CrowdPlanr: Planning made easy with crowd

Lotosh, Ilia; Milo, Tova; Novgorodov, Slava

doi:10.1109/icde.2013.6544940

Cited by 20 publications

(11 citation statements)

References 9 publications

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“…The question designing strategy of [20] is to generate the optimal set of questions. [14] builds the desired travelling plans incrementally, optimally choosing, at each step, the best questions, so that the overall number of questions to minimize the number of the asked questions. Selecting workers with high individual qualities for tasks always does beneficial to the final quality of answers.…”

Section: Related Workmentioning

confidence: 99%

Landmark-Based Route Recommendation with Crowd Intelligence

Zheng

Zheng³

et al. 2016

Data Sci. Eng.

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Route recommendation is one of the most widely used location-based services nowadays, as it is vital for nicedriving experience and smooth public traffic. Given a pair of user-specified origin and destination, a route recommendation service aims to provide users with the routes of the best travelling experience according to given criteria. However, even the routes recommended by the big-thumb service providers can deviate significantly from the ones travelled by experienced drivers, which motivates the previous research that leverages crowds' knowledge to improve the recommendation quality. Since route recommendation is normally an online task, low-latency response to drivers' queries is required in this kind of systems. Unfortunately, latency of crowdsourced systems is usually high, because they need to generate tasks and wait for workers' feedbacks before answering queries. To address this issue, we extend our previous system-CrowdPlanner-by proposing some strategies to reuse existing answers (truths) to deal with newly coming queries more efficiently. A prototype system has been deployed to many voluntary mobile clients and extensive tests on real-scenario queries have shown the superiority of

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Section: Related Workmentioning

confidence: 99%

Landmark-Based Route Recommendation with Crowd Intelligence

Zheng

Zheng³

et al. 2016

Data Sci. Eng.

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“…This field is attracting more attention from different research communities as interesting connections between them and human computation are being revealed (Dai et al, 2010Kamar et al, 2012;Lotosh et al, 2013). In solving computationally hard problems from different fields -especially those that require input from humans, or for which the complete model is not known -human computation has emerged as a powerful and inexpensive approach.…”

Section: Introductionmentioning

confidence: 99%

Herding the Crowd: Using Automated Planning for Better Crowdsourced Planning

Manikonda

Chakraborti²,

Talamadupula³

et al. 2017

HCj

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One subclass of human computation applications are those directed at tasks that involve planning (e.g. tour planning) and scheduling (e.g. conference scheduling). Interestingly, work on these systems shows that even primitive forms of automated oversight on the human contributors helps in significantly improving the effectiveness of the humans/crowd. In this paper, we argue that the automated oversight used in these systems can be viewed as a primitive automated planner, and that there are several opportunities for more sophisticated automated planning in effectively steering crowdsourced planning. Straightforward adaptation of current planning technology is however hampered by the mismatch between the capabilities of human workers and automated planners. We identify and address two important challenges that need to be overcome before such adaptation of planning technology can occur: (i) interpreting inputs of the human workers (and the requester) and (ii) steering or critiquing plans produced by the human workers, armed only with incomplete domain and preference models. To these ends, we describe the implementation of AI-MIX, a plan generation system that uses automated checks and alerts to improve the quality of plans created by human workers.

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“…A first prototype of CrowdPlanr was demonstrated in [17]. The demonstration gives only a high level overview of the the system capabilities and user interface.…”

Section: Introductionmentioning

confidence: 99%

Answering planning queries with the crowd

et al. 2013

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Recent research has shown that crowd sourcing can be used effectively to solve problems that are difficult for computers, e.g., optical character recognition and identification of the structural configuration of natural proteins. In this paper we propose to use the power of the crowd to address yet another difficult problem that frequently occurs in a daily life -answering planning queries whose output is a sequence of objects/actions, when the goal, i.e, the notion of "best output", is hard to formalize. For example, planning the sequence of places/attractions to visit in the course of a vacation, where the goal is to enjoy the resulting vacation the most, or planning the sequence of courses to take in an academic schedule planning, where the goal is to obtain solid knowledge of a given subject domain. Such goals may be easily understandable by humans, but hard or even impossible to formalize for a computer.We present a novel algorithm for efficiently harnessing the crowd to assist in answering such planning queries. The algorithm builds the desired plans incrementally, choosing at each step the 'best' questions so that the overall number of questions that need to be asked is minimized. We prove the algorithm to be optimal within its class and demonstrate experimentally its effectiveness and efficiency.

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CrowdPlanr: Planning made easy with crowd

Cited by 20 publications

References 9 publications

Landmark-Based Route Recommendation with Crowd Intelligence

Landmark-Based Route Recommendation with Crowd Intelligence

Herding the Crowd: Using Automated Planning for Better Crowdsourced Planning

Answering planning queries with the crowd

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