282 research outputs found
Towards procedural strategy game generation : evolving complementary unit types
The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios, maps, and unit types. One of the main envisioned uses of SGDL, in combination with an evolutionary algorithm and appropriate fitness functions, is to allow the generation of complete new strategy games or variations of old ones. This paper presents a first version of SGDL, capable of describing unit types and their properties, together with plans for how it will be extended to other sub-domains of strategy games. As a proof of the viability of the idea and implementation, an experiment is presented where unit types are evolved so as to generate complementary properties. A fitness function based on Monte Carlo simulation of gameplay is devised to test complementarity.peer-reviewe
Spicing up map generation
We describe a search-based map generator for the classic real-time strategy game Dune 2. The generator is capable of creating playable maps in seconds, which can be used with a partial recreation of Dune 2 that has been implemented using the Strategy Game Description Language. Map genotypes are represented as low-resolution matrices, which are then converted to higher-resolution maps through a stochastic process involving cellular automata. Map phenotypes are evaluated using a set of heuristics based on the gameplay requirements of Dune 2.peer-reviewe
Fifth Aeon – A.I Competition and Balancer
Collectible Card Games (CCG) are one of the most popular types of games in both digital and physical space. Despite their popularity, there is a great deal of room for exploration into the application of artificial intelligence in order to enhance CCG gameplay and development. This paper presents Fifth Aeon a novel and open source CCG built to run in browsers and two A.I applications built upon Fifth Aeon. The first application is an artificial intelligence competition run on the Fifth Aeon game. The second is an automatic balancing system capable of helping a designer create new cards that do not upset the balance of an existing collectible card game. The submissions to the A.I competition include one that plays substantially better than the existing Fifth Aeon A.I with a higher winrate across multiple game formats. The balancer system also demonstrates an ability to automatically balance several types of cards against a wide variety of parameters. These results help pave the way to cheaper CCG development with more compelling A.I opponents
AI Researchers, Video Games Are Your Friends!
If you are an artificial intelligence researcher, you should look to video
games as ideal testbeds for the work you do. If you are a video game developer,
you should look to AI for the technology that makes completely new types of
games possible. This chapter lays out the case for both of these propositions.
It asks the question "what can video games do for AI", and discusses how in
particular general video game playing is the ideal testbed for artificial
general intelligence research. It then asks the question "what can AI do for
video games", and lays out a vision for what video games might look like if we
had significantly more advanced AI at our disposal. The chapter is based on my
keynote at IJCCI 2015, and is written in an attempt to be accessible to a broad
audience.Comment: in Studies in Computational Intelligence Studies in Computational
Intelligence, Volume 669 2017. Springe
Multiobjective exploration of the StarCraft map space
This paper presents a search-based method for
generating maps for the popular real-time strategy (RTS) game
StarCraft. We devise a representation of StarCraft maps suitable
for evolutionary search, along with a set of fitness functions
based on predicted entertainment value of those maps, as
derived from theories of player experience. A multiobjective
evolutionary algorithm is then used to evolve complete Star-
Craft maps based on the representation and selected fitness
functions. The output of this algorithm is a Pareto front
approximation visualizing the tradeoff between the several
fitness functions used, and where each point on the front
represents a viable map. We argue that this method is useful
for both automatic and machine-assisted map generation, and
in particular that the Pareto fronts are excellent design support
tools for human map designers.This research was supported in part by the Danish Research
Agency, Ministry of Science, Technology and Innovation;
project name: Adaptive Game Content Creation using
Computational Intelligence (AGameComIn); project number:
274-09-0083.peer-reviewe
Adaptive game level creation through rank-based interactive evolution
This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.The research is supported, in part, by the FP7 ICT project SIREN (project no: 258453) and by the FP7 ICT project C2Learn (project no: 318480).peer-reviewe
General general game AI
Arguably the grand goal of artificial intelligence
research is to produce machines with general intelligence: the
capacity to solve multiple problems, not just one. Artificial
intelligence (AI) has investigated the general intelligence capacity
of machines within the domain of games more than any other
domain given the ideal properties of games for that purpose:
controlled yet interesting and computationally hard problems.
This line of research, however, has so far focused solely on
one specific way of which intelligence can be applied to games:
playing them. In this paper, we build on the general game-playing
paradigm and expand it to cater for all core AI tasks within a
game design process. That includes general player experience
and behavior modeling, general non-player character behavior,
general AI-assisted tools, general level generation and complete
game generation. The new scope for general general game AI
beyond game-playing broadens the applicability and capacity of
AI algorithms and our understanding of intelligence as tested
in a creative domain that interweaves problem solving, art, and
engineering.peer-reviewe
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