Combining human and machine intelligence to solve tasks with crowd sourcing

摘要

Methods are described for ideally joining human and machine computing resources to solve tasks, based on the construction of predictive models from case libraries of data about the abilities of people and machines and their collaboration. Predictive models include methods for folding together human contributions, such as voting, with machine computation, such as automated visual analyses, as well as the routing of tasks to people based on prior performance and interests. An optimal distribution of tasks to selected participants of the plurality of participants is determined according to a model that considers the demonstrated competencies of people based on a value of information analysis that considers the value of human computation and the ideal people for providing a contribution.

主权项

1. A computer-implemented method, comprising:
receiving computer-based guidance for solving a task from a computer-based resource; receiving historical human resource experience information associated with a plurality of participants, the historical human resource experience information indicating an experience level for individuals of the plurality of participants with respect to solving previous tasks; receiving historical human resource activity information associated with the plurality of participants, the historical human resource activity information indicating an availablility and an activity level for the individuals of the plurality of participants with respect to solving the previous tasks or other previous tasks; generating a distribution of the task to selected participants in the plurality of participants based on the computer-based guidance, the historical human resource experience information, and the historical human resource activity information; and generating a global solution to the task based on the computer-based guidance and human-based contributions from the selected participants.