GOAM: Game-Oriented Agentic Modeling for Turn-Based Game AI

GOAM: Game-Oriented Agentic Modeling for Turn-Based Game AI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article GOAM: Game-Oriented Agentic Modeling for Turn-Based Game AI Datorien L. Anderson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9044026/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We present GOAM (Game-Oriented Agentic Modeling), an architecture for turn-based game AI that separates strategic orchestration from tactical execution under an explicit certainty-based routing scheme. A macro process maintains longer-horizon objectives and game context; a micro process handles immediate action execution, cached patterns, and reactive play. Routing between these layers is governed by a certainty score that limits expensive language-model deliberation to genuinely ambiguous positions, reducing cost while preserving strategic coherence across a full game trajectory. We formalize the GOAM architecture; its macro/micro decomposition, certainty model, decision routing, and feedback structure, and examine a derivative implementation deployed as an LLM bridge for turn-based strategy. Supporting supplementary materials report isolated micro-agent results via NOESIS-based experiments in chess and tic-tac-toe. Together, these contributions position GOAM as a principled agentic architecture for turn-based game AI and motivate a broader empirical validation program. Artificial Intelligence and Machine Learning turn-based game AI agentic architecture dual-process reasoning certainty-based routing large language models case-based reasoning tactical pattern caching chess agentic ux methdology Full Text Additional Declarations The authors declare no competing interests. Supplementary Files GoamArch.png Layers of GOAM noesischess.pdf Noesis Chess, supplementary study noesistictactoe.pdf Noesis TicTacToe, a supplementary study llmbridgeagainststockfish.zip llm bridge (goam-deriv) against stockfish modelgauntletnote.md Model Gauntlet Note (Transient based on September 2025 models) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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