{"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/shallow-decision-making-analysis-in-general","title":"Shallow decision-making analysis in General Video Game Playing","arxiv_id":"1806.01151","date":"2018-06-04","proceeding":null,"authors":["Ivan Bravi","Jialin Liu","Diego Perez-Liebana","Simon Lucas"],"abstract":"The General Video Game AI competitions have been the testing ground for\nseveral techniques for game playing, such as evolutionary computation\ntechniques, tree search algorithms, hyper heuristic based or knowledge based\nalgorithms. So far the metrics used to evaluate the performance of agents have\nbeen win ratio, game score and length of games. In this paper we provide a\nwider set of metrics and a comparison method for evaluating and comparing\nagents. The metrics and the comparison method give shallow introspection into\nthe agent's decision making process and they can be applied to any agent\nregardless of its algorithmic nature. In this work, the metrics and the\ncomparison method are used to measure the impact of the terms that compose a\ntree policy of an MCTS based agent, comparing with several baseline agents. The\nresults clearly show how promising such general approach is and how it can be\nuseful to understand the behaviour of an AI agent, in particular, how the\ncomparison with baseline agents can help understanding the shape of the agent\ndecision landscape. The presented metrics and comparison method represent a\nstep toward to more descriptive ways of logging and analysing agent's\nbehaviours.","url_abs":"http://arxiv.org/abs/1806.01151v1","url_pdf":"http://arxiv.org/pdf/1806.01151v1.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":"shallow-decision-making-analysis-in-general","repo_url":"https://github.com/ivanbravi/ShadowingAgentForGVGAI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"ai-agent","task_name":"AI Agent"},{"task_slug":"decision-making","task_name":"Decision Making"},{"task_slug":"descriptive","task_name":"Descriptive"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}