Towards Large-Scale Collaborative Planning: Answering High-Level Search Queries Using Human Computation

By Edith Law, Haoqi Zhang. 2011

42 citations recorded by the source corpus.

Behind every search query is a high-level mission that the user wants to accomplish. While current search engines can often provide relevant information in response to well-specified queries, they place the heavy burden of making a plan for achieving a mission on the user. We take the alternative approach of tackling users' high-level missions directly by introducing a human computation system that generates simple plans, by decomposing a mission into goals and retrieving search results tailored to each goal. Results show that our system is able to provide users with diverse, actionable search results and useful roadmaps for accomplishing their missions.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Natural Language Processing

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Citations in the corpus

AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Prompts (2021, background): “In crowdsourcing, requesters break down complex tasks into pieces that can be performed independently, then combined [22, 34, 38, 54].”
Online Task Assignment in Crowdsourcing Markets (2012, background): “…skills, but can result in severely suboptimal performance for human computation tasks that require specialized training or knowledge, such as analyzing the nutritional value of food, as proposed by Noronha et al. (2011), or helping people plan their travel, as proposed by Law and Zhang (2011).”
Towards Decision Support and Goal Achievement: Identifying Action-Outcome Relationships From Social Media (2015, background): “Law and Zhang use crowdsourced workers to generate simple plans related to the “high-level missions” driving search queries, and evaluate the eﬀect of replacing search engine results for the original query with web resources related to the various steps required by a plan [28].”
Distilling the Outcomes of Personal Experiences: A Propensity-scored Analysis of Social Media (2017, background): “To aid goal achievement, prior research has leveraged crowdsourcing and friend-sourcing to create action plans, showing that it can help improve behavior [2, 60].”
No Workflow Can Ever Be Enough (2017, methodology): “Crowdsourcing workflows are also referred to as human computation algorithms or crowd algorithms [10, 16, 40, 43].”