2,052 research outputs found
EvoTanks: co-evolutionary development of game-playing agents
This paper describes the EvoTanks research project, a continuing attempt to develop strong AI players for a primitive 'Combat' style video game using evolutionary computational methods with artificial neural networks. A small but challenging feat due to the necessity for agent's actions to rely heavily on opponent behaviour. Previous investigation has shown the agents are capable of developing high performance behaviours by evolving against scripted opponents; however these are local to the trained opponent. The focus of this paper shows results from the use of co-evolution on the same population. Results show agents no longer succumb to trappings of local maxima within the search space and are capable of converging on high fitness behaviours local to their population without the use of scripted opponents
Neuroevolution in Games: State of the Art and Open Challenges
This paper surveys research on applying neuroevolution (NE) to games. In
neuroevolution, artificial neural networks are trained through evolutionary
algorithms, taking inspiration from the way biological brains evolved. We
analyse the application of NE in games along five different axes, which are the
role NE is chosen to play in a game, the different types of neural networks
used, the way these networks are evolved, how the fitness is determined and
what type of input the network receives. The article also highlights important
open research challenges in the field.Comment: - Added more references - Corrected typos - Added an overview table
(Table 1
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
Player-AI Interaction: What Neural Network Games Reveal About AI as Play
The advent of artificial intelligence (AI) and machine learning (ML) bring
human-AI interaction to the forefront of HCI research. This paper argues that
games are an ideal domain for studying and experimenting with how humans
interact with AI. Through a systematic survey of neural network games (n = 38),
we identified the dominant interaction metaphors and AI interaction patterns in
these games. In addition, we applied existing human-AI interaction guidelines
to further shed light on player-AI interaction in the context of AI-infused
systems. Our core finding is that AI as play can expand current notions of
human-AI interaction, which are predominantly productivity-based. In
particular, our work suggests that game and UX designers should consider flow
to structure the learning curve of human-AI interaction, incorporate
discovery-based learning to play around with the AI and observe the
consequences, and offer users an invitation to play to explore new forms of
human-AI interaction
A scheme for creating digital entertainment with substance
Computer games constitute a major branch of the
entertainment industry nowadays. The financial
and research potentials of making games more appealing (or else more interesting) are more than impressive. Interactive and cooperative characters can
generate more realism in games and satisfaction for
the player. Moreover, on-line (while play) machine
learning techniques are able to produce characters
with intelligent capabilities useful to any gameâs
context. On that basis, richer human-machine interaction through real-time entertainment, player
and emotional modeling may provide means for
effective adjustment of the non-player charactersâ
behavior in order to obtain games of substantial
entertainment. This paper introduces a research
scheme for creating NPCs that generate entertaining games which is based interdisciplinary on the
aforementioned areas of research and is foundationally supported by several pilot studies on testbed games. Previous work and recent results are
presented within this framework.peer-reviewe
Evolutionary Machine Learning and Games
Evolutionary machine learning (EML) has been applied to games in multiple
ways, and for multiple different purposes. Importantly, AI research in games is
not only about playing games; it is also about generating game content,
modeling players, and many other applications. Many of these applications pose
interesting problems for EML. We will structure this chapter on EML for games
based on whether evolution is used to augment machine learning (ML) or ML is
used to augment evolution. For completeness, we also briefly discuss the usage
of ML and evolution separately in games.Comment: 27 pages, 5 figures, part of Evolutionary Machine Learning Book
(https://link.springer.com/book/10.1007/978-981-99-3814-8
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