Co-generation of game levels and game-playing agents
Co-generation of game levels and game-playing agents
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DOI：
10.1609/aiide.v16i1.7431
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Aaron Dharna;J. Togelius;L. Soros
中科院分区：
文献类型：
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作者：
Aaron Dharna;J. Togelius;L. Soros
开放性是人工生命研究的一个长期基石，是系统生成潜在的无限本体的能力，这些本体具有不断增加的新奇和复杂性。工程生成系统显示至少某种程度的这种能力是一个目标，明确的应用程序在游戏中的程序内容生成。配对开放式开拓者（POET）算法，迄今为止只探索了一个跨域，是一个共同进化的系统，同时产生的环境和代理，可以解决这些问题。本文介绍了一个POET启发的神经进化系统KreativityY（PINSKY）在游戏中，共同生成多个视频游戏和代理人玩他们的水平。该系统利用通用视频游戏人工智能（GVGAI）框架，为2D Atari风格的游戏Zelda和Solar Fox共同生成关卡和代理。结果表明，PINSKY的能力，生成课程的游戏水平，开辟了一个有前途的新途径的研究在交叉的程序内容生成和人工生命。同时，这些具有挑战性的游戏领域的结果突出了当前算法的局限性和改进的机会。
Open-endedness, a longstanding cornerstone of artificial life research, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. Engineering generative systems displaying at least some degree of this ability is a goal with clear applications to procedural content generation in games. The Paired Open-Ended Trailblazer (POET) algorithm, heretofore explored only in a biped walking domain, is a coevolutionary system that simultaneously generates environments and agents that can solve them. This paper introduces a POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) in games, which co-generates levels for multiple video games and agents that play them. This system leverages the General Video Game Artificial Intelligence (GVGAI) framework to enable co-generation of levels and agents for the 2D Atari-style games Zelda and Solar Fox. Results demonstrate the ability of PINSKY to generate curricula of game levels, opening up a promising new avenue for research at the intersection of procedural content generation and artificial life. At the same time, results in these challenging game domains highlight the limitations of the current algorithm and opportunities for improvement.