Agent aptitude prediction

A method, comprising:

• by one or more processors, receiving data associated with effective completion of a plurality of tasks by a plurality of agents;
• by the one or more processors, determining a positive correlation within the data between first particular feature values of a plurality of feature vectors associated with the plurality of tasks and second particular feature values of a plurality of feature vectors associated with the plurality of agents, the first particular feature values characterizing the plurality of tasks and the second particular feature values characterizing the plurality of agents;
• by the one or more processors, determining an aptitude of each agent of the plurality of agents towards one or more tasks of the plurality of tasks that each agent has completed in a manner specified by a work distributor based on the positive correlation between the first particular feature values of a plurality of feature vectors associated with the one or more tasks and the second particular feature values of the feature vector associated with each agent;
• by the one or more processors, determining, using a machine-learning model, a probability that a designated agent will complete a designated task in the manner specified by the work distributor, the probability being determined based on the determined aptitude of the designated agent and a feature vector associated with the designated task matching the first particular feature values associated with the tasks that the designated agent has completed in the manner specified by the work distributor and a feature vector associated with the designated agent matching the second particular feature values;
• and
• by the one or more processors, providing identification information of the designated agent for display in association with an identification for the designated task and the determined probability that the designated agent will complete the designated task in the manner specified by the work distributor.

One or more computer-readable non-transitory storage media comprising instructions executable by one or more processors of a system, wherein as the one or more processors execute the instructions, the system:

• receives data associated with effective completion of a plurality of tasks by a plurality of agents;
• determines a positive correlation within the data between first particular feature values of a plurality of feature vectors associated with the plurality of tasks and second particular feature values of a plurality of feature vectors associated with the plurality of agents, the first particular feature values characterizing the plurality of tasks and the second particular feature values characterizing the plurality of agents;
• determines an aptitude of each agent of the plurality of agents towards one or more tasks of the plurality of tasks that each agent has completed in a manner specified by a work distributor based on the positive correlation between the first particular feature values of a plurality of feature vectors associated with the one or more tasks and the second particular feature values of the feature vector associated with each agent;
• determines, using a machine-learning model, a probability that a designated agent will complete a designated task in the manner specified by the work distributor, the probability being determined based on the determined aptitude of the designated agent and a feature vector associated with the designated task matching the first particular feature values associated with the tasks that the designated agent has completed in the manner specified by the work distributor and a feature vector associated with the designated agent matching the second particular feature values;
• and
• provides identification information of the designated agent for display in association with an identification for the designated task and the determined probability that the designated agent will complete the designated task in the manner specified by the work distributor.