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This paper presents a procedural level generation algorithm for physics-based puzzle games similar to Angry Birds. The proposed algorithm is capable of creating varied, stable and solvable levels consisting of multiple self-contained structures placed throughout a 2D area. The work presented in this paper builds and improves upon a previous level generation algorithm, enhancing it in several ways...
Recently, the introduction of vision-based deep Q learning demonstrated successful results in Atari, and Visual Doom AI platform. Unlike the previous study, the fighting game assumes two players with a relatively large number of actions. In this study, we propose to use deep Q Networks (DQN) for the visual fighting game AI competitions. The number of actions was reduced to 11 and the sensitivity of...
The use of statistical and machine learning approaches, such as Markov chains, for procedural content generation (PCG) has been growing in recent years in the field of Game AI. However, many of these level generation approaches account for only the structural properties of the levels. We developed multi-layered representations of levels, where each layer is designed to capture distinct gameplay information...
Replicator equations are regularly used to predict how strategies evolve in social dilemmas. These predictions are based on comparisons between the fitness of a strategy and the average population fitness. Unfortunately, fitness comparisons alone don't provide much insight into how or why individuals choose to cooperate. To overcome this limitation in replicator equations we developed a zero order...
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...
We present the Showdown AI Competition, a game-based AI competition built around a clone of the popular game Pokemon. This is a game of turn-based team battle, where the objective is to defeat an opponent team using clever combinations of creatures and their abilities. The gameplay is reminiscent of computer role-playing game battles and collectible card games. The game has characteristics, such as...
The domain of text-based adventure games has been recently established as a new challenge of creating the agent that is both able to understand natural language, and acts intelligently in text-described environments. In this paper, we present our approach to tackle the problem. Our agent, named Golovin, takes advantage of the limited game domain. We use genre-related corpora (including fantasy books...
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...
General Video Game Playing (GVGP) is a problem where the objective is to create an agent that can play multiple games with different properties successfully with no prior knowledge about them. Being an important sub-field in General Artificial Intelligence, GVGP has drawn a considerable amount of interest, and the research in this field got intensified with the release of General Video Game AI framework...
In this paper, we present an agent that learns Hierarchical Task Network (HTN) knowledge from observing a player performing actions in Minecraft. From these observations, the agent learns the tasks that the player pursues and how to achieve these tasks. We present an HTN learning algorithm and report on experiments of the agent assisting a player performing tasks in Minecraft.
Nowadays machine learning has attracted much attention. In order to apply it to various problems without relearning, its generalization ability is needed. Geometry Friends is a puzzle game where a player has to collect all targets in a two-dimensional world, and it is used in some artificial intelligence competitions. Although sufficient generalization ability is needed to apply the machine learning...
Location-Based games (LBGs) are a subtype of digital games that uses the location of players as a key component for playability, including changes to the game state. However, a significant challenge that threatens the development and popularization of LBGs is the game balancing. Since LBGs rely on players' location, it is hard to manually design interactions, challenges, and game scenarios for each...
In this paper, we introduce a Monte-Carlo tree search (MCTS) approach for the game "Hearthstone: Heroes of Warcraft". We argue that, in light of the challenges posed by the game (such as uncertainty and hidden information), Monte Carlo tree search offers an appealing alternative to existing AI players. Additionally, by enriching MCTS with a properly constructed heuristic, it is possible...
The General Video Game AI Framework has featured multiple games and several tracks since the first competition in 2014. Although the games of the framework are very assorted in nature, there is an underlying commonality with respect to the physics that govern the game: all of them are based on a grid where the sprites make discrete movements, which is not expressive enough to cover any meaningful...
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...
StarCraft is a real-time strategy game, which has a large state space, and commonly features two opposing players, capable of acting simultaneously. One of the aspects of the game is resource gathering. Each agent playing StarCraft has to gather minerals from nearby mineral field in order to produce more units. The more resources can be gathered, the larger the army is to attack the opponent and win...
This paper investigates the potential of combining deep learning and neuroevolution to create a bot for a simple first person shooter (FPS) game capable of aiming and shooting based on high-dimensional raw pixel input. The deep learning component is responsible for visual recognition and translating raw pixels to compact feature representations, while the evolving network takes those features as inputs...
Recently, the deep reinforcement learning has shown successful outcomes in classic video games (e.g., ATARI) and visual doom competition. Although it's very powerful, it suffers from very long learning time to generalize its performance. For example, it takes about 7~15 days to produce a good controller for ATARI games with state-of-the art GPUs. In this work, we propose to speed up the visual-based...
Human action recognition is currently one of the hottest areas in pattern recognition and machine intelligence. Its applications vary from console and exertion gaming and human computer interaction to automated surveillance and assistive environments. In this paper, we present a novel feature extraction method for action recognition, extending the capabilities of the Trace transform to the 3D domain...
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