Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolution
| Autor: | Kazuto Fukuchi, Jun Sakuma, Youhei Akimoto, Takumi Tanabe |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science business.industry Stability (learning theory) 0102 computer and information sciences 02 engineering and technology Latent variable Machine learning computer.software_genre 01 natural sciences Image (mathematics) I.2.1 Artificial Intelligence (cs.AI) 010201 computation theory & mathematics Encoding (memory) 0202 electrical engineering electronic engineering information engineering Feature (machine learning) 020201 artificial intelligence & image processing Artificial intelligence business Representation (mathematics) Video game computer Generator (mathematics) |
| Zdroj: | GECCO |
| DOI: | 10.48550/arxiv.2104.06106 |
| Popis: | Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural. Comment: The Genetic and Evolutionary Computation Conference 2021 (GECCO '21) |
| Databáze: | OpenAIRE |
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