{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/clear-the-fog-combat-value-assessment-in","title":"Clear the Fog: Combat Value Assessment in Incomplete Information Games with Convolutional Encoder-Decoders","arxiv_id":"1811.12627","date":"2018-11-30","proceeding":null,"authors":["Hyungu Kahng","Yonghyun Jeong","Yoon Sang Cho","Gonie Ahn","Young Joon Park","Uk Jo","Hankyu Lee","Hyungrok Do","Junseung Lee","Hyunjin Choi","Iljoo Yoon","Hyunjae Lee","Daehun Jun","Changhyeon Bae","Seoung Bum Kim"],"abstract":"StarCraft, one of the most popular real-time strategy games, is a compelling\nenvironment for artificial intelligence research for both micro-level unit\ncontrol and macro-level strategic decision making. In this study, we address an\neminent problem concerning macro-level decision making, known as the\n'fog-of-war', which rises naturally from the fact that information regarding\nthe opponent's state is always provided in the incomplete form. For intelligent\nagents to play like human players, it is obvious that making accurate\npredictions of the opponent's status under incomplete information will increase\nits chance of winning. To reflect this fact, we propose a convolutional\nencoder-decoder architecture that predicts potential counts and locations of\nthe opponent's units based on only partially visible and noisy information. To\nevaluate the performance of our proposed method, we train an additional\nclassifier on the encoder-decoder output to predict the game outcome (win or\nlose). Finally, we designed an agent incorporating the proposed method and\nconducted simulation games against rule-based agents to demonstrate both\neffectiveness and practicality. All experiments were conducted on actual game\nreplay data acquired from professional players.","url_abs":"http://arxiv.org/abs/1811.12627v2","url_pdf":"http://arxiv.org/pdf/1811.12627v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"clear-the-fog-combat-value-assessment-in","repo_url":"https://github.com/TeamSAIDA/SAIDA","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"real-time-strategy-games","task_name":"Real-Time Strategy Games"},{"task_slug":"starcraft","task_name":"Starcraft"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.12627","atlas_url":"https://app.syntology.ai/?focus=1811.12627","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}