Pith. sign in

Paper Citation Record · LEDGER

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue

As of 19 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2505.06997.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.06997 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:34:11.246739Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

  • verified exact2
  • verified fuzzy44
  • unresolved2
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8bdb5e27-83f8-4b5b-a7b3-3b9c47e0fc8b · outbound

This paper cites A comprehensive survey on mobile crowdsensing systems,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue A comprehensive survey on mobile crowdsensing systems,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.970079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.047050Z digest=sha256:2fb1f5c570bee40ecebbd26124a2a057c2e1ca279a9d65d27d45735608babdf5

Observation 97a6eb59-3122-4f22-b9b0-1f4560b6be91 · outbound

This paper cites Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.943353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.051964Z digest=sha256:28ba45fc09df4d609065c89240544895abcc7dadba97732909b302278fe58126

Observation 0d16cf28-c918-4366-a53e-d6c046fcd4df · outbound

This paper cites Drift: A dynamic crowd inflow control system using lstm-based deep reinforcement learn- ing,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Drift: A dynamic crowd inflow control system using lstm-based deep reinforcement learn- ing,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.913253Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.056393Z digest=sha256:ede111999dfb9f851d8f1cee6266915c89e8e475cce38a1fc2bf1a571add8081

Observation 68526008-232a-4722-8931-d4840a48b9e5 · outbound

This paper cites A survey on task assignment in crowdsourcing,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue A survey on task assignment in crowdsourcing,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.899609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.060970Z digest=sha256:bd5c19bd60acef8b3e434a05f497a9d2e0a7a67a274161d5436cd67495497461

Observation d41f8c1d-de4b-4c60-9d1d-596494d0d0df · outbound

This paper cites Towards crowd- sourcing internet of things (crowd-iot): Architectures, security and applications,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Towards crowd- sourcing internet of things (crowd-iot): Architectures, security and applications,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.886510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.065208Z digest=sha256:22768fc0164e5e9814cf7906c9e184df16086b43e0d7880af4d86ac8330ef355

Observation aa782874-42f2-4fdc-8dcf-1614d65a5b86 · outbound

This paper cites Task-oriented wireless communications for collaborative perception in intelligent un- manned systems,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Task-oriented wireless communications for collaborative perception in intelligent un- manned systems,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.872947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.069734Z digest=sha256:631bfbf359c4b88b536116ead6ea76135fac34255d32f6ab3410a2ab6e339173

Observation 0657766f-1b05-483f-9a1e-166e060b58f9 · outbound

This paper cites Unified perception and collaborative mapping for connected and autonomous vehicles,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Unified perception and collaborative mapping for connected and autonomous vehicles,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.860057Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.074610Z digest=sha256:185ce88c8edb006c2e82b12bf660779635686b8cd026f4401d7e7c6d88a0b648

Observation be83b62f-7765-4a4e-aee2-b2824b806fa2 · outbound

This paper cites A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue A survey and framework of cooperative perception: From heterogeneous singleton to hierarchical cooperation,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.847346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.078823Z digest=sha256:27a54053947c0bf0bee59e61977736e18d3d9177c9c2412f5754fb19c8cd8bbd

Observation 6ed7528c-fa91-47c7-ac9a-3c340fc4a1f1 · outbound

This paper cites Air-ground spatial crowdsourcing with uav carriers by geometric graph convolutional multi-agent deep reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Air-ground spatial crowdsourcing with uav carriers by geometric graph convolutional multi-agent deep reinforcement learning,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.833772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.082970Z digest=sha256:c66531c4c23bbd10facdcc0d2659c04df0a348254e3b23c78984e42ceabdca66

Observation 355c9d3f-9ecd-4e94-a8a6-9d8d7b111db9 · outbound

This paper cites Energy-efficient ground-air-space vehicular crowdsensing by hierarchical multi-agent deep reinforcement learning with diffusion models,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Energy-efficient ground-air-space vehicular crowdsensing by hierarchical multi-agent deep reinforcement learning with diffusion models,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.819943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.087403Z digest=sha256:035d25366a2c76fccb233e26fdbcdb3f493e141f5df3345c1b156838356fde64

