Hybrid Multi-Agent AI/MCTS Systems for Complex Information-Imperfect Games

Hybrid Multi-Agent AI/MCTS Systems for Complex Information-Imperfect Games | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 25 November 2025 V1 Latest version Share on Hybrid Multi-Agent AI/MCTS Systems for Complex Information-Imperfect Games Author : Lakshya Jain 0009-0003-5220-5622 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176405957.77536600/v1 274 views 109 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This paper presents a comprehensive artificial intelligence system for imperfect information games, demonstrated through the complex card game “28”. 28 is a team-based trick-taking card game, featuring bidding, trump selection and dynamic gameplay. Our system introduces several contributions: (1) a hybrid decision-making framework that dynamically combines belief networks, Monte Carlo Tree Search (MCTS), and reinforcement learning; (2) an innovative point prediction approach leading to accurate bids; (3) an advanced belief network architecture for opponent modeling, predicting the trump that an opponent might set; and (4) an Information Set MCTS (ISMCTS) implementation that handles imperfect information scenarios. The system achieves significant performance improvements through multi-modal learning from 3873 MCTS-generated games, demonstrating the effectiveness of combining multiple AI paradigms for complex game environments. Our experimental results show that the hybrid approach outperforms individual methods by 15-25% in win rates, while the belief network achieves 70-80% accuracy in opponent hand prediction. The system’s modular architecture enables real-time decision-making while maintaining strategic depth, making it suitable for adaptation to other imperfect information games. Supplementary Material File (revised_28bot_v3 (1).pdf) Download 2.10 MB Information & Authors Information Version history V1 Version 1 25 November 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords card games monte carlo tree search multi-agent systems reinforcement learning Authors Affiliations Lakshya Jain 0009-0003-5220-5622 [email protected] University of Toronto View all articles by this author Metrics & Citations Metrics Article Usage 274 views 109 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Lakshya Jain. Hybrid Multi-Agent AI/MCTS Systems for Complex Information-Imperfect Games. Authorea . 25 November 2025. DOI: https://doi.org/10.22541/au.176405957.77536600/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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