1,758 research outputs found

    A panorama of artificial and computational intelligence in games

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    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

    Generative Design in Minecraft (GDMC), Settlement Generation Competition

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    This paper introduces the settlement generation competition for Minecraft, the first part of the Generative Design in Minecraft challenge. The settlement generation competition is about creating Artificial Intelligence (AI) agents that can produce functional, aesthetically appealing and believable settlements adapted to a given Minecraft map - ideally at a level that can compete with human created designs. The aim of the competition is to advance procedural content generation for games, especially in overcoming the challenges of adaptive and holistic PCG. The paper introduces the technical details of the challenge, but mostly focuses on what challenges this competition provides and why they are scientifically relevant.Comment: 10 pages, 5 figures, Part of the Foundations of Digital Games 2018 proceedings, as part of the workshop on Procedural Content Generatio

    Agents for educational games and simulations

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    This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

    Optimising Humanness: Designing the best human-like Bot for Unreal Tournament 2004

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    This paper presents multiple hybridizations of the two best bots on the BotPrize 2014 competition, which sought for the best humanlike bot playing the First Person Shooter game Unreal Tournament 2004. To this aim the participants were evaluated using a Turing test in the game. The work considers MirrorBot (the winner) and NizorBot (the second) codes and combines them in two different approaches, aiming to obtain a bot able to show the best behaviour overall. There is also an evolutionary version on MirrorBot, which has been optimized by means of a Genetic Algorithm. The new and the original bots have been tested in a new, open, and public Turing test whose results show that the evolutionary version of MirrorBot apparently improves the original bot, and also that one of the novel approaches gets a good humanness level.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

    Artificial and Computational Intelligence in Games (Dagstuhl Seminar 12191)

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    This report documents the program and the outcomes of Dagstuhl Seminar 12191 "Artificial and Computational Intelligence in Games". The aim for the seminar was to bring together creative experts in an intensive meeting with the common goals of gaining a deeper understanding of various aspects of artificial and computational intelligence in games, to help identify the main challenges in game AI research and the most promising venues to deal with them. This was accomplished mainly by means of workgroups on 14 different topics (ranging from search, learning, and modeling to architectures, narratives, and evaluation), and plenary discussions on the results of the workgroups. This report presents the conclusions that each of the workgroups reached. We also added short descriptions of the few talks that were unrelated to any of the workgroups

    Towards Believable Resource Gathering Behaviours in Real-time Strategy Games with a Memetic Ant Colony System

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    AbstractIn this paper, the resource gathering problem in real-time strategy (RTS) games, is modeled as a path-finding problem where game agents responsible for gathering resources, also known as harvesters, are only equipped with the knowledge of its immediate sur- roundings and must gather knowledge about the dynamics of the navigation graph that it resides on by sharing information and cooperating with other agents in the game environment. This paper proposed the conceptual modeling of a memetic ant colony system (MACS) for believable resource gathering in RTS games. In the proposed MACS, the harvester's path-finding and resource gathering knowledge captured are extracted and represented as memes, which are internally encoded as state transition rules (mem- otype), and externally expressed as ant pheromone on the graph edge (sociotype). Through the inter-play between the memetic evolution and ant colony, harvesters as memetic automatons spawned from an ant colony are able to acquire increasing level of capability in exploring complex dynamic game environment and gathering resources in an adaptive manner, producing consistent and impressive resource gathering behaviors

    Deep learning for video game playing

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    In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

    Evolving artificial neural networks applied to generate virtual characters

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    Computer game industry is one of the most prof­ itable nowadays. Although this industry has evolved fast in the last years in different fields, Artificial Intelligence (AI) seems to be stuck. Many games still make use of simple state machines to simulate AI. New models can be designed and proposed to replace this jurassic technique. In this paper we propose the use of Artificial Neural Networks (ANN) as a new model. ANN will be then in charge of receiving information from the game (sensors) and carry out actions (actuators) according to the information received. The search for the best ANN is a complex task that strongly affects the task performance while often requiring a high computational time. In this work, we present ADANN, a system for the automatic evolution and adaptation of artificial neural networks based on evolutionary ANN (EANN). This approach use Genetic Algorithm (GA) that evolves fully connected Artificial Neural Network. The testing game is called Unreal Tournament 2004. The new agent obtained has been put to the test jointly with CCBot3, the winner of BotPrize 2010 competition [1], and have showed a significant improvement in the humannesss ratio. Additionally, we have confronted our approach and CCBot3 (winner of BotPrize competition in 2010) to First-person believability assessment (BotPrize original judging protocol), demonstrating that the active involvement of the judge has a great impact in the recognition of human-like behaviour
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