Observation c4b17752-71a7-445f-aca0-8fe360c7fe17 · outbound

This paper cites The system framework and application research of the parallel emergency management system (pems),.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue The system framework and application research of the parallel emergency management system (pems),

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.806105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.091410Z digest=sha256:6af4416ae642df0192e5d6aa35b69b45198fbaadb8b7e92b1c3773ef2d924787

Observation c9f52503-2851-424e-93dd-2020741bf394 · outbound

This paper cites Parallel emergency management of incidents by integrating ooda and prea loops: The c2 mechanism and modes,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Parallel emergency management of incidents by integrating ooda and prea loops: The c2 mechanism and modes,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.792790Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.095857Z digest=sha256:ac656886da68badcf61dad62814b8b54da183c537b9d8a353bc218a9a2d8c4af

Observation 4c3ad42a-a6d3-494b-92a5-c90e007588d7 · outbound

This paper cites Towards time- constrained task allocation in semi-opportunistic mobile crowdsensing,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Towards time- constrained task allocation in semi-opportunistic mobile crowdsensing,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.779950Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.099900Z digest=sha256:9361df036619b4acea840597639eed892903c268807cb1ffecff3bcefc83c827

Observation 6bc3529b-8e7c-4e5f-8b84-e1f362eee168 · outbound

This paper cites Optimal resource allocation for uav-relay-assisted mobile crowdsensing,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Optimal resource allocation for uav-relay-assisted mobile crowdsensing,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.767126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.104030Z digest=sha256:6553c8ad08b381e6f491a80d37d1a0c9bf9b665597c1362406b2973da06af9e9

Observation 8b145712-b9ab-4b29-8e11-120ef19ea565 · outbound

This paper cites Mappo-based cooperative uav trajectory design with long-range emergency communications in disaster areas,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Mappo-based cooperative uav trajectory design with long-range emergency communications in disaster areas,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.753572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.108379Z digest=sha256:c6bab42fd54dfed56a48b0c839f8d703c03bf85e4e3d2da1d388796ff1fb55db

Observation af6355f3-301b-4223-82e3-3982b4d360e1 · outbound

This paper cites Disaster-resilient emergency communication with intelligent air–ground cooperation,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Disaster-resilient emergency communication with intelligent air–ground cooperation,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.738577Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.112764Z digest=sha256:b67dd85878e07534e7d28315c0152068897dc0060abd23c227211a44ed0fe79b

Observation f80745fc-aa35-44ec-900f-81ac3a9e7b0d · outbound

This paper cites Task search and alloca- tion strategy for heterogeneous multiagent systems under communication constraints,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Task search and alloca- tion strategy for heterogeneous multiagent systems under communication constraints,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.725161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.116877Z digest=sha256:103dc6f3ad8dd341202c3d6b0940b673bc3275517ea0154d291347de62e1412f

Observation 0f61cc80-ab7c-4917-a37a-a33551fb6cc6 · outbound

This paper cites Cooperative multi- aav path planning for discovering and tracking multiple radio-tagged targets,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Cooperative multi- aav path planning for discovering and tracking multiple radio-tagged targets,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.711601Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.121155Z digest=sha256:3aece5e10961fc155c71459550745525b50d7a9a7d065b8753aedbb186d4b7c8

Observation 73ea8c17-6836-407a-a599-6829bd17211c · outbound

This paper cites Robust training in multiagent deep reinforcement learning against optimal adversary,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Robust training in multiagent deep reinforcement learning against optimal adversary,

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.698612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.125123Z digest=sha256:32b3950b7ec990c43b7c35fc98eb7d6c87626c867f0c685f44d66cae91991494

Observation d35a4077-fe0b-4adf-be4c-cad4775a3ee7 · outbound

This paper cites Sensors on the internet of things systems for urban disaster management: a systematic literature review,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Sensors on the internet of things systems for urban disaster management: a systematic literature review,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.684994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.129421Z digest=sha256:2c9a1a906173778e6896ce73a28ce59c86b06cc3de5d3b1ce0e2d0e05da3cf1c

