3,026 research outputs found
Automated Game Design Learning
While general game playing is an active field of research, the learning of
game design has tended to be either a secondary goal of such research or it has
been solely the domain of humans. We propose a field of research, Automated
Game Design Learning (AGDL), with the direct purpose of learning game designs
directly through interaction with games in the mode that most people experience
games: via play. We detail existing work that touches the edges of this field,
describe current successful projects in AGDL and the theoretical foundations
that enable them, point to promising applications enabled by AGDL, and discuss
next steps for this exciting area of study. The key moves of AGDL are to use
game programs as the ultimate source of truth about their own design, and to
make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201
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
A panorama of artificial and computational intelligence in games
This paper attempts to give a high-level overview
of the field of artificial and computational intelligence (AI/CI)
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. We identify ten main
research areas within this field: NPC behavior learning, search
and planning, player modeling, games as AI benchmarks,
procedural content generation, computational narrative, believable
agents, AI-assisted game design, general game artificial
intelligence and AI in commercial games. We view and analyze
the areas from three key perspectives: (1) the dominant AI
method(s) used under each area; (2) the relation of each area
with respect to the end (human) user; and (3) the placement of
each area within a human-computer (player-game) interaction
perspective. In addition, for each of these areas we consider how
it could inform or interact with each of the other areas; in those
cases where we find that meaningful interaction either exists or
is possible, we describe the character of that interaction and
provide references to published studies, if any. We believe that
this paper improves understanding of the current nature of the
game AI/CI research field and the interdependences between
its core areas by providing a unifying overview. We also believe
that the discussion of potential interactions between research
areas provides a pointer to many interesting future research
projects and unexplored subfields.peer-reviewe
Dataohjattu sekventiaalinen Monte Carlo -liikesynteesi
Animation in video games is composed of motion segments created by animators, and of motion synthesis methods, which combine and extend the motion segments for emerging gameplay situations. Current video games typically synthesize motion kinematically with no regard to dynamics, causing immersion-breaking motion artifacts. By contrast, physically-based methods synthesize motions by simulating physics, which ensures physical correctness.
This thesis extends sequential Monte Carlo motion synthesis, a physically-based method, to use animator-authored reference animations for guiding the synthesis. An offline component is developed, which robustly tracks various types of kinematic reference animations by controlling a simulated physical character. The tracking results are gathered as a training set for a machine learning component, which directs the sequential Monte Carlo sampling used for online motion synthesis.
For machine learning, the approximate nearest neighbors, locally weighted regression, mixture of regressors, and self-organizing map methods are implemented and compared. A product distribution sampling scheme is developed to efficiently combine machine learning with optimization. Additionally, a factorized formulation of the learning problem is presented and implemented.
The system is evaluated with an interactive locomotion test case. Given a single kinematic reference animation depicting running in a straight line, the system is able to synthesize physically-valid motion for turning and running on uneven terrain.Videopelien animaatio muodostuu animaattoreiden luomista animaatioista, sekä liikesynteesimenetelmistä, jotka yhdistävät ja laajentavat luotuja animaatioita pelissä syntyviin uusiin tilanteisiin. Nykyiset videopelit käyttävät pääsääntöisesti menetelmiä, jotka syntetisoivat liikettä kinemaattisesti huomioimatta dynamiikkaa, mikä johtaa immersiota heikentäviin virheisiin. Vaihtoehtoisesti liikesynteesiin voidaan käyttää fysiikkaan perustuvia menetelmiä, joissa fysiikan simuloinnilla varmistetaan liikkeiden fysikaalinen toteutettavuus.
Tämä diplomityö laajentaa fysiikkaan perustuvaa sekventiaalista Monte Carlo -liikesynteesimenetelmää ohjaamalla synteesiä animaattoreiden luomilla referenssianimaatioilla. Työssä kehitetään erillinen komponentti, joka kykenee seuraamaan monenlaisia kinemaattisia referenssianimaatioita kontrolloimalla simuloitua fysikaalista hahmomallia. Seurannan tulokset kootaan opetusdataksi koneoppimiskomponentissa, joka ohjaa interaktiiviseen liikesynteesiin käytettävää sekventiaalista Monte Carlo -otantaa.
Koneoppimiseen sovelletaan approksimatiivista lähimmän naapurin menetelmää, paikallisesti painotettua regressiota, regressorisekoitemallia ja itseorganisoituvaa karttaa. Koneoppiminen yhdistetään tehokkaasti optimointiin käyttämällä otantaa todennäköisyysjakaumien tulosta. Oppimisongelmaan sovelletaan myös tekijöihin jaettua muotoa.
Järjestelmää arvioidaan interaktiivisella demonstraatiolla, jossa käytetään yksittäistä suoraa juoksua esittävää kinemaattista referenssianimaatiota. Järjestelmä kykenee syntetisoimaan referenssin avulla käännöksiä ja juoksua epätasaisella pinnalla
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