270 research outputs found
Proceedings of the SAB'06 Workshop on Adaptive Approaches for Optimizing Player Satisfaction in Computer and Physical Games
These proceedings contain the papers presented at the Workshop on Adaptive approaches
for Optimizing Player Satisfaction in Computer and Physical Games held at the Ninth
international conference on the Simulation of Adaptive Behavior (SAB’06): From
Animals to Animats 9 in Rome, Italy on 1 October 2006.
We were motivated by the current state-of-the-art in intelligent game design using
adaptive approaches. Artificial Intelligence (AI) techniques are mainly focused on
generating human-like and intelligent character behaviors. Meanwhile there is generally
little further analysis of whether these behaviors contribute to the satisfaction of the
player. The implicit hypothesis motivating this research is that intelligent opponent
behaviors enable the player to gain more satisfaction from the game. This hypothesis may
well be true; however, since no notion of entertainment or enjoyment is explicitly
defined, there is therefore little evidence that a specific character behavior generates
enjoyable games.
Our objective for holding this workshop was to encourage the study, development,
integration, and evaluation of adaptive methodologies based on richer forms of humanmachine
interaction for augmenting gameplay experiences for the player. We wanted to
encourage a dialogue among researchers in AI, human-computer interaction and
psychology disciplines who investigate dissimilar methodologies for improving gameplay
experiences. We expected that this workshop would yield an understanding of state-ofthe-
art approaches for capturing and augmenting player satisfaction in interactive systems
such as computer games.
Our invited speaker was Hakon Steinø, Technical Producer of IO-Interactive, who
discussed applied AI research at IO-Interactive, portrayed the future trends of AI in
computer game industry and debated the use of academic-oriented methodologies for
augmenting player satisfaction. The sessions of presentations and discussions where
classified into three themes: Adaptive Learning, Examples of Adaptive Games and Player
Modeling.
The Workshop Committee did a great job in providing suggestions and informative
reviews for the submissions; thank you! This workshop was in part supported by the
Danish National Research Council (project no: 274-05-0511). Finally, thanks to all the
participants; we hope you found this to be useful!peer-reviewe
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
From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI
This paper reviews the field of Game AI, which not only deals with creating
agents that can play a certain game, but also with areas as diverse as creating
game content automatically, game analytics, or player modelling. While Game AI
was for a long time not very well recognized by the larger scientific
community, it has established itself as a research area for developing and
testing the most advanced forms of AI algorithms and articles covering advances
in mastering video games such as StarCraft 2 and Quake III appear in the most
prestigious journals. Because of the growth of the field, a single review
cannot cover it completely. Therefore, we put a focus on important recent
developments, including that advances in Game AI are starting to be extended to
areas outside of games, such as robotics or the synthesis of chemicals. In this
article, we review the algorithms and methods that have paved the way for these
breakthroughs, report on the other important areas of Game AI research, and
also point out exciting directions for the future of Game AI
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
Learning Human Behavior From Observation For Gaming Applications
The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value
Using machine learning techniques to create AI controlled players for video games
This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative analysi
Overview of deep reinforcement learning in partially observable multi-agent environment of competitive online video games
In the late 2010’s classical games of Go, Chess and Shogi have been considered ’solved’ by deep
reinforcement learning AI agents. Competitive online video games may offer a new, more challenging environment for deep reinforcement learning and serve as a stepping stone in a path to real
world applications. This thesis aims to give a short introduction to the concepts of reinforcement
learning, deep networks and deep reinforcement learning. Then the thesis proceeds to look into few
popular competitive online video games and to the general problems of AI development in these
types of games. Deep reinforcement learning algorithms, techniques and architectures used in the
development of highly competitive AI agents in Starcraft 2, Dota 2 and Quake 3 are overviewed.
Finally, the results are looked into and discussed
Autonomous characters in virtual environments: The technologies involved in artificial life and their affects on perceived intelligence and playability of computer games
Computer games are viewed by academics as un֊grounded hack and patch experiments. "The industry lacks the formalism and requirement for a "perfect" solution often necessary in the academic world " [Woob]. Academic Artifical Intelligence (AI) is often viewed as un-implementable and narrow minded by the majority of ทon-AI programmer. "Historically, AI tended to be focused, containing detailed problems and domain-specific techniques. This focus makes for easier study - or engineering - of particular solutions. " [СһаОЗ .By implementing several well known AI techniques into the same gaming environment and judging users reactions this project aims to make links between the academic nature of AI, as well as investigate the nature of practical implementation in a gaming environment. An online Java implemented version of the 1970'ร classic Space Invaders has been developed and tested, with the Aliens being controlled by 6 different approaches to modelling AI functions. In total information from 334 individuals games was recorded. Different types of games AI can create highly varied gaming experience as highlighted by the range of values and high standard deviation values seen in the results. The link between complex behaviour, complex control systems and perceived intelligence was not supported. A positive correlation identified between how fun the users found the game and how intelligent they perceived the Aliens to be, would seem to be logical. As games get visually more and more impressive, the need for intelligent characters cannot be denied because it is one of the few way in which games can set themselves apart from the competition. Conclusions identified that computer games must remain focussed on their end- goal, that of producing a fun game. Whilst complex and clever AI can help to achieve it, the AI itself can never overshadow the end result
Programming Robosoccer agents by modelling human behavior
The Robosoccer simulator is a challenging environment for artificial intelligence, where a human has to program a team of agents and introduce it into a soccer virtual environment. Most usually, Robosoccer agents are programmed by hand. In some cases, agents make use of Machine learning (ML) to adapt and predict the behavior of the opposite team, but the bulk of the agent has been preprogrammed. The main aim of this paper is to transform Robosoccer into an interactive game and let a human control a Robosoccer agent. Then ML techniques can be used to model his/her behavior from training instances generated during the play. This model will be used later to control a Robosoccer agent, thus imitating the human behavior. We have focused our research on low-level behavior, like looking for the ball, conducting the ball towards the goal, or scoring in the presence of opponent players. Results have shown that indeed, Robosoccer agents can be controlled by programs that model human play.Publicad
Increasing generality in machine learning through procedural content generation
Procedural Content Generation (PCG) refers to the practice, in videogames and
other games, of generating content such as levels, quests, or characters
algorithmically. Motivated by the need to make games replayable, as well as to
reduce authoring burden, limit storage space requirements, and enable
particular aesthetics, a large number of PCG methods have been devised by game
developers. Additionally, researchers have explored adapting methods from
machine learning, optimization, and constraint solving to PCG problems. Games
have been widely used in AI research since the inception of the field, and in
recent years have been used to develop and benchmark new machine learning
algorithms. Through this practice, it has become more apparent that these
algorithms are susceptible to overfitting. Often, an algorithm will not learn a
general policy, but instead a policy that will only work for a particular
version of a particular task with particular initial parameters. In response,
researchers have begun exploring randomization of problem parameters to
counteract such overfitting and to allow trained policies to more easily
transfer from one environment to another, such as from a simulated robot to a
robot in the real world. Here we review the large amount of existing work on
PCG, which we believe has an important role to play in increasing the
generality of machine learning methods. The main goal here is to present RL/AI
with new tools from the PCG toolbox, and its secondary goal is to explain to
game developers and researchers a way in which their work is relevant to AI
research
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