Observation eec004b5-6693-403f-92b4-9c24fff61d1c · outbound

This paper cites Optimization of emergency rescue routes after a violent earthquake,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Optimization of emergency rescue routes after a violent earthquake,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.670980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.133762Z digest=sha256:897c456f6ac93c6f3b8335d2ebe5deb192d51899dc1dfd3e6bb78fc8823390e3

Observation 784d75c3-0f6d-4ef1-bc50-61a8c11a51b6 · outbound

This paper cites Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Autonomous unmanned aerial vehicles in bushfire management: Challenges and opportunities,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.655843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.138017Z digest=sha256:2ef23303c4d2f50748f8a635dea5db9d1bfc2b7a29a8820031271da475432a45

Observation 230b68b7-45ac-49ef-919f-7f507ed45c39 · outbound

This paper cites Gacf: Ground-aerial collaborative framework for large-scale emergency rescue scenarios,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Gacf: Ground-aerial collaborative framework for large-scale emergency rescue scenarios,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.641143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.142195Z digest=sha256:d6e28104f04bbb0084b5162c7af9ff3d30419023266085d9a99189ab6d4fb02e

Observation 185431d8-7017-4a18-8fe3-7d6f88224e8a · outbound

This paper cites Cooperative unmanned surface vehicles and unmanned aerial vehicles platform as a tool for coastal monitoring activities,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Cooperative unmanned surface vehicles and unmanned aerial vehicles platform as a tool for coastal monitoring activities,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.626989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.146370Z digest=sha256:ad94e3a3519b7dd0d44d2f893a8fed0744de82dd3522aea15fa364cfeae06d8c

Observation 102980b2-a80e-49a9-bd49-2c9289046c02 · outbound

This paper cites Human detection and action recognition for search and rescue in disasters using yolov3 algorithm,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Human detection and action recognition for search and rescue in disasters using yolov3 algorithm,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.612515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.150439Z digest=sha256:cc3b5a2c979b4998ea5cd3b7f9767485f9d684c4496dadb48f6fd80d3d8b4133

Observation 5aa24dc5-6dde-44e1-9630-6c2ac0a3a0c9 · outbound

This paper cites A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue A comprehensive survey of unmanned ground vehicle terrain traversability for unstructured environments and sensor technology insights,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.598376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.154591Z digest=sha256:07ca5ad884c1740596c706ddc8db501f343651529eee0624efedfa57fb5a287a

Observation de2c5cbb-9b6d-40ff-b533-8a2664769983 · outbound

This paper cites Unmanned aerial vehicles for search and rescue: A survey,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Unmanned aerial vehicles for search and rescue: A survey,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-15T22:34:11.158608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:34:11.158608Z digest=sha256:7a6aed2dd7246bb0ac3338b8d2ebaf022ea3bfb45f2e8198613dac91b3363b5f

Observation 29d4f407-b407-4858-8d1b-7a4a269d5435 · outbound

This paper cites A crowd- aided vehicular hybrid sensing framework for intelligent transportation systems,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue A crowd- aided vehicular hybrid sensing framework for intelligent transportation systems,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.574783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.162750Z digest=sha256:32dda02b22af5475c0b9336bbc1063b2949493d6c643184d56ed4c76c424e61c

Observation 739228ff-6d2b-441d-80ca-51b294950f55 · outbound

This paper cites Cooperative uav trajectory design for disaster area emergency communications: A multiagent ppo method,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Cooperative uav trajectory design for disaster area emergency communications: A multiagent ppo method,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.561544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.166953Z digest=sha256:741d9ca1060bbfd63a863d50e1093e8cecec75c0d5f45a5f7d916a0bb1a96589

