200","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}

{"title":"Procedural Content Generation: Goals, Challenges and Actionable Steps","authors":"J. Togelius, A. Champandard, P. Lanzi, Michael Mateas, Ana Paiva, M. Preuss, Kenneth O. Stanley","doi":"10.4230/DFU.Vol6.12191.61","DOIUrl":"https://doi.org/10.4230/DFU.Vol6.12191.61","url":null,"abstract":"This chapter discusses the challenges and opportunities of procedural content generation (PCG) in games. It starts with defining three grand goals of PCG, namely multi-level multicontent PCG, PCG-based game design and generating complete games. The way these goals are defined, they are not feasible with current technology. Therefore we identify nine challenges for PCG research. Work towards meeting these challenges is likely to take us closer to realising the three grand goals. In order to help researchers get started, we also identify five actionable steps, which PCG researchers could get started working on immediately.","PeriodicalId":138808,"journal":{"name":"Artificial and Computational Intelligence in Games","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131435833","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}