Our paper provides and evaluates a new paradigm for collaborative human-robot operation in search and rescue-like settings with information asymmetry. In particular, we focus on settings where the human, a crowdworker in our case, is used as a sensor, providing the route-planning module with essential environmental information. In such settings, the ability to predict the expected performance of the collaborating crowdworker in real-time is instrumental for maintaining a continuously high level of performance. Through an extensive set of experiments with crowdworkers recruited and interacted through Amazon Mechanical Turk, we show that effective online prediction is indeed possible, however only if distinguishing between two subpopulations of crowdworkers, termed "operators" and "sensors", applying a different prediction model to each. Furthermore, we show that even the classification of crowdworkers to the two types can be carried out successfully in real-time, based merely on the first two minutes of collaboration. Finally, we demonstrate how the above abilities can be used for a more effective workers' recruiting process, resulting in a substantially improved overall performance.

Predicting Crowdworkers' Performance as Human-Sensors for Robot Navigation

Paper Session 3: Human Computation Applications

technical paper

The 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP 2020) will be held October 26–28th virtually at The Netherlands Institute for Sound and Vision in Hilversum, The Netherlands.

While artificial intelligence (AI) and human-computer interaction (HCI) represent traditional mainstays of the conference, HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. We promote the exchange of advances in human computation and crowdsourcing not only among researchers, but also engineers and practitioners, to encourage dialogue across disciplines and communities of practice.

This year, we especially invite works that will generate new insights into the quality (or qualities) of human-annotated datasets, including elements such as reliability and replicability of human computation and crowdsourcing experiments, novel metrics for aggregation of results, and holistic approaches to deal with bias, fairness and interpretability.

HCOMP 2020

HCOMP believes strongly in fostering and promoting broad, interdisciplinary research. Our field is particularly unique in the diversity of disciplines it draws upon and contributes to, including human-centered qualitative studies and HCI design, social computing, artificial intelligence, economics, computational social science, digital humanities, policy, and ethics. 


Nir Machlev

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Predicting Crowdworkers' Performance as Human-Sensors for Robot Navigation

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