Observation 2e460a60-32ea-47f1-bb63-032329f823c2 · outbound

This paper cites Collab- orative route planning of uavs, workers, and cars for crowdsensing in disaster response,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Collab- orative route planning of uavs, workers, and cars for crowdsensing in disaster response,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.547994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.171025Z digest=sha256:9ff793bde2123cad2b156acd92af0befaf53844d7ce8202a1bd0e88037d52676

Observation 14deaee0-dcdf-4472-87e3-5494b1a4065f · outbound

This paper cites Decentralized task assignment for mobile crowd- sensing with multi-agent deep reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Decentralized task assignment for mobile crowd- sensing with multi-agent deep reinforcement learning,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.534126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.175480Z digest=sha256:8df2ec2f32ed2d89a9cabd9c8fa39d9c110f8da81f18a184905897f67d09a26e

Observation 7db605fd-9f07-4c47-88ec-19f87e00a7d6 · outbound

This paper cites Ensuring threshold aoi for uav-assisted mobile crowdsensing by multi-agent deep reinforcement learning with transformer,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Ensuring threshold aoi for uav-assisted mobile crowdsensing by multi-agent deep reinforcement learning with transformer,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.519593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.179644Z digest=sha256:28a802d00703054954d5c383d217dbf70c521e4e351ea8f8e6b748f3b8066b66

Observation be67d6dd-e7f4-474b-8284-76d98dac80a8 · outbound

This paper cites Exploring both individuality and cooperation for air-ground spatial 16 crowdsourcing by multi-agent deep reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Exploring both individuality and cooperation for air-ground spatial 16 crowdsourcing by multi-agent deep reinforcement learning,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.506242Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.183981Z digest=sha256:09421f33639c49753c868c0e31aa7bc88c1c0076f54c42cc4be12a3668004c40

Observation de0dcba2-31bf-4b6e-9da1-d3330cbfc96b · outbound

This paper cites Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing

Reference 34

Resolution
verified exact
local_arxiv, observed 2026-08-15T22:34:11.323838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.188045Z digest=sha256:296766612ac600dcdbad5b087d692e736313cf14002d82cb4ebe83224300fa53

Observation 7f70d911-2e1c-49c8-947f-4d7bbad160a9 · outbound

This paper cites Energy-efficient 3d vehicular crowdsourcing for disaster response by distributed deep reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Energy-efficient 3d vehicular crowdsourcing for disaster response by distributed deep reinforcement learning,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.492137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.192564Z digest=sha256:a689b08fcde0867891fd8c60f1d7f35ae64af2d25d021074ec9405dc316e78e1

Observation 5f96448a-ec90-4533-85d3-35c06857fba1 · outbound

This paper cites Pomdp inference and robust solution via deep reinforce- ment learning: An application to railway optimal maintenance,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Pomdp inference and robust solution via deep reinforce- ment learning: An application to railway optimal maintenance,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.478925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.197163Z digest=sha256:7b255056288b517ce803736572df9d2302e245a85ec17b219ac9387711bdf608

Observation 8d21c0e2-0956-4bbb-b19a-a6928881da6f · outbound

This paper cites Informed POMDP: Leveraging Additional Information in Model-Based RL.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Informed POMDP: Leveraging Additional Information in Model-Based RL

Reference 37

Resolution
verified exact
local_arxiv, observed 2026-08-15T22:34:11.302761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.201181Z digest=sha256:ea0089f44eb53a0df7dc42c15eef2e280fcf55508a576b2af97ac62e55f09991

Observation 24b9feae-ceb0-4fab-b543-bfdb6865213c · outbound

This paper cites An effective cnn and transformer com- plementary network for medical image segmentation,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue An effective cnn and transformer com- plementary network for medical image segmentation,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.465159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.205609Z digest=sha256:7e87a7fe9837990eb542e070d40c7003e131cef6894f170e0efad2d69f0c4699

Observation 065b02a1-ff00-4a84-ae26-5c9db53939b3 · outbound

This paper cites A comprehensive overview and comparative analysis on deep learning models,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue A comprehensive overview and comparative analysis on deep learning models,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.451269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.209789Z digest=sha256:bd17517fea51d268f1627ba283d752bd9337d24abb803971c3964535d442668d

