ENHANCED HUMAN/MACHINE WORKFORCE MANAGEMENT USING REINFORCEMENT LEARNING

1 . A system for enhanced human/machine workforce management using reinforcement learning comprising:

a reinforcement learning server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device and configured to:

receive a plurality of historical data from a contact center;

form a partially-observable Markov chain model based at least in part on at least a portion of the historical data;

provide the partially-observable Markov chain model to an optimization server;

an optimization server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device and configured to:

receive a partially-observable Markov chain model from a reinforcement learning server;

select a plurality of work tasks based at least in part on the partially-observable Markov chain model;

select a plurality of contact center resources;

assign each of the selected work tasks to at least one of the plurality of contact center resources;

record and analyze a plurality of observations based on each selected resource's performance of each work task assigned to it;

provide the observations to the reinforcement learning server;

a retrain and design server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device and configured to:

observe and analyze a plurality of historical data from a contact center;

provide at least a portion of the historical data to a reinforcement learning server;

define a plurality of reward values to direct the operation of the reinforcement learning server; and

design and train a Markov decision process model based at least in part on the partially- observable Markov chain model, using at least a portion of the defined reward values.

2 . The system of claim 1 , wherein the plurality of reward values further comprises a plurality of negative rewards, wherein a negative reward is defined as a negative value and the retrain and design server trains away from the reward using negative-reinforcement learning.

3 . The system of claim 1 , wherein the plurality of contact center resources comprises at least a workforce management system.

4 . The system of claim 1 , wherein the plurality of contact center resources comprises a plurality of virtual bot workers.

5 . A method for enhanced human/machine workforce management using reinforcement learning, comprising the steps of:

receiving, at a retrain and design server comprising at least a plurality of programming instructions stored in a memory and operating on a processor of a computing device, a plurality of historical data from a contact center;

defining a plurality of reward values to direct the operation of a reinforcement learning server;

providing at least a portion of the historical data to a reinforcement learning server for use in a partially-observable Markov chain model;

forming, using a reinforcement learning server, a partially-observable Markov chain model based at least in part on the historical data;

selecting, using an optimization server, a plurality of work tasks based at least in part on the partially-observable Markov chain model;

selecting a plurality of contact center resources;

assigning each of the selected work tasks to at least one of the plurality of contact center resources;

training a Markov decision process model based at least in part on the partially-observable Markov chain model, using at least a portion of the defined reward values.

6 . The method of claim 5 , wherein the plurality of reward values further comprises a plurality of negative rewards, wherein a negative reward is defined as a negative value and the retrain and design server trains away from the reward using negative-reinforcement learning.

7 . The method of claim 5 , wherein the plurality of contact center resources comprises at least a workforce management system.

8 . The method of claim 5 , wherein the plurality of contact center resources comprises a plurality of virtual bot workers.