[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-188015-105":53,"doc-detail-188015-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","tue-eindhoven-university-of-technology","TU/e EINDHOVEN UNIVERSITY OF TECHNOLOGY","","This document appears to be a research presentation or technical report related to the Eindhoven University of Technology (TU/e), likely focusing on AI and multi-agent systems within a gaming or simulation context, possibly involving Minecraft. The content is heavily visual, with diagrams and screenshots that suggest a technical or academic audience. The diagram labeled \"Benchmark Module\" indicates a system for evaluating agent performance, featuring components like a Server Controller, PillagerBench Core, and Bridge Controller, interfacing with a \"Bridge Module\" that connects to a \"Minecraft Server\" within the \"Environment Module.\" This points to a structured approach for testing and comparing AI agents in a controlled environment. Further visuals highlight specific game-like scenarios or applications: \"LLM Multi-Agent System\" discusses the use of large language models and agent opponent models; \"Mushroom War Game\" and \"Dash & Dine Game\" present distinct game scenarios with associated agent objectives, such as optimizing task allocations and adapting to opponent strategies; and \"Built-in AI Opponents\" showcases rule-based policies and varied strategies for AI adversaries. The presence of the TU/e logo suggests this work is affiliated with the university. The overall theme revolves around the development, evaluation, and application of AI agents, particularly in interactive and competitive environments, with a strong emphasis on large language models and multi-agent interactions.",{"@graph":63,"@context":118},[64,80,101],{"@type":65,"itemListElement":66},"BreadcrumbList",[67,71,74,77],{"item":68,"name":69,"@type":70,"position":9},"https://docshare.wps.com","Home","ListItem",{"item":72,"name":10,"@type":70,"position":73},"https://docshare.wps.com/template/",2,{"item":75,"name":51,"@type":70,"position":76},"https://docshare.wps.com/template/general/",3,{"item":78,"name":59,"@type":70,"position":79},"https://docshare.wps.com/template/tue-eindhoven-university-of-technology/188015/",4,{"url":78,"name":59,"@type":81,"image":82,"author":87,"headline":59,"publisher":90,"fileFormat":93,"inLanguage":57,"description":61,"dateModified":94,"datePublished":95,"encodingFormat":93,"isAccessibleForFree":96,"interactionStatistic":97},"DigitalDocument",{"url":83,"@type":84,"width":85,"height":86},"https://docshare.wps.com/thumbnails/tue-eindhoven-university-of-technology/188015.png","ImageObject",442,249,{"name":88,"@type":89},"Aria Callaghan","Person",{"url":68,"name":91,"@type":92},"DocShare","Organization","application/pdf","2026-09-28","2026-09-02",true,{"@type":98,"interactionType":99,"userInteractionCount":79},"InteractionCounter",{"@type":100},"ViewAction",{"@type":102,"mainEntity":103},"FAQPage",[104,110,114],{"name":105,"@type":106,"acceptedAnswer":107},"What is the primary function of the Benchmark Module?","Question",{"text":108,"@type":109},"The Benchmark Module is designed to evaluate the performance of AI agents. It includes components like a Server Controller, PillagerBench Core, and Bridge Controller to manage and orchestrate the benchmarking process.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What role does the Bridge Module play in the system?",{"text":113,"@type":109},"The Bridge Module acts as an interface, connecting the benchmark system (via Express.js and Bridge Manager) to the Minecraft Server within the Environment Module, facilitating communication and data exchange.",{"name":115,"@type":106,"acceptedAnswer":116},"What are the main game scenarios or applications presented in the document?",{"text":117,"@type":109},"The document outlines three main applications: an LLM Multi-Agent System utilizing large language models, the \"Mushroom War Game\" focusing on task allocation optimization, and the \"Dash & Dine Game\" which explores agent adaptation to opponent strategies, along with built-in AI opponents featuring rule-based policies and varied strategies.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},188015,1790442781,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":60,"file_id":130,"file_url":131,"file_type":132,"file_size":133,"view_count":79,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":134,"language":135,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":136,"faqs":137,"seo_title":138,"seo_description":61,"update_tm":139,"read_time":40},962084926284,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","cbCaip7pejtGkpQK","https://ap.wps.com/l/cbCaip7pejtGkpQK","pdf",7046414,52,"English","# Benchmark Module\n## Server Controller\n## PillagerBench Core\n## Bridge Controller\n# Bridge Module\n## Express.js\n## Bridge Manager\n## Mineflayer\n# Environment Module\n## Minecraft Server\n# LLM Multi-Agent System\n## Large Language Models\n## Opponent Model Module\n# Mushroom War Game\n## How do agents optimize task allocations?\n# Dash & Dine Game\n## How do agents adapt to opponent strategies?\n# Built-in AI Opponents\n## Rule-based Policy\n## Varied Strategies","[{\"question\":\"What is the primary function of the Benchmark Module?\",\"answer\":\"The Benchmark Module is designed to evaluate the performance of AI agents. It includes components like a Server Controller, PillagerBench Core, and Bridge Controller to manage and orchestrate the benchmarking process.\"},{\"question\":\"What role does the Bridge Module play in the system?\",\"answer\":\"The Bridge Module acts as an interface, connecting the benchmark system (via Express.js and Bridge Manager) to the Minecraft Server within the Environment Module, facilitating communication and data exchange.\"},{\"question\":\"What are the main game scenarios or applications presented in the document?\",\"answer\":\"The document outlines three main applications: an LLM Multi-Agent System utilizing large language models, the \\\"Mushroom War Game\\\" focusing on task allocation optimization, and the \\\"Dash \\u0026 Dine Game\\\" which explores agent adaptation to opponent strategies, along with built-in AI opponents featuring rule-based policies and varied strategies.\"}]","TU/e EINDHOVEN UNIVERSITY OF TECHNOLOGY | PDF",1788386817]