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Answering Planning Queries with the Crowd

Summary: Crowdsourcing subjective planning queries whose objectives resist formalization, constructing action/object sequences incrementally. An optimal-within-class question-selection algorithm minimizes crowd effort by adaptively eliciting the best next plan element. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
10920
Venue
VLDB
Year
2013
Pagerank
5.7251004e-05
Overall Rank
7,018 | 51.86%
DOI
10.14778/2536360.2536369

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{kaplan_vldb13,
        title = {{Answering Planning Queries with the Crowd}},
        author = {Kaplan, Haim and Lotosh, Ilia and Milo, Tova and Novgorodov, Slava},
        journal = {PVLDB},
        series = {{VLDB} '13},
        volume = {6},
        number = {9},
        pages = {697--708},
        doi = {10.14778/2536360.2536369},
        url = {https://doi.org/10.14778/2536360.2536369},
        year = {2013}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,813 Efficient Algorithms for Crowd-Aided Categorization 2020 VLDB 6.0780519e-05
7,081 OASSIS: Query Driven Crowd Mining 2014 SIGMOD 5.7086247e-05
7,222 Hear the Whole Story: Towards the Diversity of Opinion in Crowdsourcing Markets 2015 VLDB 5.6679185e-05
7,989 Where To: Crowd-Aided Path Selection 2014 VLDB 5.5111036e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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