102 research outputs found
Towards the automatic generation of card games through Grammar-Guided Genetic Programming
We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality game
Game AI revisited
More than a decade after the early research efforts on the
use of artificial intelligence (AI) in computer games and the
establishment of a new AI domain the term “game AI” needs
to be redefined. Traditionally, the tasks associated with
game AI revolved around non player character (NPC) behavior at different levels of control, varying from navigation
and pathfinding to decision making. Commercial-standard
games developed over the last 15 years and current game
productions, however, suggest that the traditional challenges
of game AI have been well addressed via the use of sophisticated AI approaches, not necessarily following or inspired
by advances in academic practices. The marginal penetration of traditional academic game AI methods in industrial
productions has been mainly due to the lack of constructive communication between academia and industry in the
early days of academic game AI, and the inability of academic game AI to propose methods that would significantly
advance existing development processes or provide scalable
solutions to real world problems. Recently, however, there
has been a shift of research focus as the current plethora
of AI uses in games is breaking the non-player character AI
tradition. A number of those alternative AI uses have already shown a significant potential for the design of better
games.
This paper presents four key game AI research areas that
are currently reshaping the research roadmap in the game
AI field and evidently put the game AI term under a new
perspective. These game AI flagship research areas include
the computational modeling of player experience, the procedural generation of content, the mining of player data on
massive-scale and the alternative AI research foci for enhancing NPC capabilities.peer-reviewe
Neuroevolutionary constrained optimization for content creation
This paper presents a constraint-based procedural
content generation (PCG) framework used for the creation of
novel and high-performing content. Specifically, we examine
the efficiency of the framework for the creation of spaceship
design (hull shape and spaceship attributes such as weapon and
thruster types and topologies) independently of game physics
and steering strategies. According to the proposed framework,
the designer picks a set of requirements for the spaceship
that a constrained optimizer attempts to satisfy. The constraint
satisfaction approach followed is based on neuroevolution;
Compositional Pattern-Producing Networks (CPPNs) which
represent the spaceship’s design are trained via a constraintbased
evolutionary algorithm. Results obtained in a number
of evolutionary runs using a set of constraints and objectives
show that the generated spaceships perform well in movement,
combat and survival tasks and are also visually appealing.peer-reviewe
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
Game analytics - maximizing the value of player data
During the years of the Information Age, technological advances in the computers,
satellites, data transfer, optics, and digital storage has led to the collection of an
immense mass of data on everything from business to astronomy, counting on the
power of digital computing to sort through the amalgam of information and generate meaning from the data. Initially, in the 1970s and 1980s of the previous century,
data were stored on disparate structures and very rapidly became overwhelming. The
initial chaos led to the creation of structured databases and database management
systems to assist with the management of large corpuses of data, and notably, the
effective and efficient retrieval of information from databases. The rise of the database management system increased the already rapid pace of information
gathering.peer-reviewe
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