{"paper":{"title":"Receding Horizon Multi-Agent Deceptive Path Planner","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Brian M. Sadler, Rick S. Blum, Xubin Fang","submitted_at":"2026-05-13T20:10:35Z","abstract_excerpt":"Deceptive path planning enables autonomous agents to obscure their true goals from observers by deviating from an expected optimal path. Prior work largely solves full-horizon, end-to-end optimization for single agents, which is expensive to recompute online and difficult to scale or adapt en route. We propose a unified framework for deceptive path planning using a Boltzmann distribution, computing over short-horizon candidate trajectories within a receding-horizon loop. By param- By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupl"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That short-horizon optimizations within a receding loop can maintain effective deception without the global view of full-horizon methods, particularly when environmental changes occur.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A receding-horizon planner uses Boltzmann distributions over short trajectories to generate tunable deceptive paths for multiple agents.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4033b98b682b9860f089a4835d306fefbbb3154318e80b2d9bc383958b5f57b9"},"source":{"id":"2605.14085","kind":"arxiv","version":1},"verdict":{"id":"0646a103-b9e6-4fca-accd-ad5c54256cc5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T05:20:56.105703Z","strongest_claim":"By iterating a user-defined cost that captures deception, resources, and smoothness, and optionally includes coupling terms between agents, the framework yields stochastic policies that balance the tradeoff between optimal paths and deceptive deviation.","one_line_summary":"A receding-horizon planner uses Boltzmann distributions over short trajectories to generate tunable deceptive paths for multiple agents.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That short-horizon optimizations within a receding loop can maintain effective deception without the global view of full-horizon methods, particularly when environmental changes occur.","pith_extraction_headline":"Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents."},"references":{"count":28,"sample":[{"doi":"","year":2005,"title":"Toward a systems- and control-oriented agent framework,","work_id":"34bd9c35-219a-4e52-906c-25b987f13493","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Mission-Driven Trajectory Homotopy to Explore Dynamic Coverage of USV–UA V Sys- tems,","work_id":"accd8404-d131-43b6-a2b3-f9aca524e90e","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"A Universal Reactive Approach for Graph-Based Persistent Path Planning Problems With Temporal Logic Constraints,","work_id":"b18aab38-b54e-4e43-81be-47cb026bce9e","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents,","work_id":"b870c928-8748-4ff9-aab4-3d9615b6f4d6","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Decentralized Motion Planning for Multiagent Collaboration Under Coupled LTL Task Specifications,","work_id":"5c4125b8-3fd2-4f45-8f2f-3586b438e148","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":28,"snapshot_sha256":"2746765469a05e1ab15784ede6bed2be9d1f0b6e9d9d474f6608ddad28fe8202","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}