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Highly interactive educational contents using games are widely used, and objective evaluation of these real-time is required. In order to do this, we recorded brain waves under several interactive tasks and game situations in subjects and tried to analyze them. As a result, we focused on the proportion of beta wave components, which suggested the possibility of being able to distinguish between simple...
The interplay between an intrusion detection mechanism and a false data injection mechanism is investigated in this work in the context of an infinite horizon Linear Quadratic Gaussian Control System (LQG). A game theoretic framework is proposed between the attacker, who wishes to impair the operation of the control system while remaining stealthy, and the detector, who wishes to detect the presence...
Open systems are often submitted to rules and norms in an attempt to guarantee its well functioning and prevent selfish (e.g. free-riding) or unsustainable (e.g. tragedy of the commons) behaviour. Associated to the norms, there is usually also a sanctioning structure responsible to prevent and punish non-compliance enforcing conformity. Without disregarding the importance of these structures, we investigate...
The use of games in non-leisure contexts is referred to as serious games. The tradition of using games for purposes beyond entertainment goes back a long time before digital games. However, with the advent of digital games, serious games development has become an issue of both game design and technology development in various combinations. This paper presents a literature review of what types of topics...
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...
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...
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...
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...
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...
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...
This study investigates how genetic programs can be effectively used in a multi-agent system to allow agents to learn to communicate. Using the pursuit domain and a co-operative learning strategy, communication protocols are compared as multiple predator agents learn the meaning of commands in order to achieve their common goal of first finding and then tracking prey. The outcome of this study reveals...
In this paper, we propose a method for optimizing the game live service. Especially, we focus on improving user retention. Firstly, we define player churn in the game and extract features that contain the properties of the player churn from the game logs. And then we evaluate the importance of features using random forest in classification. Finally, we build association matrix between features and...
Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearthstone — a popular collectible card (CC) (video) game developed by Blizzard Entertainment — two players first construct their own card decks from over 1,000 different cards and then draw and play cards to cast spells, select weapons,...
In game artificial intelligence (AI), two common directions for developing non-human computer players are strong AI and human-like AI. Human-like AI aims at making computer agents behave like humans. In this direction, NeuroEvolution (NE), which is a combination of an artificial neural network (ANN) and an evolutionary algorithm (EA), had been frequently used to a make computer agent to behave like...
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