Observation 14b5a24a-cb95-4c3c-9211-ae08216c2c98 · outbound

This paper cites An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue An Introduction to Centralized Training for Decentralized Execution in Cooperative Multi-Agent Reinforcement Learning

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T22:34:11.213822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T22:34:11.213822Z digest=sha256:b85dfe18f12a136f6c59a0f00bbdd6c059da704293a3499b1da8b4ceb7d3a98a

Observation abf7f1ef-b04c-4c9f-b754-7548a4f24956 · outbound

This paper cites Intelligent routing method based on dueling dqn reinforcement learning and net- work traffic state prediction in sdn,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Intelligent routing method based on dueling dqn reinforcement learning and net- work traffic state prediction in sdn,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.437497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.218242Z digest=sha256:a71221378f98801e9fbf694999cda71f47013df61e2a69836d5ef9690b5d2e9a

Observation 4bf4a882-a407-4422-a2e6-4dcca883e63b · outbound

This paper cites Monotonic value function factorisation for deep multi- agent reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Monotonic value function factorisation for deep multi- agent reinforcement learning,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.423913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.222020Z digest=sha256:24f70f0b532f09a0dd5ec59b4b3c70a69d554b743d8279ed240bcc600888db0a

Observation 72ea18b5-b7fb-4cde-95e7-3a6569d88936 · outbound

This paper cites Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.410397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.226130Z digest=sha256:cb1b38eb3cc5285584cce11e89e74c5f813fc867a4d95d794d54865af2b8508a

Observation cd907e37-d861-42c5-8a28-7e993a6df660 · outbound

This paper cites The surprising effectiveness of ppo in cooperative multi-agent games,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue The surprising effectiveness of ppo in cooperative multi-agent games,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.395569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.230331Z digest=sha256:bb24893ac07f5717202505019ecc74dc4e3c56b5d30ba5ee4125e389200d0313

Observation 0bf045bb-f017-4f96-9e06-776f0d749e02 · outbound

This paper cites Online organizing large-scale heterogeneous tasks and multi-skilled participants in mobile crowdsensing,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Online organizing large-scale heterogeneous tasks and multi-skilled participants in mobile crowdsensing,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.381468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.234264Z digest=sha256:693512d34fc99f3a4f718a409f2ee2bb2c6165d01ad78ff915768144aaaee239

Observation d61abab3-4df8-4390-99ff-9d332291c285 · outbound

This paper cites Human-drone collaborative spatial crowdsourcing by memory- augmented and distributed multi-agent deep reinforcement learning,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Human-drone collaborative spatial crowdsourcing by memory- augmented and distributed multi-agent deep reinforcement learning,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.367457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.238076Z digest=sha256:608391399822af5c8e3d5f639a00e133bbc5d645c6c4cba8c1626e862b8b88ec

Observation 841ef362-09a8-4935-bfe3-4fa4ffc7d826 · outbound

This paper cites Modeling human steering behavior in teleoperation of unmanned ground vehicles with varying speed,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue Modeling human steering behavior in teleoperation of unmanned ground vehicles with varying speed,

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.353077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.242804Z digest=sha256:573d2c58f7bce776131022deac146bcefe5cbfc6639cde16c7280d2fcfde332a

Observation 6dfa1901-079b-4c0a-aa62-1a07a21fc967 · outbound

This paper cites 3d building model generation from mls point cloud and 3d mesh using multi-source data fusion,.

A Multi-Agent Reinforcement Learning Approach for Cooperative Air-Ground-Human Crowdsensing in Emergency Rescue 3d building model generation from mls point cloud and 3d mesh using multi-source data fusion,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T22:34:11.338596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-15T22:34:11.246739Z digest=sha256:1dad0f9b058d9de238dec2084a9455edaeebd83d757de7b14b9bab6f2013c82e

Pith citing papers

No inbound Pith citation observations are available.