Proceedings ArticleDOI
Recent Research on AI in Games
Boming Xia,Xiaozhen Ye,Adnan Omer Abuassba +2 more
- pp 505-510
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TLDR
A systematic review of typical research from 2018 on three application fields of game AI: believable agents in non-player characters research, game level generation in procedural content generation, and player profiling in player modeling is made.Abstract:
Games tend to have the properties of vast state space and high complexity, making them excellent benchmarks for evaluating various techniques, including AI ones. Techniques utilized in games capable of making them more attractive, immersive, smarter etc. can all be considered to be certain forms of game AI. Considering there are few reviews on the more recent work in the game AI field from the perspective of essential applications, in this paper, we make a systematic review of typical research from 2018 on three application fields of game AI: believable agents in non-player characters research, game level generation in procedural content generation, and player profiling in player modeling. We also provide a timeline of game AI history to give the readers a clearer picture of the game AI field. Moreover, general game AI and hybrid intelligence for games are discussed.read more
Citations
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Proceedings ArticleDOI
A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents
TL;DR: The use of cognitively inspired models of behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity, which allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.
Journal ArticleDOI
Modeling Human Behavior Part I - Learning and Belief Approaches
TL;DR: The main objective of this paper is to provide a succinct yet systematic review of the most important approaches in two areas dealing with quantitative models of human behaviors, and to directly model mechanisms of human reasoning, such as beliefs and bias, without going necessarily learning via trial-and-error.
A Partially Automated Process For the Generation of Believable Human Behaviors
TL;DR: MAGIC as discussed by the authors is an agent-based decision-making model that automatically finds points in the model's decision process that require interaction with other agents or with the simulation envionment and creates a decision graph that contains the agent's behavior pattern based upon raw data composed of time-sequential observations.
Journal ArticleDOI
Materializing the abstract: Understanding AI by game jamming
Jeanette Falk,Nanna Inie +1 more
TL;DR: It is argued that game jam formats are uniquely suited to engage participants in learning about artificial intelligence (AI) as a design material because of four factors which are characteristic of game jams: 1) Game jams provide an opportunity for hands-on, interactive prototyping, 2)Game jams encourage playful participation, 3) game jams encourage creative combinations of AI and game development, and 4) game jam offer understandable goals and evaluation metrics for AI.
Journal ArticleDOI
Modeling, Replicating, and Predicting Human Behavior: A Survey
TL;DR: In this article , the authors provide a systematic review of important approaches in two areas dealing with quantitative models of human behaviors: reinforcement learning and directly modeling mechanisms of human reasoning, such as beliefs and bias, without necessarily learning via trial and error.
References
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Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Book
Understanding Natural Language
TL;DR: A computer system for understanding English that contains a parser, a recognition grammar of English, programs for semantic analysis, and a general problem solving system based on the belief that in modeling language understanding, it must deal in an integrated way with all of the aspects of language—syntax, semantics, and inference.
BookDOI
Artificial Intelligence and Games
TL;DR: This is the first textbook dedicated to explaining how artificial intelligence techniques can be used in and for games, and how to use AI to play games, to generate content for games and to model players.
Journal ArticleDOI
A Panorama of Artificial and Computational Intelligence in Games
TL;DR: This paper attempts to give a high-level overview of the field of artificial and computational intelligence in games, with particular reference to how the different core research areas within this field inform and interact with each other, both actually and potentially.
Posted Content
Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network.
TL;DR: This paper trains a GAN to generate levels for Super Mario Bros using a level from the Video Game Level Corpus, and uses the champion A* agent from the 2009 Mario AI competition to assess whether a level is playable, and how many jumping actions are required to beat it.