Dynamic Belief State Planning Framework for Ambush Avoidance in Contested Environments: A Game-Theoretic Approach

Dynamic Belief State Planning Framework for Ambush Avoidance in Contested Environments: A Game-Theoretic Approach | 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 Dynamic Belief State Planning Framework for Ambush Avoidance in Contested Environments: A Game-Theoretic Approach Ivan Enzo Gargano, Karl Dietrich von Ellenrieder, Marianna Vivolo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8916440/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Existing approaches to adversarial path planning for ambush avoidance assume perfect, static information about opponent strategies and environmental conditions—an assumption that fails in dynamic settings where information degrades and adaptive agents learn from observed behavioral patterns. We present a dynamic belief state planning framework that extends game-theoretic path planning through four multiplicative adjustment factors modeling temporal information decay, opponent adaptation through strategy learning, environmental uncertainty propagation, and partial observability constraints. The framework operates in belief space rather than state space while maintaining polynomial-time computational complexity through linear programming formulations with temporally-adjusted utility values. Validation across 180 simulated scenarios demonstrates that our dynamic belief planning approach maintains 95% of optimal performance under severe information degradation conditions where static game-theoretic approaches degrade to 6% effectiveness—representing an 89 percentage point improvement in decision quality. The framework establishes critical operational thresholds for autonomous agents: active information gathering becomes beneficial above environmental uncertainty U=0.36, behavioral strategies with entropy below 0.3 enable opponent exploitation through learning, and extended planning horizons exceeding 20 decision epochs require 15-20% performance degradation margins. The framework provides computationally tractable solutions for autonomous agent deployment while bridging theoretical optimality and practical realizability for agents operating under degraded observability and adaptive opponents. adversarial path planning dynamic belief state planning game-theoretic planning under uncertainty temporal belief dynamics pursuit-evasion games information decay modeling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 19 Feb, 2026 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8916440","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596215608,"identity":"5ef1e8c6-b641-4f76-af02-71a338ca048c","order_by":0,"name":"Ivan Enzo Gargano","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACNhiDH4gPAzEP8VokG6BaCOuBAYMDDAzMDMRYw8e/+NnHLzV35Ixv5B48XPCHQcaeoMMknhnPljn2zNjsRl7C4Rk8RDiMTeKAMbME2+HEbTdyDA7zSBCl5fhnZol/hxM3zwBpMSBGC3+PMePHtsOJGyRAWhKIsoWnmJmx77CxxJk3BodnHJDg4TlAQIt8//HNjD++HZbjb88x/lzwx8aevYGQNRIJDMxIbpEgpB4I+A8wMP4gQt0oGAWjYBSMYAAAmH46HHrEO8oAAAAASUVORK5CYII=","orcid":"","institution":"Free University of Bozen-Bolzano","correspondingAuthor":true,"prefix":"","firstName":"Ivan","middleName":"Enzo","lastName":"Gargano","suffix":""},{"id":596215609,"identity":"1202d372-a25f-433a-b14a-272130c20d66","order_by":1,"name":"Karl Dietrich von Ellenrieder","email":"","orcid":"","institution":"Free University of Bozen-Bolzano","correspondingAuthor":false,"prefix":"","firstName":"Karl","middleName":"Dietrich","lastName":"von Ellenrieder","suffix":""},{"id":596215616,"identity":"10108ddb-2837-40ec-91a0-236acc2b7f95","order_by":2,"name":"Marianna Vivolo","email":"","orcid":"","institution":"Iveco Defence Vehicles","correspondingAuthor":false,"prefix":"","firstName":"Marianna","middleName":"","lastName":"Vivolo","suffix":""}],"badges":[],"createdAt":"2026-02-19 10:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8916440/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8916440/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103508291,"identity":"3d0b8ff7-3e2f-45cb-9a58-14233a7a56e3","added_by":"auto","created_at":"2026-02-26 13:48:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5141079,"visible":true,"origin":"","legend":"","description":"","filename":"AAMASDynamicBeliefStatePlanningFrameworkforAmbushAvoidanceinContestedEnvironmentsAGameTheoreticApproach1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8916440/v1_covered_12dd37bd-d7e2-4bab-b29d-422a68a6f20b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamic Belief State Planning Framework for Ambush Avoidance in Contested Environments: A Game-Theoretic Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"autonomous-agents-and-multi-agent-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agnt","sideBox":"Learn more about [Autonomous Agents and Multi-Agent Systems](http://link.springer.com/journal/10458)","snPcode":"10458","submissionUrl":"https://submission.nature.com/new-submission/10458/3","title":"Autonomous Agents and Multi-Agent Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"adversarial path planning, dynamic belief state planning, game-theoretic planning under uncertainty, temporal belief dynamics, pursuit-evasion games, information decay modeling","lastPublishedDoi":"10.21203/rs.3.rs-8916440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8916440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Existing approaches to adversarial path planning for ambush avoidance assume perfect, static information about opponent strategies and environmental conditions—an assumption that fails in dynamic settings where information degrades and adaptive agents learn from observed behavioral patterns. 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