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Neuroevolution has proven effective at many re-inforcement learning tasks, including tasks with incomplete information and delayed rewards, but does not seem to scale well to high-dimensional controller representations, which are needed for tasks where the input is raw pixel data. We propose a novel method where we train an autoencoder to create a comparatively low-dimensional representation of the...
This paper presents a measure intended to quantify the relative strategic depth of games as experienced by human players. The measure is based on the complexity (number and specificity of rules) of a hierarchical knowledge base that is extracted from playtraces. As a proof-of-concept, we compute the proposed measure for three arcade-style games and compare the results to the strategic depth reportedly...
Popular topics in current research within the games community are general game playing and general video game playing. Both of these efforts seek to find relatively general purpose AI to play games. Within the optimization community we are approaching the 20th anniversary of the no free lunch theorem. In this paper we suggest reasons why a games version of a no free lunch result is probably not problematic...
Procedural Content Generation (PCG) can be a useful tool for aiding creativity in the process of designing game levels. Mixed-initiative level generation tools where a designer and an algorithm collaborate to iteratively generate game levels have been used for this purpose. However, it can be difficult for designers to work with tools that do not respond to the common language of games: game design...
The emergence of mobile games has caused a paradigm shift in the video-game industry. Game developers now have at their disposal a plethora of information on their players, and thus can take advantage of reliable models that can accurately predict player behavior and scale to huge datasets. Churn prediction, a challenge common to a variety of sectors, is particularly relevant for the mobile game industry,...
In a game it is often the case that there are multiple roles or types of actors with different goals. One possible target for automatic content generation is to create multiple different software agents for these distinct roles. This paper outlines a technique, based on the multiple worlds model, for creating such actors via evolution. The objective function is based on the performance of the actors...
Many games use dynamic difficulty adjustment (DDA) to promote the achievement of flow and consequent positive affective states. However, performance based DDA assume a specific ludic attitude: that of the hard-core gamer. An alternative approach is to apply affective computing techniques to monitor players adjust difficulty to achieve the desired affective state directly. Such an emotion-controlled...
Maintaining player immersion is a crucial step in making an enjoyable video game. One aspect of player immersion is the level of challenge the game presents to the player. To avoid a mismatch between a player's skill and the challenge of a game, which can result from traditional manual difficulty selection mechanisms (e.g. easy, medium, hard), Dynamic Difficulty Adjustment (DDA) has previously been...
In this paper we discuss our recent approach for evolving a diverse set of agents for both the Pac-Man and the Ghost Team track of the current Ms. Pac-Man vs. Ghost Team competition. We used genetic programming for generating various agents, which were distributed in multiple populations. The optimization includes cooperative and adversarial subtasks, such that Pac-Man is constantly competing against...
Cooperative games with partial observability are a challenging domain for AI research, especially when the AI should cooperate with a human player. In this paper we investigate one such game, the award-winning card game Hanabi, which has been studied by other researchers before. We present an agent designed to play better with a human cooperator than these previous results by basing it on communication...
In this paper we explore the hybrid application of evolutionary computation and artificial neural networks in the development of intelligent systems able to solve the problem of approximating the optimal strategy in a tile-matching puzzle game. Three intelligent systems are proposed: an evolutionary heuristic technique, artificial neural networks, and a hybrid approach that combines both. Results...
In this article we propose a game design approach to build context adaptive games. This approach is based on a model of the game structure and a generic adaptation model. Our method consists of designing different game scenarios involving different gameplay for the game then, game engine selects and proposes the appropriate one according to the context. We have conducted a pilot experiment in order...
Game AI literature has looked at applying various enhancements to Rolling Horizon Evolutionary methods or creating hybrids with popular tree search methods for an improved performance. However, these techniques have not been analyzed in depth in a general setting under the same conditions and restrictions. This paper proposes a fair juxtaposition of four enhancements applied to different parts of...
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