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pith:FRJ5SPOM

pith:2026:FRJ5SPOMXFJXWMVYPOL3NEJBWM
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Receding Horizon Multi-Agent Deceptive Path Planner

Brian M. Sadler, Rick S. Blum, Xubin Fang

Receding-horizon optimization with Boltzmann policies generates tunable stochastic deceptive paths for single and multiple agents.

arxiv:2605.14085 v1 · 2026-05-13 · eess.SY · cs.SY

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3 Author claim open · sign in to claim
4 Citations open
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Claims

C1strongest 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.

C2weakest 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.

C3one line summary

A receding-horizon planner uses Boltzmann distributions over short trajectories to generate tunable deceptive paths for multiple agents.

References

28 extracted · 28 resolved · 0 Pith anchors

[1] Toward a systems- and control-oriented agent framework, 2005
[2] Mission-Driven Trajectory Homotopy to Explore Dynamic Coverage of USV–UA V Sys- tems, 2025
[3] A Universal Reactive Approach for Graph-Based Persistent Path Planning Problems With Temporal Logic Constraints, 2025
[4] Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents, 2023
[5] Decentralized Motion Planning for Multiagent Collaboration Under Coupled LTL Task Specifications, 2022
Receipt and verification
First computed 2026-05-17T23:39:12.270649Z
Builder pith-number-builder-2026-05-17-v1
Signature Pith Ed25519 (pith-v1-2026-05) · public key
Schema pith-number/v1.0

Canonical hash

2c53d93dccb9537b32b87b97b69121b30cbc298f4c813a431e040b3a0066662b

Aliases

arxiv: 2605.14085 · arxiv_version: 2605.14085v1 · doi: 10.48550/arxiv.2605.14085 · pith_short_12: FRJ5SPOMXFJX · pith_short_16: FRJ5SPOMXFJXWMVY · pith_short_8: FRJ5SPOM
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/FRJ5SPOMXFJXWMVYPOL3NEJBWM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 2c53d93dccb9537b32b87b97b69121b30cbc298f4c813a431e040b3a0066662b
Canonical record JSON
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